interview-cheatsheet

# /interview-cheatsheet — long-form Chinese ML/LLM interview prep Generate one comprehensive Chinese cheat sheet per invocation: formulas + derivations + from-scratch code + 25 高频题. Output passes cross-model math/code review before rendering. **Detect-only by default: never auto-commits.** ## Inputs - **`<topic>`** (required) — narrow enough for one 600-1000 line tutorial. Good: "RLHF / DPO / PPO", "MoE", "KV Cache + Speculative Decoding". Bad (too broad): "all of LLM training", "diffusion" (split into Forward Process / Sampling / CFG separately). - **`--effort`** (default `balanced`) — `balanced` ≈ 600 lines, `max` ≈ 1000 lines with deeper proofs and more L3 questions. - **`--byline`** (default `"Ruofeng Yang (杨若峰), Shanghai Jiao Tong University"`) — passed to `/render-html --author`. - **`--commit`** (default `false`) — if `false` (default), stop after rendering; user reviews and commits. Never push without explicit user approval. ## Style guide — STRICT (read `docs/tutorials/attention_tutorial.md` as canonical reference) ### Section skeleton (12-14 sections) ``` ## §0 TL;DR — callout intro line + numbered list of 5-7 takeaways ## §1 直觉 — why this matters; analogy; one-paragraph mental model ## §2 核心公��� — main formula + derivation (variance / scaling / boundary) ## §3 实现细节 — 50-80 line from-scratch PyTorch ## §4-7 变体 / 工程实践 / 常见 bug — variants, comparison tables, footguns ## §8 复杂度 / 资源 — time + memory complexity ## §9 与相关方法对比 — placement in the ecosystem ## §10 25 高...