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GitHub code review assistant
Hacker NewsGitHub code review assistant
Hi, I wrote a simple userscript to enrich the commit and pull request pages of GH. I find it useful to analyze big commits. Tested on Firefox 17 / GreaseMonkey 1.5. It's basically a vanilla JS so should work as a bookmarklet also but I prefer this to fire automatically. Looking forward to your comments. You can suggests enhancements / bugs on GH: https://github.com/jakub-g/greasemonkey-userscripts Enjoy, Jakub
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos74%74% 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.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: code review · Missing: https docs, excited, just released38%38% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting27%27% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%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, crypto12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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