Practice all the songs you know on your musical instrument

Hacker News

I built this app because I realized that I forgot about a song for a while and therefore got a lot worse at that song. The app will make you play your lowest-proficiency songs (something you just started learning) daily. Medium-proficiency songs (you can play them, but you have room for improvement) get played several times a week. High-proficiency songs (you have mastered them) are cycled through at the rate of approximately one-two per day. This app is in no way complete and probably contains bugs. With that being said, I have been daily-driving it in its current state for several days without problems. There is no Apple version because I would have to buy a Mac and pay Apple $100/year for an iPhone app that I wouldn't even use. Hopefully you find this helpful!

Visit product View on Hacker NewsOct 20, 2023

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

89%89% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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

39%39% 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

19%19% 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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