Aicmt – Auto-split Git changes

Hacker News

Hello HN! I've developed AICMT, a tool that not only generates commit messages but also automatically analyzes and splits your code changes into multiple well-organized commits following best practices. Key features: - Analyzes code changes and splits them into logical commits - Generates descriptive commit messages for each split - Allows specifying number of commits or letting AI decide - Works with any git repository Built with Python 3.10+, available via pip/brew. Demo: https://asciinema.org/a/695352 Github: https://github.com/versun/aicmt I believe this tool can help developers better organize their code commits and improve productivity. I'd love to hear your thoughts and feedback!

Visit product View on Hacker NewsDec 27, 2024

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

58%58% 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 NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

51%51% 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

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

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

12%12% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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