LyricFluent – Learn Languages with Music (Mainly Spanish)

Hacker News

My motivation for this product was to 'scratch my own itch'. I speak a few languages, have been learning a few more, and have always enjoyed listening to music in my target language. After failing to find my ideal solution, I built my own. I also wanted to give myself this challenge so that I could become a better full-stack software developer. I built the website with React & Next JS, mobile apps with React Native, and the backend with Node JS. Hosted on DigitalOcean. I worked on this for 4 years (it has been a while), but most of this time was part-time while working another job for income. The URL linked here has free lessons with no account required. Main landing page: https://lyricfluent.com Feedback welcome!

Visit product View on Hacker NewsSep 22, 2024

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Actual performance

3points

3comments

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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: apps · Missing: mac, agents, macos

51%51% 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

33%33% 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 · 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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