INFRA | SOFTWARE | HARDWARE
I gave a 160M-parameter Go model a reflect-and-fix loop so it could bootstrap past its own ceiling. It fixed zero bugs. The reason runs deeper than the code — and it taught me my model had been memorising all along.

A filter is two polynomials, and the roots of those polynomials are the whole story. Place a few points in the complex plane and you can read the entire frequency response straight off the geometry — no calculus required at the point of use.

Every neural network you've ever used was trained by the oldest trick in calculus: to minimise a function, walk downhill. The whole story of modern optimisers is a list of the specific ways plain downhill walking fails, and the patch for each.

Small, working simulations — drag the controls on the full pages, or just watch the previews cycle here. The selection rotates daily.