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TypeScript + WGSLMLI

tensorforge-webgpu

A visual tensor compiler that lowers live graphs into browser-native GPU kernels.

9 tests; the default graph lowers from 13 to 8 nodes and the live GPU result is checked against a seeded CPU oracle.

Inspect source Open live demo
tensorforge-webgpu project overview
Verified result8 → 3primitive kernels to GPU dispatches

Evidence chain

The claim, with its attack surface exposed.

Recruiters can scan the result. Engineers can inspect how it was produced and where it stops being valid.

  1. Problem

    Compiler and GPU portfolio work is difficult to evaluate when the optimization pipeline is hidden behind build logs or requires a local toolchain.

  2. Mechanism

    Typed tensor IR, shape inference, identity canonicalization, operator fusion, output-rooted dead-code elimination, liveness-aware buffer reuse, and three generated WebGPU compute kernels.

  3. Attack

    Deterministic CPU/GPU comparison, incompatible-shape rejection, persistent-slot isolation, fusion toggles, memory-plan tests, and responsive browser QA.

  4. Boundary

    A narrow dense-f32 MLP experiment: kernels favor readability over autotuning, softmax is not a parallel reduction, and timing is hardware-local queue wall time.

Reproduce it

One command to the test boundary.

The repository contains the implementation, tests, benchmark harness, and documented limitations behind this page.

git clone https://github.com/asp53826/tensorforge-webgpu && cd tensorforge-webgpu && npm ci && npm run check
Verified withCPU oraclecompiler pass testsbrowser GPU validation