Vibe Coding Best Practices for our dev team

Hacker News

-- Backwards Build: Start with business slides → PRD → documentation → tests → THEN code. Generate code only after product makes human sense. -- Leverage MCP: Use Model Context Protocol to connect your AI with codebases, documentation and external tools in a standardized way. -- Standardize Aggressively: Minimal invention, heavy use of TypeScript(no to JSX)/strong typing in Python. Follow existing conventions. -- Strict Linting: Ruff for Python, ESLint for frontend - no exceptions. -- Verification Gates: Require AI to produce tests, docs, and diagrams with mandatory human review before proceeding. -- Size Limits: 50 lines max per function, 200 lines max per file. -- All in sync: Keep the entire chain -- docs, slides, code, tests -- in sync on a daily basis -- Collaborative Review: Human-AI joint code reviews with clear quality criteria. What other AI coding practices have improved your team's output?

Visit product View on Hacker NewsMar 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, context · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, code review · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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