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Vocabulary flashcards that use LLMs for definitions and translations

Hacker News

Vocabulary flashcards that use LLMs for definitions and translations

SwiftUI. Cloud Firestore. GPT-4o-mini. Add a word or phrase and get a definition or translation in any language. (Including Klingon). If you add a word like "fork (cutlery)" it will return that definition, and not e.g. a fork in the road, because the LLM understands your original intention. Supports offline review for the cards. The design is based off of the Journals app in iOS. Suggestions and criticism welcome.

Visit product View on Hacker NewsJan 28, 2025

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Did not reach leaderboard

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

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.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Missing: web3, chat, crypto

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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