Real-Time Fuzzy Chord/Scale Identification with MIDI and ClojureScript

Hacker News

Hey HN! I made this web app to help me learn some music theory in real time whenever I stumble upon happy accidents such as chords, chord progressions, or scales that sound good, all without breaking the creative flow. It also fuzzy-finds everything so I don’t need to worry about playing exact notes. Written in ClojureScript, re-frame, and tailwind and is a private/static site hosted on Github Pages. Video demo: https://www.youtube.com/watch?v=8pHL7TzVDfk Let me know what y'all think!

Visit product View on Hacker NewsSep 11, 2023

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2points

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

69%69% 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: notes · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · 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: host · Missing: plus, platform, intuitive

29%29% 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 · Strong signals: real time · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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