Working Observations
Notes from the intersection of personal training, AI governance, and evidence-grounded practice.
These are working notes — observations from building things and noticing what connects. We log every step of our process the same way a good personal trainer logs every session: because the log is the program. Some patterns only become visible in the record.
What follows are the ones worth writing up.
A comic about three AIs, one trainer, and the unglamorous habit that separates the ones who get better from the ones who get hurt. The annotated version: every source quoted verbatim and linked, plus a free four-question governance starter you can steal.
Read →I built nutrition tools for my clients, then found a site in Australia had gone after the same problem. A field note on the human/AI line: AI for the trustworthy-data and availability layer, the human for relationship, judgment, and the did-it-actually-work check — with the numbers that say where it sits.
Read →Where the 2026 work on agent reasoning meets ALCOA+ data integrity: an autonomous agent's reasoning record can pass the Contemporaneous test and still not be the true account — a new failure class, with the receipts to check it yourself.
Read →A builder-to-builder field note: why automated accessibility checks throw false positives on modern JavaScript / Tailwind sites, how to sort them by cause, where declarative tests and a bounded local AI model actually help — and what nobody has measured yet.
Read →A case study of our own process — how a 530-word LinkedIn post was built on 6,000+ verified research artifacts, three draft variants, and the same verification discipline we use for federal grant evaluations.
Read →When your instincts as a trainer turn out to have names in the negotiation research — and the research hands you a better move for next time.
Read →