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Everyone uses AI now. Far fewer can actually build with it. Here’s the path between the two — and the order that makes it click.
There’s a strange gap in software right now. Almost every developer uses AI daily — autocomplete, a chat window, pasting an error and hoping. But there’s a wide difference between using AI and being able to build with it: to extend your own tools, wire models into products, and know when the output is quietly wrong.
I kept meeting developers stuck on the near side of that gap, unsure what to learn next. And when I went looking for a map, I mostly found “AI engineer” roadmaps aimed at ML researchers — matrices and gradient descent — not at working developers who just want to ship.
So here’s the path I actually wish I’d had, in the order that makes each step build on the last.
Foundations — how the model works #
Not the math, but the mental model: tokens, the context window, why it hallucinates, what a transformer actually does. You can’t push a tool past where you understand it, and most “AI is dumb” complaints are really “I didn’t understand the tool.” An hour with Karpathy’s Intro to LLMs and 3Blue1Brown’s transformer videos pays for itself many times over.