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Gitpatch – send patches with git push

Hacker News

Gitpatch – send patches with git push

Hey HN, I built Gitpatch to make sending code patches as simple as git push. The idea is that patch-based workflows are powerful but they're not very well supported by git platforms. Gitpatch brings this model to any repository: * Send patches by pushing branches (e.g. patch/my-patch-name) * Apply any patch locally with one command * Review them in a lightweight UI * Stack patches with patchstack/ branches This has been in development for some time, but things should mostly work. Would love any feedback, questions or ideas. Thanks for checking it out.

Visit productView on Hacker NewsJul 23, 2025
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4points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, code · Missing: mac, agents, macos
91%91% 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: ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% 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
42%42% 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
33%33% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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