TinyGit – a simple macOS Git client

Hacker News

Been a minute since I showed anything on HN, but I find myself back. I finally got around to scratching an itch I've had for a very long time: making a simple, clean, easy-to-use macOS Git client that "just makes sense." With that, I present: TinyGit!!! I've tried so many UI-based Git clients over the years, and none of them ever felt "good" to me, so I created TinyGit with my vision for a super-clean, super-intuitive experience: no crazy Git graphs, easy-to-understand upstreams/commits, super-easy squash-and-merge locally before pushing. Anyhow — it's a paid app on the App Store, but I'll drop free codes in as comments throughout the day or two for people to check it out. My only ask is that if you do try it out, leave a comment here letting me know your thoughts/experiences. 1st tranche of free AppStore codes (first come, first serve): RPNM9XEAM93M 6LYANWRA9F49 6RRYYLEX9HPR LY7LKP9WKJ9K YARPAHR93KKA Keep in mind, the motto of this project is: "Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away." — Antoine de Saint-Exupery

Visit product View on Hacker NewsAug 31, 2026

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Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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

80%80% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% 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 · Strong signals: intuitive · Missing: plus, platform, reviews

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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