AI

LLM is CPU. Context window is RAM. Andrej Karpathy captured something real with that analogy: the model processes, but the context is where information lives. What the model can reason about is bounded by what you put in that window, and how you structure it. In 2026, Gartner formally defined context engineering as a core … Read more

A few years ago, if you told a Java team that an AI tool could open a pull request, a lot of people would treat that as the finish line. To be honest, I never saw it that way. Opening a pull request is the easy part. The hard part is everything that comes after … Read more

A few days ago, I was reviewing what has actually changed in the Java plus AI ecosystem lately. Not the hype, but the pieces that help a team move from a nice demo to something they can support in a real environment. I will be honest, this is the part I care about most. Calling … Read more

Most Java teams do not need to switch to Python to integrate AI into their applications. If your stack already includes Spring Boot, Quarkus, or Micronaut, you have a clear path to add AI capabilities without rewriting your services. The ecosystem has matured to the point where you can treat model integration as just another … Read more

Artificial intelligence is now a production concern, not a lab experiment. Enterprise teams are no longer asking whether they should use AI. They are asking how to ship AI features that are secure, maintainable, cost-aware, and integrated with existing systems. This is exactly where Java has become a strategic advantage. For years, the mainstream AI … Read more