Most explanations of a language model stop at the outside: tokens in, probabilities out. Mechanistic interpretability asks the harder question — what is the model actually computing inside, circuit by circuit?
There is one reframe that has to land first, or the rest stays abstract: stop reading a transformer as a stack of layers. Read it as a residual stream — a shared channel that every layer reads a subspace from, computes on, and writes back to. Once you see it that way, attention heads and MLPs stop being a blur and become circuits you can name, probe, and reverse-engineer — much the way you'd reverse-engineer a piece of malware.
The guide below is a map of that territory: the residual stream, what attention heads and MLP neurons actually do, features and superposition (why a single neuron can mean many things), and how researchers pull human-readable structure back out of the weights.
It runs full-screen, with live diagrams you can step through.