Tired of treating AI like a black-box REST endpoint? What if you could own the stack: shape the tensors, steer memory, pick execution providers?

In this session, we make that shift. Today, with JDK 25, you can wire real models - LLMs, image classifiers, or object detection algorithms - straight from Java via the Foreign Function and Memory API to call native runtimes like ONNX for fast CPU/GPU inference. You will learn how to map tensor buffers to Java MemorySegment, how to flip execution providers, and have a self-contained Java application. Then will push further with Project Babylon’s Code Reflection: express model logic as Java code that Babylon can analyze and lower to accelerator backends, skipping external model files or the need for a glue language.

Build expressive and testable FFM-based inference today and author pure Java AI-ready models with Code Reflection tomorrow!