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AI-Augmented Development

The Faster Pencil

AI does not remove the hard part of any job. It moves it — and makes it harder to ignore.

Based on a conversation between two software developers, March 2026.

Two developers were talking late one night about what AI had actually changed in their work. They had both been using it for years. They were good at it. And what they kept coming back to was something that surprised them: the more capable the tool got, the more it demanded of them — not less.

This essay is built on that conversation. But the idea they landed on has nothing to do with software. It applies to any job where thinking is the work.

Peter Naur Was Right in 1985, and AI Just Proved It

In 1985, the Danish computer scientist Peter Naur published a short paper called "Programming as Theory Building." His argument was simple and radical: a program is not its source code. A program is a theory — a coherent mental model of what the system does, how its parts relate to each other, and why it was built the way it was. The source code is a byproduct of that theory. A trace. Not the thing itself.

I haven't typed apt install in three months

Last Tuesday I needed a file-watch service on my workstation. The kind of thing that monitors a directory and triggers a reindex when something changes. Normally that means ten minutes of reading systemd docs I've read fifty times before, copying a unit file from somewhere, adjusting paths, running systemctl --user enable, checking journalctl for the inevitable typo in ExecStart.

Instead I described what I wanted. Claude Code found an existing service on the machine, used it as a template, wrote the unit file, enabled it, started it, checked the logs, confirmed it was running, and updated the knowledge manifest so future sessions know the service exists. I approved three actions. The whole thing took about ninety seconds.

I haven't typed apt install in three months. Not because I stopped installing software — I install more than ever. But the person typing the commands isn't me anymore.

The Code Was Never the Moat

Bruce Perens says the entire economics of software development are dead. He said this in response to a story about a developer using Claude to rewrite a Python library from LGPL to MIT in a few hours. 130 million monthly downloads. 1.3% textual similarity to the original. A clean room implementation, or close enough that the legal distinction barely matters anymore. The whole thing took roughly five days.

Perens is half right. The economics of code as artifact are dying. The economics of knowing what to build are stronger than ever.

Skill-Driven Development vs Spec-Driven Development

Most teams using AI for development have settled on a workflow that looks roughly like this: write a detailed specification, feed it to the agent, review the output, iterate. It is disciplined. It is responsible. It works. And after six months of watching it in practice, I believe it has a structural limitation that becomes more expensive the longer you use it.

The limitation is not quality. Spec-driven development produces good output. The limitation is that every session starts from zero. The spec carries the knowledge. The agent carries nothing.

kcp-commands: Save 33% of Claude Code's Context Window

Every token Claude Code's context window can hold is an opportunity — a tool call result that stays in scope, a file that does not need to be re-read, a decision that does not need to be recapped. Wasting those tokens on noise is a quiet tax on every session.

Today we are releasing kcp-commands: a Claude Code hook that recovers 33.7% of a 200K context window in a typical agentic coding session by intercepting Bash tool calls at two critical points.

The full number across our benchmark session: 67,352 tokens saved.

Update (March 3, 2026 — v0.9.0): kcp-commands now writes a JSON event to ~/.kcp/events.jsonl on every Phase A Bash hook call. kcp-memory v0.4.0 ingests that stream to provide tool-level episodic memory — kcp-memory events search "kubectl apply" returns every time Claude ran that command across all your projects. Phase A gives Claude vocabulary. Phase B cleans output. Phase C remembers what ran.