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Gix – A small CLI tool that adds AI to Git

Hacker News

Gix – A small CLI tool that adds AI to Git

I’ve been working on Gix — a small CLI tool that brings a bit of AI into the Git workflow. Written in Go, cross-platform, and fully local (uses your own OpenAI key). It helps split large diffs into smaller commits and suggests conventional commit messages. Still early-stage, but usable. Open to feedback, contributions, or a star if you find it helpful. Repo: https://github.com/ademajagon/gix

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · Missing: mac, agents, macos
67%67% 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: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% 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
44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
27%27% 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
13%13% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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