Notes on how AI works
Writing
Explainers on the ideas behind modern AI, from the mathematics of learning to the models and systems built on it.
Each piece takes one idea, derives it from first principles, and checks it with code you can run. They are written for students, practitioners, and anyone who wants to see why the methods work rather than only how to call them.
1 pieces · newest first
Probability, linear algebra, and optimization, and why the standard losses and update rules look the way they do.
How language models are trained, what they optimize, how they generate text, and where they go wrong.
Models that plan, call tools, and act in loops, and the engineering it takes to make them reliable.
Where the everyday loss functions come from. Choose a probability model for the target, write down its likelihood, and the loss follows.
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