Launch Intel
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GitInsight – Quick stats and AI roasts of your GitHub

Hacker News

GitInsight – Quick stats and AI roasts of your GitHub

I was initially working on a beginner backend project on roadmap.sh to sharpen my skills. This was essentially showing an aggregated view of your GitHub activity on the CLI. I thought, if I'm getting all the data anyway, then why not feed it to a LLM and make it roast people in multiple ways? GitInsight shows event counts per repo and gives AI powered feedback (roasts) after ingesting all your recent commits, PRs, issues and comments along with their messages. Please do let me know how you find it and I'm always open to feature requests!

Visit productView on Hacker NewsJan 14, 2025
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: activity, open · Missing: mac, agents, macos
81%81% 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 · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
21%21% 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
19%19% 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
13%13% 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.

Correct prediction on native model

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