Building a Personal AI Factory

Summary

Building a Personal AI Factory (July 2025 snapshot) Published: July 1, 2025 Overview I keep several claude code windows open, each on its own git-worktree. o3 and sonnet 4 create plans, sonnet 3.7 or sonnet 4 execute the plan, and o3 checks the results against the original ask. Any issues found are fed back into the plan template and the code is regenerated. The factory improves itself. Read on to see what might be useful for you. Guiding Principle – Fix Inputs, Not Outputs When something goes wrong, I don’t hand-patch the generated code. I don’t argue with claude. Instead, I adjust the plan, the prompts, or the agent mix so the next run is correct by construction. If you know Factorio you know it’s all about building a factory that can produce itself. If not, picture a top-down sandbox where conveyor belts and machines endlessly craft parts because the factory must grow. Do the same thing with AI agents: build a factory of agents that can produce code, verify it, and improve themselves over time. Basic day to day workflow - building the factory My main interface is claude code. It’s my computer now. I also have a local mcp which runs Goose and o3. Goose only because I’ve already got it setup to use the models hosted in our Azure OpenAI subscription. Looking to improve this at some point, but it works for now. Step 1: Planning I’ll give a high level task to claude code, which calls over to o3 to generate a plan. o3 is a good planner and can ask a bunch of good questions to clarify the job to be done. I then have it write out a <task>-plan.md file with both my original ask and an implementation plan. Step 2: Execution First, sonnet 4 reads the plan, verifies it, and turns it into a task list. Next claude code execute the plan, either with sonnet 3.7 or sonnet 4 depending on the complexity of the task. Because most of my day-to-day is in clojure I tend to use sonnet 4 to get the parens right. One important instruction is to have claude write commits as it goes for eac...