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ReadVocab – Turn English books into vocabulary flashcards

Hacker News

ReadVocab – Turn English books into vocabulary flashcards

Hi HN! I'm Mahmoud, and I built ReadVocab to solve a problem I faced while learning English: the frustrating experience of constantly interrupting reading to look up words. ReadVocab turns popular English books into pre-made flashcard sets, organized by chapter. It includes: - Audio pronunciation for each word - Contextual sentences from the book - Multiple definitions and translations - Spaced repetition system for efficient learning - 20+ books available (growing weekly based on community votes) The project took 2 years to develop. It's designed to help intermediate learners (B1-C1) complete a 400-page book in about 2 months while mastering 1000+ new words. Try it out: http://readvocab.com/?via=HN I'd love to hear your feedback, especially from fellow language learners or those teaching English!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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 NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, context · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
41%41% 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, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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