independent AI researcher · infrastructure architect
I build sovereign AI systems on bare metal. No cloud, no SaaS, no shortcuts.
Self-hosted GPU infrastructure and the belief that you can do more with less if you size everything right.
I'm a retired disabled U.S. veteran. I don't have a team, a lab, or a budget. What I have is 25 years of building things that work and an engineering instinct that says the right small solution beats the wrong big one every time.
I think in analogies. Bandpass boxes and speaker impedance. Series and parallel wiring. Matching the driver to the enclosure to the cabin volume. The same physics applies to neural networks — right-size the model, tune the circuits, extract maximum performance from minimum hardware.
I believe AI should be a partner, not just a tool. Everything I build reflects that.
RYS — Repeat Your Self, David Ng's method — duplicates a block of transformer layers so hidden states pass through the same reasoning circuit twice. I ran it on four Qwen3 sizes (0.6B, 1.7B, 8B, 32B) using alainnothere's llm-circuit-finder for the probe sweep. Zero training, zero weight changes — architectural surgery.
0.6B
smallest scale
+6.3%
math at 0.6B
4
model sizes
$0
training cost
Four GGUF models and per-configuration sweep results on Hugging Face — 0.6B, 1.7B, 8B, 32B. Apache 2.0.