Gen Commit – AI generates your Git commit messages

Hacker News

I was tired of writing commit messages and wanted something more intelligent than Cursor's suggestions. Gen Commit is a simple CLI tool that automatically generates git commit messages. Just run gencommit instead of git commit and it analyzes your staged changes to craft a (proper) descriptive commit message and description. Especially nice to use an agent to figure out where things went wrong, since the description is pretty verbose with details about what changed. You can configure it to use different AI providers and models. Available via Homebrew or pip. GitHub: https://github.com/raghavpillai/gen-commit

Visit product View on Hacker NewsAug 22, 2025

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63%63% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, 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

36%36% 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 · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, 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

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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