GPT4 CLI tool to draft your Git commit messages

Hacker News

Hey HN engineers, I've been working on a new utility that I think you'll find useful. Inspired by someone else's work with GPT for drafting git commit messages, I've developed my own Python CLI tool that does the same - now with the power of GPT-4! It generates commitlint-compliant messages and also checks for errors in the commit. It's already helping streamline my workflow and I thought you might want to give it a spin: https://github.com/ninjaa/gpt-commit-messages . Have AI take care of commit messages, so you can focus on what matters most - building amazing things! <3

Visit product View on Hacker NewsMay 18, 2023

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

61%61% 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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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

45%45% 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.

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

14%14% 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.

Correct prediction on native model

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