I made a git rebase TUI editor

Hacker News

I use interactive rebase quite often, and particularly like the editor bundled with IntelliJ. But I do not always work with IntelliJ, and am not 'fluent' with Vim, so I tried to replicate roughly the same rebase experience within a TUI. I used a small TUI OCaml project i made last year. The notable features are: - Move commits up and down, fixup, drop - Rename commits from the editor (without having to stop for a reword during the rebase run) - Visualize modified files along commits - 'Explode' a commit ,creating a commit for each modified file (a thing I found myself doing quite often) Feedbacks (both on the tool and the code) and contributions welcome, hope it could fit other people needs too !

Visit product View on Hacker NewsOct 13, 2024

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

87%87% 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

65%65% 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.

TrustMRRFits verified-revenue profile · Strong signals: visualize, way · Missing: mobile apps, ios, personal

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

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · 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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