Articles on code craft, architecture, and the human judgment that separates working software from software that lasts.
What working with me looks like when we build features in a Rails app with AI: spec, tests as a readable definition of done, "go" lock gate, implement, verify. The development loop for non-technical founders.
A 90-minute guide from a fresh Mac to a production-grade Rails app, generated by Claude Code. No development experience required.
ThoughtWorks identified a new category of engineering work between coding and delivery. Nobody has named it yet. I've been doing it.
Ruby is the fastest, cheapest, and most token-efficient language for AI coding agents. Here's the data.
Constraints as understanding — why working within limits produces the deepest insight.
Signal over noise — why the domain model matters more than the library layer.
Strategic sacrifice — accepting complexity now for positional advantage later.
Embodied mastery vs. pattern-matching — AI as collaborator, not replacement.
Technical debt as physical decay — and the energy required to reverse it.
Why every codebase drifts toward disorder — and what to do about it.
Trained perception — what reduced listening teaches you about reading code.