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"Git who" – A new CLI tool for industrial-scale Git blaming

Hacker News

"Git who" – A new CLI tool for industrial-scale Git blaming

I've always wanted a better way to explore the authorship data embedded in a Git commit log. I'm having fun building a CLI tool to do this. It's a bit like the "Contributors" tab on Github that shows you how many commits each contributor has made but much faster and with many more options. If you get a chance to try it out, please let me know. I'd love to hear feedback and suggestions. Thank you!

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Actual performance

252points
127comments
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
85%85% 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
51%51% 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 · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

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