AI for Java Developers: From Zero to Production in 5.5 Hours
Happy Monday and welcome to another edition of the newsletter. It feels like yesterday that we entered the month of June and just like that June is almost over. We are full swing into summer and I'm loving every single part of it.
This past weekend we had an opportunity to spend some time in the Columbus OH area for a wedding. I have never actually been to Dublin and I can say this is one of the most underrated cities in Ohio. Such a beautiful area with some really nice walkable areas for everyone.
Next week I'll be on a beach in my happy place on Hilton Head Island in South Carolina so I probably won't have any updates over the next couple of weeks. I think about you a lot and how lucky I am to be a part of this community and the opportunity I have to affect so many people. I hope you are having a great day. If you get a chance take a couple of minutes today try and think about what you are grateful for.
This week's newsletter is really exciting for me. I was able to wrap up and publish a course that has been a long time in the works. I'll also tell you about an MCP server I published and a conversation I had with Dan Dobrin on Spring Office Hours.
AI for Java Developers Course is now Live
I'm happy to announce that my latest course is now available for FREE on YouTube. I took everything that I have learned over the last 12-18 months and poured it into one of my most comprehensive courses yet.
I'm often asked with all the momentum in the Python space for AI, what does this mean for Java developers. If you want to get into Machine Learning & Data Science, Python is a great language to learn. But if all you want to do is consume these Large Language Models, you would be hard pressed to find a better place to do this than Java. Java is the language of the enterprise and integration with AI is now a requirement.
Here's what makes this course different: Instead of just showing you how to make REST calls to OpenAI, you'll learn how to solve the real problems that emerge when building production AI applications. We dive deep into prompt engineering, tackle LLM limitations head-on, and explore the Swiss Army knife of solutions that Spring AI provides.
🎯 What you'll learn:
AI Foundations & Spring AI Mastery
- AI fundamentals (ML, deep learning, LLMs) for Java developers
- Spring AI architecture and portable API design
- Prompt engineering techniques from basic to advanced
- Multimodal AI: text, image, and audio generation
Overcoming LLM Limitations
- Prompt guarding and stuffing strategies
- Retrieval Augmented Generation (RAG) with vector databases
- Function calling and tool integration
- Model Context Protocol (MCP) servers for reusable AI components
Production-Ready Development
- Local vs cloud models (cost optimization & security)
- Open source models with Ollama and Docker
- Observability with Prometheus and Grafana dashboards
- Testing strategies for deterministic and non-deterministic AI responses
- Chat memory and conversation management
Real-World Applications
- Building intelligent REST APIs and streaming responses
- Structured output for type-safe AI responses
- Multiple model integration in single applications
- Enterprise security and deployment considerations
This isn't just theory. Every concept includes hands-on coding with Spring Boot, so you'll walk away with practical skills you can implement immediately in your Java applications.
The best part? You'll build everything using the Java and Spring tools you already know and love. No need to learn Python or become a data scientist. This course bridges the gap between traditional Java development and the AI revolution.
From zero to building intelligent apps in 5.5+ hours of hands-on content. This isn't just another AI course. It's your complete roadmap to becoming an AI-powered Java developer who can ship real applications that matter.
Ready to level up your career and join the AI revolution as a Java developer?
Watch the full course below 👇🏻
Beehiiv MCP Server
While I was putting the Spring AI course together I thought it would be a good opportunity to talk about a real life use case for an MCP server I built. This newsletter that you are reading right now is thanks to a wonderful platform called Beehiiv.
I author my newsletter in Beehiiv, and then I publish it to my website. My website requires the content to be in Markdown but it also requires some special markdown blocks. I have images, YouTube embeds and Tweets all which require a specific format. In the past this was a very manual process. Now I use the Beehiiv MCP server to pull my latest newsletter, convert it to the specific markdown and then write the file and images in the correct location.
If you want to check out the source code for the MCP server you can find it here. An interesting feature of this repo is the native images. Not everyone who wants to use this MCP server will have Java installed or know how to use it the JAR. I added a GitHub action that will publish native images for Windows, MacOS, and Linux.
If you're a fellow Beehiiv author and find this useful feedback is welcome.
Spring Office Hours S4E17: Spring AI & Google Gemini Beyond the Demo
Key Takeaways
Model Selection Philosophy: Don't chase every new model release. Choose based on your specific use case - long context windows for document work, cost efficiency for high throughput, or performance for confined tasks.
Google's AI Ecosystem Simplified:
- Gemini.Google.com: Consumer chatbot
- AI Studio: Developer playground with advanced controls
- Vertex AI: Enterprise platform supporting Gemini + 10,000+ Hugging Face models
Major Technical Highlights
Cost Optimization
Google's implicit context caching automatically reduces costs when reusing documents across queries. Gemini Flash costs just $0.10/$0.40 per million tokens vs competitors at $75/$150.
MCP vs A2A Standards
- MCP: Connects agents to internal tools/APIs (great for controlled environments)
- A2A: Enables secure agent-to-agent communication across organizations
- Big news: Google donated A2A to Linux Foundation today, making it an open standard
Enterprise Reality Check
Java developers ARE AI developers. No need to re-platform to Python. The new unified GenAI SDK provides GA Java support with Spring Boot integration and native image compatibility.
What's New
Agent Development Kit (ADK): Open-source Java framework for building multi-agent workflows with Spring Boot integration and visual debugging.
Serverless AI: Deploy Gemma 3 on Cloud Run for pay-per-use pricing and sub-second startup times - perfect for private document processing without sharing data externally.
Bottom Line
We're shifting from AI experimentation to production implementation. Java developers have everything needed to build robust, cost-effective AI applications today without abandoning existing infrastructure.
Watch the full replay below for live demonstrations and deeper technical discussions.
This week on Spring Office Hours I sit down with Craig Walls as we do a little AI show and tell and chat about Craig's upcoming book, Spring AI in Action.
TWEETS
I spent some time last week exploring the Spring AI codebase and attempting to add a new feature.
Google launched it's new Gemini CLI which is open source!
I have found myself using Claude Code more than any other tool out there and I really enjoyed this conversation between Riley and Micky.
UNTIL NEXT WEEK
I hope you enjoyed this newsletter installment, and I will talk to you in the next one. If you have any questions for me or topics you would like me to cover please feel free to reply to this email or reach out to me on Twitter.
Happy Coding,
Dan Vega
https://www.danvega.dev