Lexic – GPT-powered eBook reader for language learners

Hacker News

Hi guys, I created a simple ebook reader on top of Readium and the GPT API. The idea was to automate creating flashcards for any new words I encounter while reading foreign language literature. Before my process was the following 1. Highlight the word/phrase 2. Go back to all highlighted words when on my laptop 3. Check them in Google Translate 4. Get some context and examples from ChatGPT 5. Create a flashcard in Anki The app does all that automatically and I use it daily, so if anyone finds that useful, here is a URL for the TestFlight beta - https://testflight.apple.com/join/9pJ8x81Y .

Visit product View on Hacker NewsDec 7, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, new · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, 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: ide · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

46%46% 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

41%41% 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

16%16% 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 · Missing: web3, crypto, cryptocurrency

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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