Genesis Keynote by Stephan Janssen

AI and large language models are becoming increasingly prominent in Java development, with many new tools and frameworks emerging

Java developers can now run LLM inference locally using projects like JLama, without relying on cloud services or Python

The Java ecosystem is evolving to support AI workloads while maintaining compatibility and stability through projects like GraalVM and native compilation

Developer productivity has increased significantly with AI assistance, though human intelligence and oversight remain essential

New tools like DevOps Genie and LangChain4J enable Java developers to integrate AI capabilities directly into their workflows

Privacy and data security concerns are driving development of local/private LLM deployment options for enterprise use

The Java community is actively working on bridging gaps between Java and AI technologies through various open source projects

Modern Java development involves using AI as a “thought partner” while still maintaining control over code quality and architecture

The six-month Java release cadence has helped the platform evolve and adapt to support new AI/ML workloads

There’s a growing need for Java libraries and tools that make AI capabilities more accessible to developers while preserving Java’s strengths