$ ls ~/talks
Hi, I'm Dan. I help developers make sense of Spring, modern Java, and AI live on stage.
I've given 100+ talks in 34 cities, from SpringOne and Spring I/O keynotes to local Java User Groups. Every talk gets live code, honest opinions, and something you can build the same afternoon.
Speaking since 2002 · abstracts · past talks
Where you can catch me next
Porto Tech Hub Conference 2026 · Porto, Portugal
Fundamentals of Software Engineering in the Age of AI (Opening Keynote)
Every few years, something comes along that's supposed to end software engineering as we know it. IDE features that write code for you. Low-code. No-code. Now AI. And every time, the role adapts, the fundamentals stay, and the people who understand the craft come out ahead. Still, the anxiety this time around is real. The headlines say software engineering is dead. Your group chat says juniors are cooked. Your CEO just asked why the team isn't shipping 10x faster. Take a breath, because nobody knows where we are heading, but it's worth talking about honestly. Here's something you've probably noticed. AI is amazing at the things you don't know. Ask it about a language you've never touched or a framework you're just picking up, and it looks like magic. Then you ask it about the code you've lived in for five years, and suddenly you're catching mistakes on every other line. That's not a bug in the tool. That's the fundamentals talking. AI tools are genuinely powerful, and they reward the developers who already understand what good software looks like. Naming things well, knowing when to refactor, reading code critically, designing systems that hold up. These fundamentals matter more now, not less, because they're what let you tell when the output is wrong. Whether you write code, ship it, or plan the work around it, Dan Vega and Nate Schutta will cut through the noise around AI coding tools: where they help, where they hurt, and why the fundamentals are your biggest advantage right now. You'll leave knowing what to invest in, what to keep learning, and why the craft still matters.
J-Fall 2026 · Ede, Netherlands
Spring Cleaning: What Boot 4 Lets You Delete
It is now more important than ever to stay up to date and patched on open source software. If you are upgrading to Spring Boot 4 there's a decent chance you're maintaining three or four things that are now unnecessary, and nobody on your team told you. This is a tour of the release disguised as a demolition. Built-in resilience, so Spring Retry goes. HTTP interface clients, so Feign goes. First-class API versioning, so that filter someone wrote in 2021 goes. RestTestClient, so a pile of test scaffolding goes. Along the way we'll hit the additions you can't delete your way into: JSpecify null safety, Jackson 3, modular auto-configuration and the features it quietly turns off, plus the smaller changes that are easy to miss and quietly useful. You'll leave with fewer dependencies to patch, less code to maintain, and a working plan for the upgrade.
QCon San Francisco 2026 · San Francisco, CA
AI for Java Developers
AI has become an unavoidable topic in software development, and many Java developers are wondering if they need to learn Python to participate. The answer is no. The Java ecosystem has everything you need to build modern AI applications, and your existing skills carry over more than you might think. This workshop is the hands on version of that idea. Bring a laptop with Java and your IDE of choice. You'll spend three hours coding alongside me and end the session with a working AI powered Spring Boot application you understand top to bottom. We'll build it one feature at a time. Model abstraction, so your code runs against OpenAI, Anthropic, or a local model on your laptop with a single config change. Prompt templates for cleaner, reusable prompts. Structured output that turns LLM responses into typed Java objects you can use in your domain. Tool calling that lets the model fetch live data and trigger real actions in your application. And the Model Context Protocol, where you'll build a working MCP server that exposes your application's capabilities to any MCP compatible AI client. You don't need prior AI experience. Just comfort with Spring Boot and a willingness to write code alongside the room. You'll leave with a working repository, a clear mental model of how the pieces fit together, and the confidence to bring AI capabilities back to your own projects.
QCon San Francisco 2026 · San Francisco, CA
The Fundamentals of Software Engineering in the Age of AI
Every few years, something comes along that's supposed to end software engineering as we know it. IDE features that write code for you. Low-code. No-code. Now AI. And every time, the role adapts, the fundamentals stay, and the people who understand the craft come out ahead. Still, the anxiety this time around is real. The headlines say software engineering is dead. Your group chat says juniors are cooked. Your CEO just asked why the team isn't shipping 10x faster. Take a breath, because nobody knows where we are heading, but it's worth talking about honestly. Here's something you've probably noticed. AI is amazing at the things you don't know. Ask it about a language you've never touched or a framework you're just picking up, and it looks like magic. Then you ask it about the code you've lived in for five years, and suddenly you're catching mistakes on every other line. That's not a bug in the tool. That's the fundamentals talking. AI tools are genuinely powerful, and they reward the developers who already understand what good software looks like. Naming things well, knowing when to refactor, reading code critically, designing systems that hold up. These fundamentals matter more now, not less, because they're what let you tell when the output is wrong. Whether you write code, ship it, or plan the work around it, Dan Vega and Nate Schutta will cut through the noise around AI coding tools: where they help, where they hurt, and why the fundamentals are your biggest advantage right now. You'll leave knowing what to invest in, what to keep learning, and why the craft still matters.
CodeMash 2027 · Sandusky, OH
Spring AI in Practice: A Hands-On Workshop for Java Developers
Building an AI demo is easy. Understanding the patterns behind a real AI application is harder. In this workshop, we'll live code our way through the core capabilities of Spring AI, building each feature from scratch and seeing how it fits into a modern Java application. Bring a laptop with Java and your IDE of choice. You'll spend four hours writing code alongside me, working through the core features of Spring AI with hands-on examples you can take home and continue exploring. We'll tackle one capability at a time. We'll start with Spring AI's model abstraction and see how the same code can work with OpenAI, Anthropic, or a local model with minimal configuration changes. Then we'll use prompt templates to create reusable prompts and structured output to turn LLM responses into typed Java objects. From there, we'll add tool calling so models can access live data and invoke Java code. We'll finish with the Model Context Protocol by building an MCP server and exposing capabilities to MCP-compatible AI clients. You don't need any prior AI experience. Just comfort with Spring Boot and a willingness to write code alongside the room. You'll leave with the complete workshop repository, a clear mental model of how these capabilities fit together, and practical examples you can use in your own projects.
Jfokus 2027 · Stockholm, Sweden
Spring AI in Practice: A Hands-On Workshop for Java Developers
Building an AI demo is easy. Understanding the patterns behind a real AI application is harder. In this workshop, we'll live code our way through the core capabilities of Spring AI, building each feature from scratch and seeing how it fits into a modern Java application. Bring a laptop with Java and your IDE of choice. You'll spend the workshop writing code alongside me, working through practical examples you can take home and continue exploring. We'll start with Spring AI's model abstraction and see how the same application can work with OpenAI, Anthropic, or a local model with minimal configuration changes. From there, we'll explore prompts and structured output, turning model responses into typed Java objects your application can actually use. Then we'll move beyond simple prompts and look at context engineering: how to give a model the right instructions, data, conversation history, and capabilities at the right time. We'll use tool calling to connect models to Java code and live data, and explore the patterns that help AI applications become more useful, reliable, and maintainable. Along the way, we'll build each capability into the same application so you can see how the pieces fit together rather than treating them as isolated demos. You don't need any prior AI experience. Just comfort with Spring Boot and a willingness to write code alongside the room. You'll leave with the complete workshop repository, a clear mental model for building AI applications with Spring AI, and practical examples you can continue experimenting with after the workshop.