Billard – Generate music from ball collisions in 2D space

Hacker News

Hello HN! Here's Billard. It combines music and physics into a unique creative tool, as I explore various unconventional methods for generating music. Most traditional music composition tools revolve around the idea of a repeatable pattern. Billard is a webapp that never repeats itself. It generates music automatically based on the collisions of balls in a 2D space. Collisions trigger notes (or chords) in a given key. One can add balls or move them (y-position is pitch); the app remembers its state between reloads; or it can be reset with the 'init' button on the top left. Gravity can be adjusted in real time to change the behavior of the balls. It owes a lot of inspiration to Brian Eno and Erik Satie (inventor of musique d'ameublement , or "furniture music"). Some may think the lack of pattern makes it not musical enough -- but this lets it be listened to —and watched— for a while without boredom. The webapp is made using plain JavaScript. (All SVG icons were made 'by hand'.) It uses Tone.js only for triggering piano samples. Beyond piano, it's MIDI-enabled and works well at slow speed with haunting, dark synth sounds. Hope you like it!

Visit product View on Hacker NewsJun 18, 2024

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

88%88% 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: using, notes, plain · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

Correct prediction on native model

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