Notes on AI-augmented development, agent orchestration, and memory systems.
Building in public. If you try this and it breaks, it worked on my machine ¯\_(ツ)_/¯
Letting It Run: Toward an Autonomous Build Loop
Part 4 of Building My Agentic Workflow in Public. Part 3 gave parallel agents a shared contract so their work stayed coherent. That coherence is what let me finally take my hands off the wheel. By this point I had the pieces: a process, session handoffs, sub-agents, a contract, and an evaluator that could not cheat. What I wanted next was simple to say and hard to do. Hand the system a batch of features, walk away, and come back to something that is 85% to 90% done. ...
Read blog post ›When Agents Diverge: The Code Contract
Part 3 of Building My Agentic Workflow in Public. In Part 2 I pushed work onto parallel sub-agents to save context. This post is about the mess that created, and how I cleaned it up. Parallel sub-agents let me build faster. They also introduced a problem I did not see coming. Each agent built its piece reasonably. The pieces did not match each other. The divergence problem Picture three sub-agents, each handed one feature of the same epic. Each one sees the epic. None of them sees the others. So they each make their own small decisions: how to log, how to handle errors, what to name things, where shared helpers live, how their feature talks to the next one. Every one of those decisions is defensible on its own. Together they are a mess. ...
Read blog post ›Surviving the Context Window
Part 2 of Building My Agentic Workflow in Public. Part 1 turned my prompting into a repeatable process. This one is about the wall I hit right after: a single session is never big enough for real work. In Part 1 I turned the way I work into a repeatable workflow: an agile-style vocabulary of epics, features, tasks, and defects, with a command to drive each step. It made the work predictable. It didn’t solve the bigger problem, which showed up the moment the work got real. An LLM session has a memory limit, and serious work blows right past it. ...
Read blog post ›From Prompts to a Process: Making Claude Code Repeatable
This is Part 1 of Building My Agentic Workflow in Public, a series on how I actually work with Claude Code day to day. It starts with the problem from AI Fatigue: the tools got faster, my brain didn’t. This post is the first thing I did about it. For a while, every session with Claude started the same way. I re-explained the project. I re-explained how I like things named, where files go, what “done” means. The model was capable. I was the bottleneck, retyping the same context over and over. Then the session ended, and all of it died with it. ...
Read blog post ›AI Fatigue Is Real
Before ChatGPT, a work task had a shape I understood. Get the task, sit with it for two or three days, solve it, move on. One problem at a time. These days I can build in days what used to take me months, sometimes faster than feels reasonable. And after about four hours of it, I’m wiped. Not satisfied-tired. Wrung out, a little anxious, short with the people around me, brain running at a thousand miles an hour while I’m trying to power down. ...
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