Doing is the new Strategy.
Last Friday, Linus and Luke titled the WAN Show "Software Is Over." They were half-joking. The story underneath it should bother every software executive more than it apparently does. In the span of a few weeks, two different people rebuilt Photoshop. Separately. Neither one knew the other
Read this post →The best study we have says AI made experienced developers slower — and they couldn't feel it. Speed was never the leverage. Here's what the teams winning with AI actually do differently.
Everyone has the same AI tools. The people winning pair curiosity with the discipline to turn a question into a measured result — and back into the next question.
The build/buy/rent triangle had a hidden fourth variable: the cost of production. It just collapsed, and at scale you can now pay a team to build and own the software for less than the license costs.
The most capable AI model on the planet got pulled offline by three words hidden in a payload. That's a confused deputy attack, and we've been fighting it under different names for thirty years.
I moved a working local build into a CI/CD pipeline to test my own thesis. Every failure was an artifact of the pipeline, not the code.
No cloud APIs, no monthly fees. A fully automated pipeline from Ghost post to voice-cloned audio narration, running on a used RX 580 in an LXC container.
Constraints never disappear, they just shift where they show up. AI has torn down where we used to expect them, and where they're showing up may be the difference between teams succeeding or merely keeping up.
AI tools amplify whatever you bring to the table. The eight thousand startups rebuilding their apps right now brought the wrong thing.
A campsite rule that became a software principle that works as a career strategy. Three layers: deliver, optimize, improve.
Government agencies are spending years deploying yesterday's AI tools while agentic development compresses delivery from months to days. The gap isn't linear anymore. It's exponential.
The social construct is fraying. The principle is thriving. The distinction between the two is the only thing that matters.
Organizations optimize for the risk of change while silently accruing the far greater risk of irrelevance. The practitioners solved this decades ago. The structure just won't listen.
WebAssembly delivers on its promise, but the promise is more specific than the marketing suggests. The advantage isn't raw speed — it's predictable performance in the face of computational unpredictability.
The centralized CI/CD pipeline was the right bridge for its era. Containers, powerful workstations, and agentic tooling put us on the other side.
The companies pulling back aren't proving AI doesn't work. They're proving that vanity metrics and unlimited budgets are a bad combination regardless of the technology.
A person without a position is a person without a signal. And no signal means no relevance.
The playbook for building great AI systems is the same one we've ignored for human teams since 2009.
Anthropic announced recently that their new Mythos model is too dangerous to release to the public. It can find vulnerabilities and exploit them at a rate never before seen. And while many of us are taking this news with a grain of salt since all the AI companies are in a hype race as much as they’r
AI continues to consume all the oxygen in the room. We’ve gone from surprise to memes to denial with bouts of anger and sadness. And maybe just a touch of nihilism as we watch the multi-trillion dollar companies in this space maneuver, collapse entire consumer ecosystems, and appear to be rushing fa