Fabian G. Williams aka Fabs

Fabian G. Williams

Principal Product Manager, Microsoft Subscribe to my YouTube.

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?

My Agents Were Passing Notes Through a 313KB Text File. So I Built Them a Message Board.

Amid the Grok Bot buzz, I read the docs, looked at my own brittle agent-to-agent handoff, and built a portable message bus for my Apple-and-local-models fleet. The third pillar after Receipts and Evals.

Fabian Williams

9-Minute Read

The Agent Board web view showing a threaded handoff between two named agents

My agents used to hand off work by appending to a single text file that had grown to 313KB. It was brittle, it was crude, and nobody could tell when a message had actually been read. This week, while the internet argued about Grok Bot, I read Grok Bot’s docs, looked hard at my own setup, and built my fleet a real message board: threaded, self-hosted on a Mac Mini, with an iMessage ping so a reply never sits unseen. It is the third pillar in a stack I keep compounding: Receipts proved the…

Doug Was Right: I Swapped In The MoE, And The Concurrency Math Changed

Last post I measured the concurrency ceiling on a 27B dense model and closed with a promise. Doug Ware, who builds applied AI systems, had handed me the one caveat I did not test: a dense model is the hard case, and a mixture-of-experts model with only about 3B active parameters per token should leave real headroom for a second slot to pay off. So I ran the exact same 36-load matrix on an MLX MoE, changed one variable, and let the numbers settle it. The dense model flatlines. The MoE keeps climbing. And at 8 concurrent agents the dense model makes you wait 32 seconds for a first token while the MoE answers in under 1.

Fabian Williams

9-Minute Read

A line chart showing aggregate throughput staying flat near 20 tokens per second on a dense model while a mixture-of-experts model climbs from 58 to 159 tokens per second as concurrent agents rise from one to eight

Two days ago I posted 36 barrier-synchronized loads against 1 local model and let the numbers answer a question Reddit handed me. That post ended with a promise. A reader named Doug Ware, who builds applied AI systems for a living, had replied with the one caveat I had not tested, and I said out loud that measuring it was the next thing I would do. This is that measurement. I did not wait, I did not hand-wave it, and I kept every honest asterisk in.

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