AI music and auto-charting and custom rhythm minigame sandbox
I've been tinkering with a browser-based rhythm game creation tool. The pitch is simple: AI makes the music, Essentia.js figures out the beats, and you write the game logic in JS. Demo: https://rhythm-seodang-web.vercel.app/ The problem I wanted to solve: most rhythm game workflows are heavy. Proprietary editors, manual charting, fixed gameplay patterns, desktop-only. I wanted something where you could just... open a browser tab and start messing around. How it works: - Music comes from AI services (Suno/Udio). No user uploads, no copyright headaches. - Essentia.js (WASM…
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I've been tinkering with a browser-based rhythm game creation tool. The pitch is simple: AI makes the music, Essentia.js figures out the beats, and you write the game logic in JS. Demo: https://rhythm-seodang-web.vercel.app/ The problem I wanted to solve: most rhythm game workflows are heavy. Proprietary editors, manual charting, fixed gameplay patterns, desktop-only. I wanted something where you could just... open a browser tab and start messing around. How it works: - Music comes from AI services (Suno/Udio). No user uploads, no copyright headaches. - Essentia.js (WASM port) runs entirely in-browser. Beat tracking, onset detection, energy curves, segment boundaries, all client-side. - The output is a timing-only chart. What you do with that timing is up to you. The fun part is the minigame sandbox. Charts and gameplay are completely decoupled. You define spawn rules, input handling, rendering, all in short JS functions. Same chart can become a taiko-style drum game, a directional swipe thing, or something experimental. Preview runs instantly. Tech: Next.js, Essentia.js, custom rhythm engine, Canvas rendering, deployed on Vercel. Current state: playable with sample tracks, chart generation works, minigame workshop is functional. In-platform AI music generation (prompt to track to playable) is next. Still rough around the edges. If you've worked with WebAudio or rhythm engine internals, curious what you think. Feedback welcome.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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