Simple way to generate music with cellular automatons

Hacker News

At Toronto Music Hack Day I built a simple, yet sleek grid based interface to generate music with cellular automatons. Tap on the grid to add more atoms. http://tap-pad.herokuapp.com It's rather beautiful as although the music is random sounding, it's actually 100% deterministic. I made a web version and an iPad app. You can also share the state of the grid and share it via link: http://tap-pad.herokuapp.com/661621 Code is all open-sourced on Github under MIT: https://github.com/dmitric/tap-pad-web https://github.com/dmitric/tap-pad-ios

Visit product View on Hacker NewsAug 12, 2013

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

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

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

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

TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

17%17% 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

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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