AyoobAI Hadron
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Trading.

Per-operator dispatch on a stream of 1,000+ market ticks per second.
The buyer's problem

A trader has 12 dashboards open. Each one consumes a market-data stream pushing 1,000-2,000 ticks/sec. Today's browser drops to 20-30 fps because every tick re-aggregates on the main thread with no per-operator routing - and the lost frames are lost money on time-sensitive decisions.

What Hadron replaces

The per-tick aggregation layer. Hadron's per-operator dispatch picks GPU for the heavy groupBy, keeps the buffer on GPU for the consecutive sort (inter-operator buffer retention), and stays on CPU for the small filter - re-evaluated every frame as buffer size grows.

What the buyer gets

60 fps holds at 1,000+ ticks/sec while a naive React baseline craters to 20-30. Every operator visibly chooses its backend each frame; the dispatch decision is observable for audit and compliance. Zero per-tick server round-trip.

Patents demonstrated: P1 P3 Reference customers: financial trading platforms, cryptocurrency exchanges, market-data dashboards
How to tell Hadron is working

Hadron is not claiming the GPU is fastest. It picks the right backend per operator, per frame, as the data shape changes. A 200-row filter runs on CPU; the 200K-row groupBy across the next 5 seconds of ticks runs on GPU; the sort consumes the groupBy's GPU buffer directly (core dispatch routing). The decision flips live as the streaming window grows or shrinks.

GPUStreaming groupBy when the visible window has thousands of ticks; sort consuming the GPU-resident output.
WORKERBackground thread when the per-frame work is borderline and GPU transfer would dominate.
CPUInline for the small filter step and any per-frame work too small to be worth dispatching.
REFUSEDcategorical refusal kicks in for operators whose shape would make GPU actively worse.
UI heartbeat: 0 frames painted Longest frame gap (last 5s): 0 ms main thread responsive
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Hadron is a product of AyoobAI.