Mnemeo – flashcard app with typing and rating modes

Hacker News

Hi HN! I built Mnemeo after years of searching for the right flashcard app. I loved Memrise's typing mode and clean UI, but when they pivoted away from user-generated content, I had to find alternatives. Anki is powerful but wasn't quite what I wanted - the UI felt dated and the web version is limited. Plus I kept hitting ease hell. So I built what I personally wanted: a modern spaced repetition app where you choose between typing mode (for exact answers - spelling, code syntax, languages) or rating mode (for concepts and quick reviews). Same cards, different review styles based on what you're learning. Key features: - SRS algorithm with intuitive 3-point scoring (wrong/close/correct) that avoids ease hell - Everything accessible in 2 clicks - no menu diving - Full keyboard shortcuts - review cards without touching the mouse - Import from Anki/CSV (I migrated my own Anki decks and Memrise courses this way) - Export your study data to CSV - Leaderboards and personal study metrics - Responsive web app - add, edit, study, organize from any device - Text-only for now - no audio/images yet Completely free for now, but looking to add premium features. Just launched after building what I wanted for my own studying. Would really love your feedback and what features matter most to you!

Visit product View on Hacker NewsSep 24, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: plus, intuitive, reviews · Missing: platform, host, exclusive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, answers, way · Missing: mobile apps, ios, entrepreneurs

43%43% 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

19%19% 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.

Incorrect prediction on native model

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