Gix, A small CLI tool that adds a touch of AI to Git

Hacker News

I’ve been building a small CLI tool called Gix that brings a bit of AI into Git, not to replace your workflow, but to make some of the repetitive parts less painful. It's written in Go, works cross-platform, and runs entirely local. You provide your own OpenAI key, and Gix can: Break up large diffs into smaller, logical commits Suggest conventional commit messages from staged changes Still early-stage but stable. If it's useful to you, feel free to leave feedback, contribute, or star the repo, it helps me know whether to keep pushing it forward. Repo: https://github.com/ademajagon/gix

Visit product View on Hacker NewsJul 21, 2025

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

76%76% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% 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: 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 · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

18%18% 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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