A language grows
by building things.

A simulation combines data, behavior, and repeated updates. Creatures move, resources change, populations grow and shrink, and the interface needs to show what happened.

That makes it a useful place to discover awkward APIs, ownership problems, and expensive operations. A feature that looks fine in a tiny demonstration may behave differently inside a long-running world.

Consequences make the experiment.

Inherited traits become interesting when they interact with limited food, water, movement, and time. Watching a population change helps turn abstract rules into observable behavior.

Observe before you optimize.

Repeatable experiments and measurements help distinguish an interesting ecosystem pattern from a software problem. Both can start with the same question: why did that happen?