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This GitHub repo by Andrej Karpathy outlines four simple rules in 65 lines that boost AI coding accuracy from 65% to 94%. It covers thinking before coding, keeping implementations minimal, making surgical changes, and defining clear success criteria.
- A 65-line CLAUDE.md file allegedly boosted AI coding accuracy from 65% to 94%
- The guide is attributed to Andrej Karpathy and reportedly hit 220,000+ stars on GitHub
- It boils down to four rules: think before coding, keep implementations minimal, make surgical/targeted changes, and define clear testable success criteria upfront
This project packages four principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—into a Claude Code plugin or CLAUDE.md file to curb LLM code pitfalls like overengineering and hidden assumptions. It enforces explicit reasoning, minimal edits, and test-driven success criteria to produce cleaner, more accurate AI-generated code.
- A single CLAUDE.md file encodes four Karpathy-derived rules—think before coding, simplicity first, surgical changes, goal-driven execution—to fix common LLM coding failures.
- It explicitly bans unrequested abstractions, error handling, or flexibility, and restricts edits to only what was asked, leaving style and unrelated code untouched.
- It pushes test-first, criteria-based execution so the model loops until defined success conditions pass rather than coding aimlessly.
- Installable as a Claude Code plugin/marketplace package or via curl into CLAUDE.md, with a Cursor rule file and support for project-specific rule additions.