Fabian G. Williams aka Fabs

Fabian G. Williams

Principal Product Manager, Microsoft Subscribe to my YouTube.

Trust the Model the Least. I've Been Building That at Home for Months.

This weekend Satya Nadella published 'Models as Insider Risks in the Super Intelligence Era': separate the supply of intelligence from the authority over it, put the controls outside the model, and build systems that let us trust the model the least. I agree, and I didn't have to start this weekend. On my own hardware and my own time I've been running that architecture for months: receipts that need outside proof, a judge that isn't the model being judged, frontier and local models graded side by side every night, and a public log of every time my agents got it wrong. Here's the map from his 7 principles to what I run, where local models take it further, and where I'm not there yet.

Fabian Williams

9-Minute Read

A timeline titled Trust the model the least: my public trail before this weekend. Dated posts: Aug 10, 2025 a local model and a hosted model side by side, fully traced; Mar 28, 2026 evals: the agent says done, did it do anything; May 16 public audit-trail receipts, same receipt frontier or local; Jun 7 your agent said it did the work, I checked the disk; Aug 1 Receipts, Evals and the Loop on adotob.com/receipts; Aug 2 every task replayed on a local model, both graded; Oct 4 grading my own local AI by hand. Then Oct 10, 2026: Satya Nadella, Models as Insider Risks in the Super Intelligence Era.

BLUF (bottom line up front): This weekend Satya Nadella published Models as Insider Risks in the Super Intelligence Era. His line: “The most trustworthy Super Intelligence system will not be the one with the model we trust most. It will be the one that enables us to trust the model the least.” I agree, and I didn’t have to start this weekend. On my own hardware and my own time I’ve been running that architecture for months, and I’ve been writing it up in public as I…

Don't Worry About the Terminator. Worry About David.

When people talk about AI ending badly, they reach for Skynet and the Terminator: a machine that wakes up and turns on us. I think the better warning is David, the android in Prometheus, who did harm because of what his maker set out to do and taught him to value. Mo Gawdat makes the same point: AI is a magnification of whatever humanity is. So responsibility is shared. The labs owe us broader data, tighter release discipline and a business model that doesn't make companies rent back their own intelligence. The rest of us owe ourselves something too: stop giving our data away, catalog it locally, and build our own ontology that compounds. Here's what that looked like for me this weekend, grading my own local AI by hand.

Fabian Williams

11-Minute Read

A bar chart titled One weekend grading my local AI, by hand. My 3 real questions: 3 pass. 21 trick questions, traps and unknowns: 17 pass, 4 partial, 0 fail. Invented claims: 0 in the 3 real questions, 1 in the 21 trick questions.

BLUF (bottom line up front): I’m just starting too. My data has lived in my Obsidian vault, my 2nd brain, for a while, but the practices around it were fragmented. They’re now coming together as a whole, and that whole complements, and at times runs ahead of, what I focus on at work: observability, accountability, governance and ownership. My rule for all of it: if you can’t see it, you can’t observe it; if you can’t observe it, you can’t measure it; if you…

Limen: I Put a Camera at My Window and Asked Who Owns What It Sees

This week is Fix, Hack, Learn at Microsoft, and my project is a local-first street-vision system I am calling Limen, the Latin word for threshold. An Arduino board watches my front window, a vision model on my own MacBook Pro M3 Max names what crosses (person, vehicle, wildlife), and a human confirms the label to build a training set I actually own. No cloud, no subscription, no frame leaving the house. Today I ran a scripted 90-second proof of concept: I walked up, drove in, parked, and walked to the door, and every one of 180 frames was auto-labeled locally in about 8.4 seconds each. Here is what worked, what did not (plates are still unreadable), and the one hard constraint that shaped the whole architecture: my evals rig is sacred and this project was never allowed to touch it.

Fabian Williams

9-Minute Read

A view from a front window: a man walking up the path toward the door holding a bag and a bottle, boxed on device with a green person label, with his vehicle boxed and a blue vehicle label, and a caption reading Limen local VLM auto-label on-device no cloud

This week is Fix, Hack, Learn (FHL) at work (Microsoft), our internal week to chase a build we care about. Last FHL I worked on my agents and agentic loops, helping me scale myself and my work as a product manager. This time my project is personal, and it starts with a question I could not stop turning over: when a camera watches the front of my house, who owns what it sees?

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