OpenFluke
Shrinking massive AI into digital seeds
Weight-agnostic compression: dense matrices collapse to 64-bit layer seeds, with non-differentiable evolutionary search. Edge checkpoints measured in bytes, not megabytes.
Research · the Loom era
The research road to Welvet
Before Welvet there was Loom: the exploratory engine where the hard ideas were first tried, measured, and written up. This is the archive of that work, deep-dive reports and AI-generated audio briefings that trace how a 3D volumetric experiment became the disciplined engine we ship today.
The lineage
From Loom to Welvet
Loom proved the ideas; Welvet made them dependable. Same mission, sovereign AI on your own hardware, with the lessons of the research years baked in.
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Loom: the lab
A fast-moving research engine (M-POLY-VTD): a 3D volumetric grid, 21 numeric types, target propagation, and a topological DNA engine. Big swings, published openly.
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The rewrite
Loom's flat package hit honesty walls. Welvet is the clean rebuild: one feature per folder, tests kept out of the engine, and no silent fallbacks.
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Welvet: the engine
Today: 34 numeric types, three backends, and a public 137,039-cell proof run. The research became something you can ship and verify.
Deep research library
Reports and audio briefings
Nine release-era studies from the Loom lab. Listen in the browser or download the matching PDF. All files are hosted on files.openfluke.com.
Loom 0.83 capabilities and applications
Experimental acceleration: Plan 9 SIMD on ARM and x86 (no CGO or CUDA), Apple Metal zero-copy, and Qualcomm Hexagon NPU offload with the Drift Spectrum.
AI model bridging and runtime comparison
Live ingestion from PyTorch, TensorFlow, JAX, and scikit-learn into portable .entity checkpoints, plus a three-way runtime compare against ONNX Runtime, LiteRT, and Core ML.
Why fast AI hardware fails basic math
Per-layer NPU offload via OpenVINO, the small-tensor latency tax, 28x INT8 wins on large ops, and honest notes on when deterministic hardware still drifts from software.
Solving the M-POLY-VTD dispatch paradox
Why Plan 9 assembly wins on fat matrices but inverts on volumetric grids, the WebGPU validation tax, and the pivot to load-time GPU graph compilation.
Loom breaks AI vendor lock with Go
ENTITY native checkpoints, the Planet Bridging hub, and pure Go plus WebGPU: why importing a model is not the same as shipping a portable brain.
Loom Poly AI engine research
The flagship M-POLY-VTD deep dive: volumetric dispatch, 21 numeric types, target propagation, and side-by-side Go ML comparisons.
Operation mesh shrinks local AI
How Loom's operation mesh and release trajectory tightened the local-AI deployment story on consumer hardware.
Mac Mini beats RTX 4090 with Loom
Cache-aware tiling: why Apple Silicon plus Loom can outrun big discrete GPUs on the right workloads.
Continuity
Ideas that became Welvet
The research wasn't thrown away. Each Loom breakthrough has a home in the Welvet engine you can read about in the book.
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Volumetric grid
Loom's 3D coordinate mesh lives on as Welvet's architecture package for volumetric models.
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Numeric polymorphism
21 dtypes in Loom became 34 in Welvet's quant, from FP64 down to 1-bit.
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DNA engine
Topological signature matching is now systems/dna, with 16,000+ cells verified by w2a.
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Target propagation
The gradient-free training idea carries into the Welvet runtime training paths.
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Seeds & evolution
Weight-as-seed and evolutionary search became stub/seed and systems/evolution.
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WebGPU, zero CGO
Pure-Go WebGPU acceleration is a first-class Welvet backend, tested on real GPUs.
From the research
How the approach compared
A condensed view from the Loom deep-dive. The full analysis, with JAX and the Go ML ecosystem, is in the reports above.
| Dimension | Loom / Welvet | PyTorch |
|---|---|---|
| Execution | 3D volumetric mesh, spatial routing | 1D sequential / dynamic graph |
| Language | Pure Go, single native binary | Python + C++/CUDA |
| Quantization | Up to 34 types, FP64 to 1-bit | FP8/INT4/1-bit via TorchAO |
| Training | Backprop + native target propagation | Autograd only |
| GPU | WebGPU, cross-platform and browser | CUDA / ROCm / Metal |
| Footprint | Zero dependencies | Large runtime |
Where the research went
The Loom era is why Welvet exists. Read how every idea landed in the engine, or follow the build in the open.