Last post I showed off the chainsaw I built myself — my build-autonomous loop, the padded room, the whole rig — and cut down a little tree with it: a Go CLI to boss around a smart plug. It was well worth building! I also think you should probably stop building your own. Yes, including me. Especially me.
People keep asking me how I actually work with Claude Code now. Not the “does AI coding work” question — I’ve beaten that horse into glue on this blog already — but the boring, practical, how-does-my-loop-actually-work question. So here it is. The whole rig. And fair warning: what I’m about to describe is a chainsaw, and most of the industry is still lined up at the axe-chopping contest.
I just spent a few days at a retreat on the future of software development. Small rooms. Unconference format. Some of the sharpest people in our industry - the kind of names that show up on the spines of the books on your shelf. I’m not going to tell you who was there or who said what. We ran the whole thing under Chatham House Rule: use what you learn, attribute none of it. This is my initial take-away.
Here’s a question I can’t stop chewing on: why is some software so damn good, and most of it so… not? Not buggy. Not slow. Just bad. Bad in a way you feel in your gut the moment you touch it. The answer isn’t tooling, or budget, or headcount. It’s taste. And I’m starting to think taste is the whole ballgame in the age of AI.
A couple weeks ago I wrote that your organization has to learn as fast as your best AI adopter. Externalize, combine, socialize, internalize. Run Nonaka’s spiral or get left behind. A bunch of you wrote back with the same question: okay, but where does the knowledge actually go?
Everyone is asking the wrong question. “How do we adopt AI agents?” “Which tool?” “What’s the right policy?” Wrong questions. The right question is: how does your team turn what one person learned on Tuesday into something everybody on the team uses by Friday?
Next week I get to watch one of my favorite things: see a room full of smart people handed a hard problem and watch them figure it out in real time. I’m headed to InfoComm to mentor, judge, and sit on a panel for an idea sprint about AI at the edge. And I could not be more excited.
A response to “LLMs are Eroding My Software Engineering Career and I Don’t Know What To Do” - and to everyone I keep hearing this same anguish from. You’re not wrong that the ground is moving. You’re wrong about which ground.
There’s a thing going around the engineering blogs and Hacker News right now: AI killed Agile, Waterfall is back, write your specs up front, welcome to “Waterfall 2.0.” You may have seen the Medium piece titled Agile Is Dead. AI Killed It. Welcome Back, Waterfall.. Hmmm.
And once I followed the logic far enough, I ran into a second question that nobody seems to want to answer out loud: if the agent writes the code, who actually reads it?
I’ve been dorking around with local AI coding agents for a while now. At some point curiosity got the better of me and I had to know: what the heck are these things actually doing under the hood?
A recent industry retreat of senior engineering practitioners published their findings on where software development is heading. Reading it felt like someone had been reading my mind — and the minds of the people I follow online — and synthesized it all into one document. When a lot of people arrive at the same conclusions independently, that’s signal worth paying attention to. Here’s my take on each major theme.
Look, I’m going to be direct here. If you are not using AI agentic coding methods right now - today - you are going too slow. Not “a little behind.” Not “maybe missing something.” Too damn slow.
A Scottish football manager once said, “Some people think football is more important than life. But they are wrong. It’s much more important than that.” When intelligence becomes a utility, the ability to ACT on it becomes everything. In today’s software world, Agency is more important than anything else!
The AI train has left the station. Some of us are on board, generating entire modules from specs. Others are still on the platform debating whether the train is even real. Four smart people wrote about this shift this past week. They all agree on what it means. And I agree with them.
If software changes something in the real world, it’s a robot.
Looking back at 2025, one theme dominates: AI agentic programming went from novelty to necessity. This year transformed how I think about software development, career advice, and even my personal hardware projects. Here’s what I wrote about, what I learned, and what I expect in 2026.
I don’t like the term “vibe coding.” I strongly prefer “AI-assisted software engineering.” But regardless of what you call it, Gene Kim and Steve Yegge have written a book about it - and their journey from skeptics to true believers is the most interesting part.
There’s a lot of folks who are in outright denial about AI agentic programming. They are wrong. AND… they are right. My thoughts.
There’s no way to really understand something unless you dive into first principles. This is especially true for AI coding agents. What is the editor doing under the hood? I try to peel the onion a bit.
Reading time: 10 minutes
It’s brutally hard to find a job as a new college grad today. And AI is changing things even for seasoned Engineers. Here’s my advice.
The evolution of AI-assisted programming has reached a pivotal moment, transforming from simple code completion to truly collaborative AI agents. This shift represents not just an improvement in tooling, but a fundamental change in how software is developed.