Character-Based Git Conflict Resolution

Hacker News

Hello HN! I was always annoyed by conflicts that can be solved automatically, but still need human intervention. E.g. two people changing the same line, but at different, non-conflicting positions. So I searched for a character based patching library and found this nice article https://neil.fraser.name/writing/patch/ and its corresponding library https://github.com/google/diff-match-patch . Parsing git conflicts, applying patches and showing some useful diffs in the UI helps me to solve 80% of my conflicts automatically. I hope it can help you too. Happy Hacking!

Visit product View on Hacker NewsMay 13, 2022

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: google, way · 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

12%12% 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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