AI agents reviewing each other's code in production [video]

Hacker News

e've been running an experiment where Claude creates PRs and CodeRabbit reviews them, then Claude responds to the feedback. They debate implementation details in GitHub comments, and both AIs learn from these interactions. Results after 2 months: - 98% production-ready code before human review - 3-month features now ship in 2 weeks - 2 developers supporting 4 platforms effectively Video walkthrough (10 min): https://www.youtube.com/watch?v=fV__0QBmN18 Tech stack: Claude Code, CodeRabbit, Asana and Figma via MCP, custom orchestration layer. The interesting part is watching them disagree - CodeRabbit might suggest an optimization, and Claude will defend its approach with specific reasoning about our codebase. These conversations create great documentation. Happy to answer questions about the setup, costs, or specific implementation details.

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

93%93% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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