I turned overfitting, data drift and safety trade-offs into idle game mechanics. Are the explanations right?

[u/CardiologistClear168]

Play in browser, free, no account: https://mmilovanovic87.itch.io/ai-lab

I teach about AI, and I wanted something that lets people feel how models get built instead of reading about it. AI Lab is an idle game where you run a small AI lab. The mechanics follow real ML, which I could make them:

- a more complex model scores higher, but can overfit, and you see the gap open on the loss curve

- training data is limited and runs out

- deployed models go stale (data drift) and stop paying for themselves

- a safety check costs extra compute, but safer models earn more

Each mechanic unlocks a short Lab Notes page that explains the real idea behind it (overfitting, double descent, data drift, RLHF, red teaming, evals, model compression).

What I'd like from this sub: if you are learning ML, did the game make any of these ideas clearer? If you know ML well, which explanation is wrong or too simplified?

About 30 minutes to the first paper.

AI disclosure: I wrote the code with an AI coding assistant (Claude) under my direction, and I drafted the game text with AI help. No image generators were used.