Build software with AI by describing what you want. Covers prompting techniques, workflow patterns, rules files, and when to intervene.
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Programming where you describe what you want and let AI generate code. You evaluate by results, not by reading every line. Coined by Andrej Karpathy (Feb 2025).
Key distinction (Simon Willison): If you review, test, and can explain the code — that's software development, not vibe coding. Vibe coding means accepting AI output without fully understanding every function.
| Topic | File |
|---|---|
| Prompting techniques | |
| Research-Plan-Implement workflow | |
| Rules files (.cursorrules, CLAUDE.md) | |
| Common pitfalls and fixes | |
| Tool selection by use case | |
Vague prompts → vague results. Before touching your AI tool:
Bad: "Build a social media app" Good: "Build a social feed: text posts (280 chars), follow users, chronological feed, likes/comments. Use React, Tailwind, Supabase."
Persistent context that teaches AI your conventions. Put it in once, applies to every interaction:
See
rules-files.mdBefore implementing, have AI explore and plan:
Catching misunderstanding during planning = 10x cheaper than debugging cascading errors.
AI generates code that looks flawless but has subtle bugs. After every change:
The Karpathy move: copy error message, paste with no comment, usually it fixes it. If AI can't fix after 2-3 attempts, describe the behavior you want instead.
Set explicit boundaries:
Good for: Prototypes, MVPs, internal tools, weekend projects, UI components, boilerplate, learning Bad for: Security-critical code, performance-critical code, compliance-heavy domains, long-term production systems
The best vibe coders understand architecture, spot bad AI output, and know when to intervene. If you can't evaluate whether AI produced good code, you need to learn more before vibe coding production systems.
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