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Vokabeln.io – AI generated, spaced repetition flashcards

Hacker News

Vokabeln.io – AI generated, spaced repetition flashcards

Hi HN, for the past few months, I have been building a ChatGPT powered app for learning German vocabulary. During my stay in Germany, I wanted to get my German to a higher level. I have tried all of the top 20 language learning apps, and was extremely frustrated that most of them are about gamification and making me do childish exercises that don't really teach anything. I have also tried Anki, but could not keep myself disciplined enough to be noting words I want to learn, translating them, and finding sentence examples. Decks did not work for me either, as I know the top 1-2000 words. Vokabeln.io allows studying vocabulary in two ways: Either by word frequency or from collections. The collections can be generated from any piece of text, from which the app extracts (mostly correctly, trennbare verben can be tricky) vocabulary, including conjugations, and the user only marks the words they don't know. The practice part similar to Anki, but simpler with only swiping left/right/top depending on how well user knows the word. I have found Anki's way of showing four options mentally distracting. During practice, ChatGPT generates sentence examples and word explanations (very useful for German compound words). It's still very early work and likely has some bugs, but I'd be very glad for any feedback! It's on Play Store and App Store (tech-stack is Flutter + expressJS + postgres), the links are here: https://vokabeln.io The App is paid after 7 days, but you don't need to start a free trial or create an account to test it out.

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Actual performance

3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
70%70% 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, including · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, month, way · Missing: mobile apps, ios, personal
61%61% 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, chatgpt · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% 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, 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: chat, paid · Missing: web3, crypto, cryptocurrency
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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