Lex – learn languages by reading and writing, not just flashcards

Hacker News

Hi HN. I built Lex in an attempt to overcome the same hurdle that I always faced as a language learner: lots of vocabulary drilling, and no place to read or write. I'd find myself having to use a flashcard app, reading on my own, and then trying to find a space to write and none of them could communicate with each other. Lex combines the three to get 34 languages: Reading: leveled texts (CEFR A1-C2) or AI generated texts on a topic of your choice. Tap on any word when you read it to add it directly to your vocabulary! Writing: generate and/or compose an essay on a custom essay prompt, and receive AI feedback on structure, errors and an estimated essay level. Vocab: words that you save are used for spaced-repetition exercises (Flashcards, Match pairs, Fill-in Blanks, etc.). For the generation and essay feedback, I used OpenAI, and for front and back I used Next.js + Postgres. But it's early and there is much I have yet to do. Here are two things I'm totally unsure about and would love to hear HN's thoughts on: Quality of the AI essay feedback depends a lot on language, tuning prompts per language is harder than I thought. Unit economics: each essay check/retext creation is an API call and people want a language app to be cheap. Not quite sure of the sustainable pricing. I am polyglot and speak a few languages myself, so this is very much a tool that I created out of my own frustration. This is as much feedback as they can get, and every "this already exists, it's called X" is welcome.

Visit product View on Hacker NewsJun 23, 2026

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

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

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

40%40% 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

21%21% 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

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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.

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