Fabian G. Williams aka Fabs

Fabian G. Williams

Principal Product Manager, Microsoft Subscribe to my YouTube.

I Added a Second Local Agent This Week. Here Is the Receipt for Every Human Decision Behind It.

I trialed a second local coding agent, Hermes from Nous Research, on my own MacBook, pointed at the same local Qwen 3.8 model I already run. Installed additively so nothing already working could break, governed with manual approvals, then tested until it proved it behaves. Here is the trial, in tables and screenshots, plus a receipt for the human hours behind writing it up.

Fabian Williams

7-Minute Read

The Hermes agent running against a local Qwen3.8-27B MLX server, with the Apple M3 Max GPU pinned at 97 percent on the first turn

This week I trialed a second local coding agent, Hermes from Nous Research, on my own MacBook Pro M3 Max, pointed at the same local Qwen 3.8 model I already run. I installed it additively, so nothing already working could break, governed it with manual approvals, and did not stop until it proved it behaves. Here is the trial, in tables and screenshots.

I Swapped My Local Coding Model Overnight From a Hotel. The Agent Graded the Upgrade Itself.

Local models are how I run my community work, my volunteer projects, my side hustles, and my musings, for two reasons: cost and keeping client data away from the labs. A new version dropped, so I upgraded it overnight from a hotel, additively, without touching the old one. Then I handed the new model the reviews and let my OpenCode agent test its own upgrade. It stood up a throwaway server, probed itself, and found three config gaps quietly throttling it. Here is the journey, in tables.

Fabian Williams

8-Minute Read

Activity Monitor showing the M3 Max GPU pinned at 92 percent while an OpenCode agent runs probe requests against a throwaway Qwen3.8 test server on port 8082

Local models are how I run my community work, my volunteer projects, my side hustles, and my musings. Two reasons, both simple:

I Made My Evals Replay Every Task on a Local Model. The Frontier Lead Got Thin.

My agents run on frontier models, but a free local model sits idle on a Mac Mini in my office. So I wired my eval system to replay every writing task on the local model and grade both. Across 10 like-for-like rematches the local model reached statistical parity — and beat the frontier model outright on four of them. Here is the receipts-first system that made it prove it.

Fabian Williams

7-Minute Read

Eval cockpit showing the verdict panel: 31 golden cases, 10 replayed, mean like-for-like delta -0.05, with Volume and Quality gates passing

My agents do real work on frontier models. Every dollar of that work is metered against my OpenAI and Anthropic bills. Meanwhile a perfectly capable local model, gpt-oss:20b, sits on a Mac Mini in my office costing me nothing. The obvious question: for which tasks could the free local model do the job just as well?

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