DeepSeek’s latest paper is making waves, but the dense mathematics can feel intimidating.

The underlying idea, though, is surprisingly intuitive.

I have put together a visual explainer that walks through the key concepts using analogies rather than equations, from libraries and limousines to Wile E. Coyote and Gremlins. You do not need a deep technical background to follow; the deck builds intuition first.

We revisit the core Transformer stack (residual stream + attention + FFN) to show where scaling can create numerical instability, and how DeepSeek used geometric constraints to keep signals stable (illustrated by the infamous Ariane 5 numerical overflow failure).

In short: what DeepSeek actually did, how they turned a hardware constraint into a math feature, and why it could change how we scale AI.