I made an app to learn guitar scales

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Hi, I'm Sergei, the creator of Guitartonic. I've been playing guitar for 17 years, but never really learned how to improvise. Most of the elaborate music theory PDFs I found online overwhelmed me, so I built a simple tool to practice basic scales and modes. It worked so well that it even inspired my roommate to pick up his guitar again. The app has been live for a few months. I had moved on to other projects and almost forgot Guitartonic existed, until the website started getting organic traffic. I noticed that people were coming to the site (somehow, 90% of the traffic is direct) and staying for four to ten minutes on average. So someone was actually using it! I even received a few heartwarming messages from users saying Guitartonic rekindled their love for playing guitar. Yesterday, someone donated $25 to support my work, which motivated me to share the project here and ask for feedback. Fellow guitarists, how did you learn scales and improvisation? What helped you the most along the way? I'd love it if you gave Guitartonic a try and let me know what you think!

Visit product View on Hacker NewsApr 29, 2025

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

86%86% 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: exist, io · Missing: https docs, excited, just released

61%61% 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: user, using · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

21%21% 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.

Incorrect prediction on native model

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