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Betafeedback – Tools for Feedback Loops and Sprint Reviews
Hacker NewsBetafeedback – Tools for Feedback Loops and Sprint Reviews
Share cardActual performance
33points
6comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive69%69% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto59%59% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal24%24% 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, podcasting23%23% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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