## Salka Elmadani

**Building [Inference-X](https://inference-x.com)** — 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**](https://git.inference-x.com/salka/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](https://inference-x.com) · [Documentation](https://docs.inference-x.com) · [Source Code](https://git.inference-x.com/salka/inference-x) · [Elmadani.SALKA@proton.me](mailto:Elmadani.SALKA@proton.me)

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*Morocco*