Kevin is often featured as a (keynote) speaker at conferences around the world, where he shares his passion and knowledge about developer experience, open source, cloud native development and Java. He is also an author, java Champion, and an accomplished software architect and platform engineer. Kevin currently works as a Senior Principal Developer Advocate at Red Hat.
Kevin contributes when he can to projects like Quarkus, Knative, Apache Camel, and Podman (Desktop). He’s also an organizing member of the Belgian CNCF and the Belgian Java User Group.
Multilingual and multicultural, Kevin speaks English, Dutch, French, and Italian fluently. Currently based in Belgium, he has also lived in Italy and the USA.
When he’s not coding or speaking at conferences, you’ll likely find Kevin exploring the great outdoors—whether he's hiking rugged trails, gravel biking through scenic routes, snowboarding down mountain slopes, or packrafting on untamed waters.
Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” Java application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?
Fortunately, we can actually infuse AI models built by actual AI experts into our applications in a fairly straightforward way, thanks to some new projects out there. We promise it’s not as complicated as you might think! Thanks to the ease of use and superb developer experience of Quarkus and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy 🙂
In this session, you’ll explore a variety of AI capabilities. We’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll get our hands dirty with some code and exploring LangChain4j features such as prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); and discovering ways to run (and train) models locally using tools like Ollama and/or Podman AI Lab. In addition, we’ll take a look at observability and fault tolerance of the AI integration and compile the app to a native binary. Maybe we’ll even try some new features, such as generating images or audio!
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