---
title: "assignment1-basics/CLAUDE.md at main"
tags:
  - wiki
  - sources
sources:
  - "[[assignment1-basics/CLAUDE.md at main]]"
domains: [agentic, skills]
date: "2026-07-17T06:23:36.502Z"
created: "2026-07-17T06:23:36.502Z"
includeInRss: false
---

# assignment1-basics/CLAUDE.md at main

## Takeaways
- Stanford **CS336** (Language Modeling From Scratch) **CLAUDE.md**: AI agents are TAs, not solution generators for implementation-heavy assignments.
- **Strict prohibitions**: no Python/pseudocode, no solutions, no TODO completion, no repo edits, no bash commands, no third-party implementation pointers.
- **Allowed**: concept explanation, lecture/doc pointers, general code review feedback, guiding debug questions, high-level algorithm nudges, sanity checks/toy examples/profiler hints via dialog.
- Teaching loop emphasizes clarifying questions, lecture references, next-step suggestions, and **tests/invariants over fixes** (masks, shapes, toy tensors).
- Covers core assignment surface: tokenizers, transformers, optimizers, training loops, Triton, distributed training, scaling laws, alignment/RL—agents must not implement these for students.

## Notes
Stricter than 1cg's sample AGENTS.md gist (which allows 2–5 line examples). Reference for **maximum** academic integrity guardrails vs. vault productivity agents that actively implement.

## Open questions
- Useful negative template when defining what vault agents *should* do that course agents must not?