LanguageMe AI- Custom readings and smart flashcards to learn languages

Hacker News

Attention all language learners! I built an app that allows you to generate reading material according to your language level, then learn that content with spaced-repetition flashcards. I’m an avid Mandarin learner. For me, I’ve found the two most effective methods to learn vocabulary are: 1. Reading content that I’m interested in and is suitable to my level. 2. Flashcards that show words in context, and appear at increasing intervals (spaced-repetition). So I built an app that combines these two methods. Generate a reading about a topic you love -> click words you don’t understand to add them to your flashcards -> study the flashcards -> repeat. The app uses GPT-4o to generate the readings and flashcards, OpenAI’s TTS to speak them, and OpenAI’s Whisper to track words as readings play. It supports 65 languages. It’s called “LanguageMe AI” and it’s available on the Apple AppStore. It requires creds to login but no email verification, so feel free to put in anything you like.

Visit product View on Hacker NewsJul 26, 2024

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

84%84% 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: apple, apps, email · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, 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

43%43% 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.

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: smart · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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