An interactive playground for cellular automata patterns

Hacker News

Last month, I stumbled upon an article about 1D reversible automata. https://richiejp.com/1d-reversible-automata I was struck by how simple mathematical rules could create patterns that look remarkably like microscope images of living cells. I had to explore this further, so I built Automata - an interactive visualiser where you can play with these patterns yourself. (Tip: Start with Rule 75 in reversible mode - you'll see what I mean about cell-like patterns) Excited to hear what you discover. Thanks to @richiejp whose article sparked this project. I see he's built a very good explorer since I read the article but mine has a couple of different features so I think it's still worth sharing.

Visit product View on Hacker NewsJan 31, 2025

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62%62% 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.

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

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos

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

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

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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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