Thoughts, stories and ideas.
I’ve been using an AI orchestration framework I built called Mozart . You can check it out here: https://github.com/jstuart0/mozart-orchestration The idea behind Mozart is pretty simple. Instead of asking one AI to build an entire feature, it coordinates a team of specialized agents. Each
Most AI agent orchestrators fail in the same predictable way. They throw every persona at every problem. You get planning, coding, security review, UX critique, infrastructure checks, and validation all at once, whether the task needs it or not. It sounds thorough on paper, but in practice it is expensive,
A few years ago I could keep up with the pull requests my team shipped in a week. Not skim. Actually read. Follow the logic, notice the shortcuts, remember a month later where the interesting bits lived when I needed them again. I can't do that anymore. Not
If you work with AI coding agents long enough, you run into the same problem: the agents are productive, but the workflow around them gets chaotic. One Claude Code session is writing tests. Another is refactoring something risky. A Codex session is halfway through a change you barely remember prompting.
About six years ago I was in the middle of a surprisingly heated debate on a team. The question sounded simple: If something breaks in production, what should you keep so you can reproduce the issue? Specifically, we were talking about containers. Should we keep the Dockerfile and Helm charts
About seven or eight years ago, I was leading a development team when someone introduced me to a concept I hadn’t encountered before: mob programming. The idea sounded almost counter-intuitive. Instead of developers working individually—or even in pairs—the entire team works together on
It started with a problem every developer knows I was deep into a side project, using AI to write code faster than I ever had before. Claude, GPT, Copilot — the code was flowing. But there was a catch. Every time the AI changed something, I had to test it.
There’s a quiet shift happening in software engineering that most teams haven’t fully acknowledged yet. AI isn’t just helping us write code. It’s changing our relationship to understanding it. And when something goes wrong, we tend to blame the wrong thing. We
How a weekend experiment turned into a 30-service AI system that controls my entire home — with zero cloud dependencies. When I told Alexa to turn off my office lights for the thousandth time and watched it route my voice through Amazon's servers, process it in some
Two weeks ago, Alexey Grigorev — founder of DataTalks.Club and someone who teaches over 100,000 engineers how to build production AI systems — watched Claude Code run terraform destroy on his production infrastructure. His database, his automated snapshots, 2.5 years of student submissions — gone in seconds.
AI coding tools are incredible. They also make it dangerously easy to build systems no one fully understands. I’ve caught myself shipping features in hours that later took weeks to untangle. The problem isn’t the tools. It’s the speed. When code can be written