I Made the Duolingo for Wine – Wine Bible

Hacker News

I recently launched Wine Bible, a mobile app for learning about wine through interactive lessons, quizzes and tasting guides. I built it because most wine education is either buried in books or locked behind expensive courses. I wanted a way to help people learn wine the same way they’d learn a language — through daily practice, fun repetition, and interactive lessons. Right now, Wine Bible includes: • A growing library of wine lessons (grapes, regions, wine making, wine business, sparkling wine, fortified wine) • Guided wine tasting with an AI-powered coach • Over 250 grape varieties with their taste notes, origin, history, food pairing and regions Would love feedback from anyone curious about wine, building in edtech, or who’s worked on similar educational apps.

Visit product View on Hacker NewsApr 21, 2025

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3points

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

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, notes · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, 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

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

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

22%22% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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