I

I spent two months optimizing my Git client

Hacker News

I spent two months optimizing my Git client

Last year I released Retcon, a Git GUI specifically made for rewriting history. Think `git rebase -i`, but with all the friction removed: no need to stash changes, a single undo command that works in any situation, and the ability to make changes even when there are conflicts. (so a bit of an alternative take on jj, that's more Git-compatible and comes with less mental load, but has its own tradeoffs) Today I'm releasing version 1.4, which has basically one difference: everything is much faster. The app handles histories with hundreds of thousands of commits now, where before it'd freeze for seconds just trying to open these repos. So it's exactly the same thing as before, just much more pleasant to use, and usable on vastly larger repos. I'm planning to write a series of blog posts of all the different optimizations once I've had time to rest, but in the meantime, I wanted to show the result of these efforts! If you're interested, the linked page has a couple of graphs showing some specific speed measurements.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, open · Missing: mac, agents, macos
86%86% 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: compatible · Missing: supports, reddit linkedin, podcasting
78%78% 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
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
42%42% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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.

Incorrect prediction on native model

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