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Experiment tracking.

Weights & Biases-grade exploration of 5,000 training runs, locally, in milliseconds.
The buyer's problem

An ML researcher comparing thousand-run hyperparameter sweeps in W&B pays ~600 ms per refinement to the server. Two hours of iteration is one lost hour of waiting.

What Hadron replaces

The client-side filter/group/sort layer behind the W&B dashboard. The aggregation runs locally on every refinement instead of round-tripping.

What the buyer gets

A researcher iterates a dashboard at native input rate; the team's most-active surface in W&B becomes the cheapest one to operate.

Patents demonstrated: P1 P3 Reference customers: ML experiment-tracking platforms
How to tell Hadron is working

Hadron is not claiming the GPU is fastest. It picks the right backend per operation. The same sort routes to a different backend at 5,000 rows vs. 500 rows, and the filter that precedes it may pick differently again. Watch the Dispatch evidence panel beside each app: every column-click and slider drag prints which path was chosen, the score, and the per-operator plan.

GPUHardware-accelerated - chosen when batch size, shape and precision all favour it.
WORKERBackground thread - chosen when GPU transfer cost would dominate.
CPUInline - chosen for small workloads where dispatch overhead would dominate.
REFUSEDGPU categorically refused (patent-protected) - specific sort/filter shapes that would lose on GPU.
UI heartbeat: 0 frames painted Longest frame gap (last 5s): 0 ms main thread responsive

Experiment grid explorer

5,000 simulated training runs × 13 hyperparameters and metrics × per-epoch loss curves - all browser-local.

Click any column to sort. Tick rows to overlay their loss curves (up to 50 at once). Drag the facet sliders or chip filters; every refinement runs in your browser tab.

What to watch Sort 5,000 rows by validation loss, then drag the batch-size slider. The result settles before the slider stops moving - W&B's hosted dashboard cannot do that today at this scale.
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