Adds a major section documenting how @alkdev/flowgraph (already npm-published,
uses ujsx) becomes the compute graph authoring and execution layer for
alknet-tensor, replacing webgpu-torch's imperative nn.Module hierarchy and
autograd recording with declarative ujsx templates and reactive DAG execution.
Key points documented:
- The ujsx tree IS the compute graph (CUDA-graphs-shaped but declarative)
- flowgraph's two HostConfigs: GraphologyHostConfig (compile/validate) and
ReactiveHostConfig (execute with signal-driven status propagation)
- nn modules become ujsx components, autograd becomes reverse tree walk
- Conditional/Map components enable dynamic structure CUDA graphs can't express
- Network-callable compute graphs (mix local + remote ops in one template)
- TSX authoring via standard JSX→h transform (ujsx jsx-runtime as target)
- graphology → petgraph port: ~15 API methods map 1:1, removes ~5400 lines of JS
- Updated POC priorities: end-to-end skeleton now includes flowgraph integration,
petgraph host port as a separate POC
glm-5.2
f11522aaa4