Zorbi
Indie HackersFlashcards that predict when you're going to forget them.
Existing spaced-repetition tools are difficult to use and aren't designed for the way that people actually use them. We want to build an efficient study tool that people actually want to use.
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2followers
Did not reach leaderboard
Launch Intel predictions
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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, 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, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive41%41% 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, revenue14%14% 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, crypto0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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