Category:
AI and knowledge work
A reading list for AI-augmented development
A small, opinionated reading list for engineers who want to go deeper into AI-augmented development. Annotated with personal notes on why each one matters and where to start. Not exhaustive — just the things I'd recommend to someone asking me where to begin.
The tools we build shape how we think
I started building AI tooling expecting it to make me faster. It did. What I didn't expect was that, three months in, I'd be approaching unfamiliar problems with a different mental shape than I had before. The tools changed the work, and the changed work changed how I think about work. That second loop is the more interesting one.
What I haven't learned yet
After writing a lot of confident-sounding posts about AI tooling, here's the inventory of what I'm still confused about. MCP servers I haven't really used. LangGraph patterns I copied without fully understanding. Prompt engineering subtleties I keep getting wrong. If you know any of this better than I do, I want to learn from you.
The compound effect of AI tooling
Each piece — rules, skills, subagents, journaling — is small on its own. The interesting thing is what happens when they layer. The system stops feeling like a collection of tools and starts feeling like an environment that was shaped to fit me. That shift is the compound effect, and it's the part nobody talks about.
AI-powered engineering workflows
An AI assistant doesn't replace the daily capture/plan/report cycle most working engineers run. It changes the friction. The work that used to take an hour at the end of the day takes five minutes. What AI does well, and what still needs human judgment, are different in ways worth naming explicitly.
Planning 22 blog posts with an AI agent
I sat down with an AI coding agent to plan a 22-post series. I didn't expect the planning itself to be the most interesting part — but it was, and most of what I learned is about how to use these tools, not about what to write.
From writing code to designing intelligence
AI at work is often framed as a productivity hack or a risk — a convenient reduction of something structural. Here’s a sharper read on what’s changing, what broke when I pushed into unfamiliar territory, and why human validation still drew the line.