Couscous – AI-powered code reviews that keep your team in sync

Hacker News

Couscous is a VS Code extension that uses AI to analyze your code quality against best practices and team conventions. It provides instant visual feedback, rewarding you with the satisfaction of delivering high-standard "code dishes" or... well, letting you know when your code needs a little more seasoning. Think of it as your personal code chef, ensuring your team follows conventions and best practices. Write clean, elegant code, and you'll feel like a master chef in a prestigious restaurant, serving up delicious couscous. But if your code is messy or inconsistent, you might just get a not-so-appetizing reminder to clean up your act. Features AI-powered code analysis using Deepseek Couscous icon for compliant files (score > 70%) Poop icon for files needing improvement Inline violation highlighting AI-generated improvement suggestions Quick-fix code actions

Visit product View on Hacker NewsJan 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, using, code · Missing: mac, agents, macos

72%72% 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

60%60% 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: code review, ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

37%37% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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