
Configure AI Models Instead of Hard-Coding Them
Keep model names in configuration, select models by task, and compare routes using the cost of completed work.

Keep model names in configuration, select models by task, and compare routes using the cost of completed work.

Why pull-request-controlled AI review instructions collapse a trust boundary, and how teams can restore it without giving up useful repository context.

Kimi K3 brings open weights to the edge of the closed-model frontier, challenging US control, enterprise dependence and trillion-dollar AI valuations.

An opinion piece on the US government gating frontier AI releases, the security argument, and the commercial cost of slowing models down.

A speculative look at AI-generated mini apps, VibeOS, Google Search, and why durable software survives where state, trust, and depth matter.

How I use Codex, JetBrains IDEs, Copilot review, AGENTS.md, reusable skills, and CI checks to make AI coding reliable.

What current UK evidence says about AI exposure, adoption, entry-level work, and the roles most likely to change.

A developer-focused summary of the State of AI 2026 survey, covering adoption, coding agents, paid usage, costs, risks, and what engineering teams should take from it.