Lize Raes loves helping developers build real-world applications with AI and agents. As Developer Advocate for Java + AI at Oracle and collaborator at LangChain4j, she’s passionate about making cutting-edge tech accessible and useful for developers. Her path has taken her from cochlear implant research to advising the Belgian government during COVID-19, to bioinformatics for drug development. Lize likes her projects hands-on and slightly unconventional, especially when they help make the world a bit better. Outside of work, you’ll find her behind the piano or in her woodworking atelier.
Modern software increasingly interacts with AI models, query languages, and other specialized programming models, forcing you to switch contexts, embed foreign code, or write verbose wrappers to bridge the gap. What if you could express logic from outside the JVM directly in Java?
In this session, we start by looking at how Java enables you to safely interact with runtimes outside of the JVM using the Foreign Function and Memory API and tools like jextract.
Then will push further with Project Babylon’s code reflection: express AI models as Java programs that Babylon can analyze, transform and lower to inference engines, thus leveraging hardware accelerator backends while skipping external model files or the need to glue code.
Along the way, you will learn how to build expressive FFM-based inference today and author pure Java AI-ready models with Code Reflection tomorrow!
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