Life4.py – visualizes John Conway's Game of Life in a command terminal

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life4.py is a Python program that visualizes John Conway's "Game of Life" in a command terminal, with additional controls to move the viewpoint and to zoom in on or out of the animation. See for background and details: https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Summary: The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, each of which (at any given time) is in one of two possible states, "live" (alternatively "on") or "dead" (alternatively "off"). Every cell interacts with its eight neighbours, which are the cells that are directly horizontally, vertically, or diagonally adjacent. The way the pattern of cells develops is driven by two rules, the so-called "Birth- and Survival-rules". The result looks like a busy scene of moving "micro-organism"-like creatures, crawling, expanding, dying out and re-emerging.

Visit product View on Hacker NewsMar 25, 2026

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

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

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

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

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

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

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

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

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

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