Writing on ai

Part three gave one agent a spec. A factory runs many of them on one line, and the idea is fifty years old. What a software factory is, why every era rebuilt one, when it fails, who runs them, and one real factory followed from a whiteboard to production.

Part two ended on a question nobody has solved: how do you know the agent understood what you meant? Spec driven development is the current answer. Where it comes from, who is pushing it, the five approaches worth knowing, and what to do with it today.

Part one ended with five things I want from any agentic harness. This is what each of the five actually costs to build, which part of each one fails quietly, and how to check whether the harness you are using has them.

A model that can write anything still cannot open a file, run a command, or know when to stop pretending it did. The first post in a series on what actually turns a language model into something you can safely put to work.

I grepped my own name in my own codebase and found fourteen hits, all of them in code comments. When I asked the agent that wrote them who they were for, it told me: itself.

The dead time while a coding agent works adds up to a real part of the day. I built wordwright, a terminal typing trainer that teaches grammar and writing in the gap, and this is how its interface is put together.

An experiment. Can an ordinary speech engine catch a single mispronounced sound in Russian and English, and can it hear prosody, the melody and rhythm that carry an accent?

Why letting an AI review its own code is marking its own homework, and how Greenlight enforces a TDD trust boundary between the agents that write tests and the agents that write code.