I’ve started to become pretty skeptical of heavily scripted approaches to AI-assisted software development. The more I work with modern models, the less convinced I am that the future is a giant chain of tightly controlled agents passing work between each other like some kind of assembly line. Right now, I’m betting more on skills and on the model’s ability to recognize when those skills should be applied. By “skills,” I mean reusable bits of organizational knowledge. Things like how we modify U...
NextJS and Caching
The new cache model in Next.js is much more explicit than the older ISR/fetch-cache world. The important mental model is that The cache key is effectively composed from a few inputs: build ID, function/component identity, serialized arguments, closed-over serializable values, and sometimes dev/HMR state. So this: But if you have a One of the more subtle aspects of For example: And you use it like this: So even though Conceptually the cache key becomes something like: vs: That may seem obvio...
Integrating AI at Scale: A Practical Framework
One of the most common mistakes organizations make when approaching AI is starting from a place that is too broad to be actionable. Statements like “we need to integrate AI” carry the right level of urgency, but very little direction. They resemble earlier conversations about adopting the internet or moving to the cloud, where the technology itself became the focus rather than the problems it was meant to solve. AI is no different. It is not a strategy in itself, but a tool that can be applied i...
Creativity in the Third Golden Age of Computer Science
There's a notion that the To watch a machine produce reasonable answers to questions of all kinds as quickly as a slot machine spits out winnings is a disquieting thing. But my mind races to find the narrowing sliver of purpose that has always eluded me. If work is the thing you do to distract yourself from thinking about purpose, and work is being consumed by an automaton, there are fewer and fewer places to hide. I don't think there's a good answer to purpose yet - our minds aren't capable of...
Comprehension Debt
AI has dramatically accelerated how quickly we can write software. Entire features can appear in minutes. But this speed introduces a new risk that traditional engineering practices were never designed for. The real danger isn’t just technical debt. It’s As At the same time, The result is a dangerous feedback loop. Traditional technical debt accumulates slowly and heresy spreads. To manage this new reality, engineering teams need a stronger foundation for AI-driven systems. In an AI-assisted...