Build a Reusable Cellular Automata Engine

Mon, 10 Aug 2026 20:56:00 +0100

Across this book we repeatedly rebuilt the same pieces:

state
neighborhood
rule
step loop
measurements
rendering

That repetition was useful while learning.

Now it is time to turn those concepts into interfaces.

Keep the engine small

A useful engine does not need to know what Conway’s Life, Lenia or an NCA is.

It only needs to orchestrate state transitions.

from dataclasses import dataclass
from typing import Callable, Any


@dataclass
class Automaton:
    state: Any
    step_fn: Callable[[Any], Any]

    def step(self):
        self.state = self.step_fn(self.state)
        return self.state

    def run(self, steps):
        for _ in range(steps):
            self.step()
        return self.state

That is intentionally boring.

Build a Cellular Automata Laboratory

Mon, 10 Aug 2026 21:00:00 +0100

We now have enough pieces to stop thinking in terms of isolated scripts.

A useful cellular-automata laboratory should let us define, run, measure, replay and visualize experiments across many kinds of automata without hiding the mechanisms we spent the whole book learning.

The laboratory is not the model

Keep these concerns separate:

model definition
execution backend
experiment configuration
metrics
artifacts
analysis

A rule should not know where its PNG is saved.

Software Design on AIBussin — AI applications, systems and books

Build a Reusable Cellular Automata Engine

Keep the engine small

Build a Cellular Automata Laboratory

The laboratory is not the model