Principal Product Manager, Microsoft Subscribe to my YouTube.
The matched observation-side discipline to announce-intent-before-action. Four small artifacts (Sensor Roll Call, Conflict Receipt, Gap Map, Cross-Source Dedup) the agent must emit before any decision. Currently running on a real fleet at MACONA; the receipts are public.
A small autonomous AI agent fleet I run as a volunteer for a 501(c)(3) nonprofit. Week 19 shipped 17 reliability PRs, 2 awareness-day blog posts, and 37 cold introductions — and earned zero human clicks, zero donations. This is the corrections panel I wrote on my own retro before anyone else could.
I volunteer with MACONA, a 501©(3) nonprofit that ships food, medicine, feminine hygiene products, donated computers, and clothing to communities and schools in West Africa. For a few few monthis now I have run a small autonomous AI agent fleet for the organization: five named agents, cron-driven, running through OpenClaw on a simple Windows box.
Two days benchmarking three Qwen3.6 variants against gpt-oss:120b on an M3 Max. A 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload 10x faster than gpt-oss — fast enough to seriously consider moving the work off SaaS frontier APIs. Plus the silent-hallucination trap I almost shipped through.
I spent two days benchmarking three Qwen3.6 variants against gpt-oss:120b on my MacBook Pro M3 Max. The shocking result: a 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload that I use for the non profit MACONA.org in 6 seconds — 10x faster than gpt-oss:120b on the same prompt. Fast enough that I now have reasonable confidence I could move this kind of work off the SaaS-hosted frontier models I have been paying for and onto local hardware on my dev machine. The deeper…
Fabian G. Williams aka Fabs Site