Developer, builder, AI-native. Thoughts on code and shipping software.
Lincoln staffed his cabinet with the people who wanted his job. The A(i)-Team plugin staffs its pipeline with agents whose only job is to reject bad work, capped so nothing loops forever.
A `claude -p` call bills for its own furniture before your prompt even loads, and the fix that buys 4.4x on a quick call turns out to be almost worthless on a long agentic run, because they're two different taxes.
An engineering director's honest story of failed AI adoption: stalled workflows, review bottlenecks, and how to build an AI workflow your dev team will actually use.
TDD with AI agents sounded like the right call. Then I got 3,400 tests, half of them testing JSON files and config. Here's what I learned fixing it.
MCP servers burn your most valuable resource: context. I replaced mine with skills and CLI tools. Same capabilities, a fraction of the overhead.
I was vibe coding with Claude for months. Then I started treating it like a real teammate — with PRDs, scoped tasks, and standing context. Everything changed.
A one-line bash script called Ralph Wiggum fixed context rot by restarting Claude in a loop. Then Anthropic broke it. That failure led me to subagents and changed how I build software.
Your AI starts brilliant and ends useless. It's called context rot — LLM performance degrades as context grows. Here's why it happens and how context engineering fixes it.
How I turned 30+ scattered Claude chats into a 500-file markdown system that runs my entire life - in one month.
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