Coding With David

There’s a moment that caught me off guard recently. Not a breakthrough in code. Not a clever refactor. Not even a production issue solved at 2 AM. It was something simpler—and much bigger. I realized I had fully accepted AI-driven development. The Workflow That Changed Everything I built a system that feels almost unreal when you step back and look at it. Here’s what happens: A bug or feature is created in Azure DevOps An application polls for new work When it finds an item, it: Creates a branch Writes or updates the code Generates tests Runs those tests Updates documentation Runs a separate agent for code review (with a report) If everything passes… creates a PR All of this is powered by a combination of: Agents Skills Prompts Tools In other words… a small army of AI doing what used to take hours (or days). The Unexpected Realization After watching this system run, I leaned back and had a surprising thought: “I can focus on being a so...

  Why AI looks brilliant in demos, struggles in production, and delivers real value only when teams build the right operating system around it. If you have watched AI coding demos lately, you have probably seen something that looks almost unbelievable. A model spins up a feature in minutes. It creates a clean UI. It wires up some logic. It even explains itself confidently. The whole thing feels smooth, fast, and oddly effortless. Then you try to use the same approach on a real production application, and the experience changes immediately. Suddenly, the AI misses conventions, breaks patterns, invents abstractions, touches files it should not touch, and produces code that looks polished but does not really belong in your system. So what happened? The demos were not fake. They were just operating under kinder conditions. The hidden reason demos look so good AI tends to look brilliant in environments with very few constraints. A greenfield prototype, a standalone script, or ...