Backend | Java | Quarkus | AI | Career
A few years back, the question “compile to native or stay on the JVM?” had a short answer: it depended on whether you needed sub-100ms cold start as part of the SLA. If you did, you went GraalVM Native Image and paid the price in reflection metadata, dynamic class loading limitations, and tooling that was … Read more
A lot of AI coding demos look impressive for about five minutes. Then reality shows up. Reality is the old service nobody wants to touch on a Friday afternoon. Reality is the module with naming debt, historic compromises, and a commit history that reads like a crime scene. Reality is the enterprise Java codebase that … Read more
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