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Superlang – a language-learning e-reader with adjustable difficulty

Hacker News

Superlang – a language-learning e-reader with adjustable difficulty

Hi HN, I built superlang.com to solve an interesting problem in the language-learning space. One of the leading theories in language acquisition is the "input theory", which suggests that the best way to learn a language is by reading as much as possible. But there's a catch: 1. The material must be at a level where you can understand a reasonable percentage of the text. 2. It has to be engaging enough to keep you reading. Balancing these two factors is surprisingly hard. Real stories with real plotlines (The Wizard of Oz, Romeo and Juliet, or even War and Peace) are too difficult for most language learners to read. On the other hand, simple short stories purpose built for language learning (think Hans goes to the market, or Amy orders at the restaurant) are typically not very engaging. Superlang aims to solve this dilemma completely. Some notes on the features: * Choose from a wide range of public domain stories * Adjust the difficulty level of the text to match your reading ability * Re-read the same story at increasing difficulty levels as you gain confidence * Each page has illustrations to aid comprehension * All the bells and whistles are there: sentence/word translations, grammar insights, audiobook mode * All content is AI-assisted but human reviewed, ensuring accuracy and avoiding the pitfalls of AI-only language learning tools I’d love to hear your thoughts and feedback. Whether it's about the app, the tech behind it, or what's next, feel free to ask me anything! Cheers, Creed

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: notes · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
34%34% 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
32%32% 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
15%15% 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.

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