Generate commit messages using AI

Hacker News

Hey everyone! I've just launched my latest project SplitJoin today. TLDR: SplitJoin helps you generate commit messages from source code using AI. Writing commit messages isn't the hardest problem to solve but I tend to write less informative messages over time (see xkcd: https://xkcd.com/1296 ). Using an assistant helps me stay disciplined and be a better communicator. My goal for the project is to complement copilot/etc on non-coding related tasks. I'd love to hear what people think about it and please share any feedback you have.

Visit product View on Hacker NewsNov 7, 2022

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

82%82% 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

75%75% 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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% 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

11%11% 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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