I build AI solutions for engineers.
Why filesystem-first context architectures work, where they break down, and how the same idea can be implemented with structured object spaces using small, composable primitives instead of bespoke tools.
As AI systems begin to consume documentation at runtime, documentation shifts from external reference to operational input. This article explains why in this setting runtime-coupled documentation becomes unavoidable, even more so for on-prem and regulated deployments, and why this is an architectural boundary, not a tooling choice.
In domains where every letter, number, and logic gate matters, "close enough" in semantic similarity can be dangerously wrong. This article shows why vector search often fails on engineering content and describes a hybrid, structured approach that handles technical detail far more reliably.
Using AI for software engineering and writing doesn't simplify my job - instead I move quicker, handle more decisions, and deal with more moving parts. As a result, work becomes more intense, and I cover more ground and learn more than ever before.