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Nemorize – AI-powered spaced repetition for learning anything

Hacker News

Nemorize – AI-powered spaced repetition for learning anything

I built Nemorize to solve my own frustration with flashcard apps: spending hours creating cards instead of actually learning. What it does: Describe what you want to learn (e.g., "Norwegian A1 vocabulary" or "React hooks") and AI generates a complete lesson with 15-25 questions Spaced repetition system tracks your progress across 9 mastery levels (Apprentice → Burned) AI evaluates open-ended answers for language courses and conceptual questions Works on mobile and desktop Tech stack: F# / ASP.NET Core backend Vanilla JS frontend and lit SQLite database Claude for AI generation and evaluation What makes it different: AI generates both the lesson content AND the flashcards, not just one or the other Stricter evaluation at higher mastery levels (Guru+ requires correct spelling/grammar for language courses) No subscription wall for core features Try it: https://nemorize.com I'd love feedback on the learning experience and any rough edges you find.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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: claude, apps, open · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, answers · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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