FeedbackVault
Indie HackersThe feedback memory system for managers
I built FeedbackVault because I experienced this problem myself: important feedback moments get lost, and reviews become too dependent on memory.
Share cardActual performance
1followers
Did not reach leaderboard
Launch Intel predictions
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Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos44%44% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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, 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, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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