Octokraft – code health and PR review for AI-assisted teams

Hacker News

Maintaining software goes beyond PR review. Octokraft is a technical debt management platform that helps you ship confidently by validating patterns, consistency, security, and more across your repositories. It tracks code friction, development practices, PR reviews, stacked PRs, and more. It detects architecture drift, learns your team's conventions and enforces them on new PRs, measures test quality beyond just coverage, and shows you which parts of the codebase are getting harder to work in. When something regresses, you know about it before it compounds. Free for 1 project and you can find a bunch of open source projects already analyzed.

Visit product View on Hacker NewsApr 21, 2026

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

88%88% 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.

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

39%39% 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: platform, reviews · Missing: plus, intuitive, host

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

28%28% predicted probability of success on Indie Hackers, 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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