RuleHunt – TikTok for Cellular Automata

Hacker News

We built RuleHunt to search for interesting cellular automata rules using tiktok-style engagement monitoring. The first rule shown is Conway’s Game of Life. As you scroll, you will see other random rules – the search space is 2^512. Help us find good rule heuristics by starring the ones you like! On mobile it's a tiktok-like scrolling interface; on desktop it's an interface for targeted rule searching. Starred rules go to a global leaderboard. GitHub repo: https://github.com/rulehunt/rulehunt

Visit product View on Hacker NewsOct 22, 2025

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

22%22% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

14%14% 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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