In
Introducing Flagr: An open-source feature flagging and A/B testing tool
Introducing Flagr: An open-source feature flagging and A/B testing tool
Share cardActual performance
7points
Made the leaderboard
Launch Intel predictions
Analyze your own launch →79%79% 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.
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
34%34% predicted probability of success on BetaList, based on ML models trained on real launch data.
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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