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Practical notes from current work and experiments across architecture, inference systems, and infrastructure.

12/01/2027

Grade the Agent on What It Did, Not What It Said

A practical method for evaluating AI agents — completion decided from audit events instead of the model's own account, typed blockers as honest outcomes, repair budgets that ignore rewritten prose, and paired benchmarks on frozen inputs.

05/01/2027

Model-Agnostic Doesn't Mean Models Are Interchangeable

What it takes to run one agent across hosted and self-hosted models — a common wire format, per-model capability records, refusal instead of silent fallback, confidentiality that follows content, and domain rules that live in typed code rather than prompts.

29/12/2026

Live Numbers Inside a Static Answer

How to put real-time values into AI-written text safely — an explicit closed token instead of guessing, a component the sanitiser can't break, one shared subscription per conversation, and a display that always says whether a number is live.

22/12/2026

Let the Model Describe the Interface, Not Write It

An argument for agent-generated UI built on a closed, typed spec — data by reference, no styling or handlers in props, invalid specs returned as repairable results, sources that must be real, and a tightly fenced HTML escape hatch.

15/12/2026

Scaling Agent Workers on One Machine, Memory First

Sizing worker pools when there's no next node — ready capacity versus process count, cgroup-aware limits, warm drains, a parent/child deadlock that's easy to build, and an admission bug that double-counts memory.

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