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

7 entries · Clear filters

V · Systems

systems/dna

Quant and train must be measurable as topology/weight fingerprints — DNA detects logic shifts.

V · Systems

systems/evolution

Topology search and weight crossover need first-class splice + NEAT on CPU-resident grids.

V · Systems

systems/tween

Target propagation (chain-rule or Hebbian layerwise gaps) is an alternative credit-assignment path. Not the same package as TrainMode Tween / TweenChain on a Sandwich (those are layers/paral

V · Systems

systems/tanhi — TANHI · UDP HUD

Training visualization must never block the engine — best-effort UDP JSON-lines to a HUD.

V · Systems

systems/telemetry

Static structural blueprints (sizes, op kinds) differ from live TANHI events.

V · Systems

lucy — SoftAcc / Score measuring

Adaptation benches (test41-w, tide, live_gpt) need one shared measuring math — SoftAcc, Availability, AdaptPct, Score — not three copies of the formulas.

V · Systems

Lucy density — synthetic organism

A new host (char LM, MNIST, a layer sprint) should not copy tide's goldilocks math. The question is always the same: can the net run and train at the same time in a small box, then how far t