Documents the architectural direction for a PyTorch-shaped tensor computation
library built on Rust + wgpu, where QuickJS is a thin API/composition layer
and Rust owns memory, dispatch, and WGSL codegen. Derived from webgpu-torch
as the reference design (op_spec → opgen → WGSL shader pipeline) but not a
port of its code — webgpu-torch is the reference, alknet-tensor is the
production architecture.
Key decisions: JS holds handles (BufferId), Rust owns wgpu::Buffers; ~4-5
high-level Rust ops (create_tensor/dispatch_kernel/register_kernel/read/write)
not ~20 low-level GPU API calls; WgslGenerator as a third handlebars backend
in typebox-rs codegen alongside RustGenerator and TypeScriptGenerator; tensor
ops as OperationSpecs on the registry (network-callable over irpc, verified
protocol-compatible on quickjs by POC 2).
Documents the downstream problems this solves as a side effect: distributed
compute over irpc, LLM-authored model code (toolEnv pattern), edge/embedded
tensor compute, the compositing problem sidestepped (compute has no surface),
and cross-platform by construction (wgpu's many backends).
glm-5.2
7d7b99c04d