I write about AI the way I use it at work: as a tool that has to earn its place, not a headline to repeat. Most of what crosses my desk is a claim in search of a use case. My job, on this page and in every article filed under it, is to sort the parts that change how a team actually ships from the parts that are just noise dressed up as innovation.

That means I read the books practitioners are recommending to each other and tell you plainly whether they hold up once you try to apply them, not just whether they were well marketed. It means I track the AI news that actually moves the ground under a delivery team, agent frameworks, model releases, policy shifts, and I skip the stories that are AI in name only. And it means I stay honest about the gap between a slick demo and a system your team can run, monitor, and trust on a Tuesday afternoon when something breaks.

If you build with AI, manage people who do, or just need to make a real decision about adopting it, this is the shelf where I keep what I have actually learned. New pieces land here as I finish them.