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

8 entries · Clear filters

II · Foundation

core — types & backends

Every polymorphic path needs one place for DType, LayerType, Activation, Backend, Tensor[T], and slim Layer metadata — without QAT morph defaults.

II · Foundation

weights — FormatNone MatVec

Unquantized matrices still need a typed store that streams MatVec and SGD without forcing a float32 master or Morph-as-training.

II · Foundation

quant — 20 pack formats

Inference, storage, and train need classic Q-packs, k-quants, IQ, Ternary/Binary, and Affine without a separate QAT mode or retained f32 master. Format is storage truth.

II · Foundation

simd — Plan 9 kernels

CPU peak needs hand-written AVX2/NEON without a silent Go fallback that pretends SIMD ran.

II · Foundation

webgpu — device GEMV & shaders

GPU paths must bind a real adapter. Host “fake GPU” was banned so suites cannot stamp WebGPU done when ALU ran on CPU.

II · Foundation

tiling — SC/MC & workgroups

MatVec throughput depends on tile size and when to go multi-core vs GPU workgroups. Centralizing caps keeps Dense and friends consistent.

II · Foundation

architecture — volumetric grid

Networks are spatial (Depth×Rows×Cols×LayersPerCell), not only linear stacks. Topology lives here; compute lives in layer packages.

II · Foundation

fusedgpu — decoder on device

Token-by-token host round-trips kill decode. A fused engine keeps weights and scratch resident for Q4_0 and BinaryG128 hybrid paths.