AI & ML

This is the sixth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, part four here, and part five here. I think context management is one of the most important skills in AI-driven development, and it’s weird that compared to other AI-related topics, almost nobody talks about it. We […]

Ryan Carson has built companies for 25 years, including Treehouse, which taught over a million people to code. He knows what it takes to grow a team. So when he told me he’d raised $2 million in seed funding for his latest company, Untangle, an AI-powered divorce assistant, and had no plans to hire anyone,

I just sat in a room full of data engineers the other week who were worrying about AI automating them out of work the same way auto manufacturing in Detroit was upended half a century ago. All AI. All the time. That’s what technology professionals are talking about. Data scientists, data engineers, and data architects

I just sat in a room full of data engineers the other week who were worrying about AI automating them out of work the same way auto manufacturing in Detroit was upended half a century ago. All AI. All the time. That’s what technology professionals are talking about. Data scientists, data engineers, and data architects

Steve Yegge’s article about programmer burnout (“The AI Vampire”) along with Margaret Storey’s article about Cognitive Debt started an ongoing conversation about programmer fatigue and software quality—two topics that should be linked, but often aren’t. Steve argues that programming constantly with the help of agentic AI leds to burnout; it’s fast, it’s fun, but keeping

Steve Yegge’s article about programmer burnout (“The AI Vampire”) along with Margaret Storey’s article about Cognitive Debt started an ongoing conversation about programmer fatigue and software quality—two topics that should be linked, but often aren’t. Steve argues that programming constantly with the help of agentic AI leds to burnout; it’s fast, it’s fun, but keeping

Personal AI doesn’t have to run your life to change it. It just must see you clearly and feed your behavior back to you in a way you can’t dodge. Once you look at AI as feedback loops instead of little butlers, the whole “agent” conversation starts to feel upside down. We’ve overrotated on agents

Human-in-the-Loop becomes an operational bottleneck In my previous article, ”The Missing Layer in Agentic AI,” I argued that AI agents need a deterministic execution kernel—a privileged “Kernel Space” that validates every proposed action before it touches the real world. That article focused on what happens at the execution boundary: idempotency, JIT state verification, and DFID-correlated

Human-in-the-Loop becomes an operational bottleneck In my previous article, ”The Missing Layer in Agentic AI,” I argued that AI agents need a deterministic execution kernel—a privileged “Kernel Space” that validates every proposed action before it touches the real world. That article focused on what happens at the execution boundary: idempotency, JIT state verification, and DFID-correlated

As enterprise AI agent adoption scales, the absence of centralized, organization-level tool infrastructure is producing compounding costs. When adoption is built around optimizing for deployment speed, enterprises expose themselves to a combination of risks: duplicated engineering effort, security exposure, and operational opacity. Every enterprise needs its own shared tool registry, one that reflects its specific