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GitInsights – a lightweight developer metrics platform
Hacker NewsGitInsights – a lightweight developer metrics platform
I originally launched GitInsights on HN 11 months ago, and got some brutal (but good!) feedback. Since then, I have used feedback to transform the product from "for eng managers" to "for eng teams", added/removed/improved metrics, and in the process picked up some decent traction. Same name, completely different product. As always, any feedback is welcome.
Share cardActual performance
9points
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Made the leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos63%63% 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, podcasting52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released47%47% 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 · Strong signals: platform · Missing: plus, intuitive, reviews40%40% 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, revenue20%20% 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, crypto7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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