
Let AI Agents Handle the Refactoring Nobody Wants to Do
Migration tickets sit in backlogs forever because they're tedious, not because they're hard. That's exactly the work an agent should do.
Build notes from a solo engineer running autonomous agents that ship code while I sleep. Agent architecture, the cost and latency tradeoffs, local-first AI, and where the market is heading, with the real numbers and the dead ends, not just the wins.
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Migration tickets sit in backlogs forever because they're tedious, not because they're hard. That's exactly the work an agent should do.

Every team building AI inside a company hits the same wall: the model doesn't know who you are.

A model server knows its own last response time. It cannot tell you how deep the queue is, how often the box is thrashing between models, or what every other caller has been getting lately.

A dense 27B at 8-bit runs my local chat work at 15 tokens a second. Its 35B Mixture-of-Experts sibling runs the same prompts at 70.

The agent solved the problem and the theory stayed in the prompt. Notes on what gets left behind when the code is clean and the model in your head isn't.

Two labs fenced their best models behind US compliance in a single week. I build from outside the fence, and I'd already bet on models nobody can revoke.

For twenty years the web ran on a quiet handshake: free content, search traffic, ads to pay the bill. The handshake is coming apart because the reader is no longer a person. It is an agent.

The constraint used to be typing speed. Now it's how well you direct, review, and orchestrate. Notes on what one person can ship with the right agent setup.

On April 8, I shipped an autonomous dev pipeline. The same day, Anthropic announced Claude Managed Agents. Same problem, same time. One is the engine. The other is the self-driving car.

I moved my entire autonomous agent pipeline from OpenClaw to GitHub Actions and Claude Code CLI. Anthropic's subs decision was the catalyst, but portability and auditability were the real reasons.