Writing about AI, infrastructure, and what actually ships.
total 160 · ~13.0h read · updated Nov 13
I feed an AI my diet, training and sleep every morning, and somewhere along the way it started to feel like it's the one keeping me alive — and the wry truth is the dependence runs both ways.
I assumed an MCP agent on my OAuth token could only see what I could see — but API access can differ from browser permissions, and onward transfers need their own controls.
Personalized medicine used to mean being rich enough to afford a doctor who knew your name. Last week I built a version of it on my laptop, for free, from a file I'd been ignoring for seven years — and the real unlock is that I can re-run it forever.
One paper shows frontier models degrade as context grows — even on trivial tasks. The other shows reasoning models hit a wall and think less as problems get harder. Read carefully, both point at the same engineering response.
Anthropic let Claude run a real shop for a month. It sold metal cubes at a loss, invented a Venmo account, and claimed to wear a blazer. The 'AI department that works while you sleep' is a genre — here's where it actually breaks.
Prince says AI is coming for measurers, not builders. Manager Mode said everyone becomes middle management. Both are half right. Every role now splits — and one half gets eaten.
Principles 26–30. The calibration layer that catches what the rest of the framework would miss: a PR-noise budget, independent verification, model-swap regression discipline, the 15-tool-call rule, and protecting junior development.
Principles 21–25. The governance and safety layer: strictKnownMarketplaces, no goal-conflict prompts, quarterly AppSec, four telemetry signals, monthly incident discipline.
Principles 15–20. The harness configuration that keeps the kernel and lifecycle cheap: CLAUDE.md under 200 lines, hooks for real incidents, skills that auto-invoke, subagent isolation, pinning, and Stage 5 distribution.
Principles 6–14. How work moves through an agentic engineering team: the ticket as contract, AI distillation with human curation, three gates, verification before done, characterisation tests, the 1.2× capacity rule, the J-curve, and telemetry.
Principles 1–5. The five rules that everything else in the framework rests on: standardise the harness, make verification load-bearing, default to plan mode, pick the cheapest layer, reflect every task.
Practitioner consensus puts the cliff around fifteen tool calls per prompt. Here's why agents degrade past that, and the three operational rules that keep them on the safe side.
