Writing about AI, infrastructure, and what actually ships.
total 138 · ~11.5h read · updated Jun 25
One viral stat says low-paid women are most at risk from AI. Another says it's high-paid women. Both are real numbers — and they're measuring two completely different things. Here's the map that separates them.
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.
An MCP agent on my own OAuth token only ever sees what I could see — so the access boundary is the vendor's job. I believed that, until I realised the agent splits data protection into two halves and the vendor only ever sees one of them.
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.
