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AI-Augmented Development

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.

Working Memory, Episodic Memory, Semantic Memory. Your Agent Has One.

Every session starts from zero. The agent cannot remember the decision it helped you make last Tuesday, the bug it spent three hours debugging last week, or the architectural pattern you established last month. This is not a model capability problem. It is a memory architecture problem — and it has a tractable solution.

The AI-Augmented Consultant: Knowledge Infrastructure Before Deliverables

A brief arrives on Friday afternoon. A compliance startup is building an AI-powered scoring engine. They have a working architecture. What they do not have are settled positions on five hard architectural questions. The deadline is Monday.

The question I asked myself was not "how do I answer these five questions?" It was "what knowledge infrastructure do I need to answer them well?"

That reframing is the entire methodology in one sentence.