It’s been about 8 months since my last update on neovim4j, and the config has evolved significantly. The name “neovim4j” is now a bit of a misnomer—while it started as a Java-focused setup, it’s grown into a polyglot development environment.

Rust Support 🦀

The biggest addition is comprehensive Rust support. I’ve integrated:

• rustaceanvim for advanced LSP features powered by rust-analyzer
• crates.nvim for smart Cargo.toml management and dependency completion
• codelldb debugger integration
• neotest for running Rust tests directly in the editor

The Rust setup mirrors the Java tooling quality—full LSP, debugging, and testing all working seamlessly. Semantic highlighting is disabled in favor of Treesitter for more colorful syntax highlighting.

I had the pleasure to present about building Java applications using LLMs together with Bazlur at GeeCon 2025. The weather was amazing and Krakow is a beautiful historical city.

Key Topics Covered

Here are the key topics from the video with direct links to those sections:

• LangChain4j Basics: An introduction to the framework, demonstrating how it abstracts communication with various LLMs like OpenAI and Gemini using builder patterns.
• Prompt Engineering: The speakers explain the difference between System Prompts (defining the AI’s behavior/personality) and User Prompts (the specific query).
• AI Services & Streaming: A look at how to create high-level interfaces for AI interactions, including streaming responses for real-time chat experiences.
• Memory Management: How to provide LLMs with context from previous conversations using providers like MessageWindowChatMemory and storing history in databases.
• Tools (Function Calling): A deep dive into how LLMs can trigger Java methods to perform specific tasks, such as fetching web content or compiling Java code.
• Jakarta EE Project Generator: A demonstration of using an LLM tool to generate a complete Jakarta EE project structure via a chat interface.
• Retrieval-Augmented Generation (RAG): Using PGVector and embedding models to store and retrieve private data efficiently.
• Chunking and Tokenization: The importance of segmenting data so the AI receives the right context without exceeding token limits.
• Model Context Protocol (MCP): An introduction to the standard for connecting AI models to external data sources and tools.
• Q&A Session: Discussions on prompt injection, guardrails, and testing non-deterministic AI outputs.

Next up we are both busy building a workshop about Langchain4j and its integration with Spring. If you are interested in learning more join us at JNation.pt. Bring your laptop the session will be 180 minutes and lots to code about ;)

There is always a distinct thrill in learning something new and immediately putting it to the test. My journey with Model Context Protocol (MCP) servers began with a basic ‘books API’ demo, but I quickly wanted to take it a step further and build something with real-world utility. Since I enjoy working with Keycloak, I thought: Why not create an MCP server for it?

The vision was simple: enable developers to just ‘chat’ with Keycloak. There are so many standard tasks—setting up new users, groups, clients, and browser workflows—that could be streamlined through conversation. For those unfamiliar, Keycloak is an open-source identity and access management solution. I released the first experimental version this past summer, and since then, the wave of constructive community feedback has been incredible. That momentum is exactly what gets me excited to keep building.