Most AI projects start with code. I started with documentation. Before implementing O-AI, I spent...
Most AI projects start with code.
I started with documentation.
Before implementing O-AI, I spent weeks writing a complete engineering handbook covering architecture, memory systems, multi-agent design, security, testing, deployment, debugging, governance, and long-term maintainability.
Why?
Because AI projects grow fast—and without clear engineering principles they become impossible to maintain.
I spent months optimizing my AI development pipeline before realizing the bottleneck was never the pipeline. It was my specs.…
🧠 1. The Uncomfortable Moment After a Productive AI Session 🧠 2. The Problem: AI Can Create...
Over the past few months I built an AI-assisted delivery framework — not to write code faster, but to...
Documentation is often the first product a developer experiences. Before they see your architecture,...
What if the biggest problem with AI coding assistants isn't the AI... but the way we write our...
Over the last year, I’ve reviewed about a dozen projects where AI was used heavily in development....
