Beyond client work, I run deep technical experiments — and I publish the ones that fail, not just the ones that work. A method that only reports its wins is marketing, not engineering.
AI-native OS kernel
A from-scratch operating-system kernel in Rust with zero external crates — boots via UEFI to a graphical shell in ~195 KB, with an in-kernel AI inference experiment. Research into what an AI-native OS could be.
Frontier models on consumer hardware
Local LLM inference at scale: exact 7B inference in 2.2 GB VRAM, and JIT expert-loading for Mixture-of-Experts that exploits temporal locality — the path to running big models on the machine you already own.
What makes a model generalize
~140 GPU experiments on the real question: does a model understand, or interpolate? Finding: structure beats raw parameters out-of-distribution — with the exact boundaries measured, and several of my own predictions falsified along the way.
The graveyard of falsified ideas is documented, on purpose. That discipline is exactly what I bring to a client's system.
Have an AI problem that needs to actually work?
From a one-hour consultation to a complete build: AI adoption, custom software, assistants, automation, reliability engineering, security audits. Everything verifiable is on GitHub; the rest I'll show you live. Remote worldwide, based near Milan.