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Flashcard app to learn the vocabulary of English books/series/movies

Hacker News

Flashcard app to learn the vocabulary of English books/series/movies

Made this app because I was tired of not knowing 50 words/book when I read in English. You can try it on the AppStore searching "Quicky app". For Android user please use the web mobile version at https://quicky.app/. The Desktop version has a shitty responsive design. IMPORTANT NOTE: If you are not French, you will want to tell you are to try the features of the app (sorry it's only in French though) I'd love to hear your feedback.

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Actual performance

7points
1comments
Made the leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps, user · Missing: mac, agents, macos
31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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