THE OPEN NOTEBOOK

A book for the curious.

Name: The Welvet feature book
Author: Samuel Watson

70 chapters inside the Welvet AI engine. Start with why it exists, then follow the ideas into layers, learning, and the systems that connect them.

New to the work? Start with Welvet in plain language, then read What Welvet is. Code examples and reported results below are preserved from the source edition. Read the complete book on one page →

Explore:Everything I · Orientation II · Foundation III · Layers IV · Runtime V · Systems VI · Model IO VII · Apps VIII · Stubs IX · Validate

6 entries · Clear filters

IV · Runtime

runtime/forward

A grid of heterogeneous ops needs one walker that dispatches by concrete type and fails loudly on unknowns.

IV · Runtime

runtime/backward

Training needs a reverse tape over the same ops forward used — no separate graph framework.

IV · Runtime

runtime/training

Suites and small nets need MSE+SGD and tween hooks without inventing an external trainer or a retained float32 master beside storage.

IV · Runtime

runtime/step — step mesh

Spatial feedback (remote links) needs a discrete-time mesh where every cell updates from a double buffer — different from a decoder wavefront. Cross-numeric train also needs the same mesh wi

IV · Runtime

Cross-numeric train + down-the-dem

Weight storage dtype and activation Tensor[T] are independent axes. Proving train without a retained float32 master means sweeping W×A — not only matched float32 acts.

IV · Runtime

TrainMode — 29 named updates

Backprop is one update, not the only one. Credit assignment (broadcast gap, head proxy, sparse duty clock) has to be a named axis you can race — not a comment in a notebook. Cameral Mix also