Fabian G. Williams aka Fabs

Fabian G. Williams

Principal Product Manager, Microsoft Subscribe to my YouTube.

What Happened When My Ducati Monster 821 Fell Off a Dealership Lift

A factual, chronological account of what happened to my 2015 Ducati Monster 821 while it was in a dealership's care, how the staff responded, why the trade numbers did not work, and the repair plan. Not a bash piece, a record.

Fabian Williams

9-Minute Read

The fuel tank of a 2015 Ducati Monster 821 Dark on a shop stand, with blue tape marking damaged areas

I brought my 2015 Ducati Monster 821 to Bob’s BMW for an ABS and traction-control (DTC) diagnostic. While it was in their care it fell off a service lift from about 3 feet, landed on its right side, petrol tank got damaged, along with the front wheel on the way down plus broken rear break pedal, etc. etc.. The written estimate for the damage came to $7,268.88. I looked at trading the bike instead of repairing it, but the trade number worked out to roughly $3,000 after the dealer’s…

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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