Salka Elmadani

Building Inference-X — better output from the same model.

Universal AI inference engine. Fused computation, adaptive precision, surgical expert loading. 305 KB, 19 backends, zero dependencies. Built in Morocco for the world.

What I build

Project	What it does
Inference-X	Universal inference engine — 305 KB binary, 19 hardware backends, 23 quantization formats, fused dequant+dot kernels, Shannon entropy adaptive precision. Same model, cleaner signal.
Z-EUL	Mathematical framework for bias-free analysis of neural networks. Z = dI/d(log s) · exp(i theta). Used to measure and optimize AI model architectures.
Organ Architecture	Neural network surgery — extracting, measuring, and grafting components between AI models to create functional chimeras.

How it works

The same model produces higher-fidelity output through Inference-X because the computation path is cleaner: fused kernels eliminate intermediate buffers, adaptive precision allocates depth where it matters, and surgical expert loading keeps only active parameters in memory.

A smaller model running through a clean engine can outperform a larger model running through a noisy one.

Philosophy

The best inference engine is the one you do not notice. You should hear the model, not the framework.

Links

inference-x.com · Documentation · Source Code · Elmadani.SALKA@proton.me

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Morocco