AI Coding & Cloud

Please take a moment to forward this information to co-workers, friends, and social networks. Be sure to register.

This meetup is co-scheduled with AI Coding & Cloud, Boulder Java Users Group, and Denver Java Users Group.

See location pictures below.

5:30-6:00: Food, Soda, and Networking

6:00-6:15: Announcements & Raffles!

6:15-7:30: AI Semantic Layer (Critical Infrastructure)

Gartner just named the semantic layer a non-negotiable foundation for AI. Almost nobody in the industry knows what that means yet, or why it should worry them.

Every LLM response is a guess, not a fact. Most of the time it's close enough that nobody notices. Then it's wrong, and there was no way to see it coming, because the model doesn't know what your data means—the model doesn't know anything. It's always guessing, based on patterns, every time. The guess is right often enough to feel like magic. It will never be right 100% of the time, and can't change that. It's not a bug waiting on a fix. It's a hole in your architecture.

As one recent paper on the semantic gap puts it:

> “When AI systems enter the picture, descriptive documentation is insufficient. LLM-based interfaces operate directly over data representation. If meaning is not encoded structurally, the model infers it probabilistically.”
Your systems are full of structured data (JSON, databases, APIs) that means something to the code that built it and nothing to anything else, including the AI you just pointed at it. A field called status: 3 means nothing outside your system. Multiply that by every field, every service, every team, and you get an enterprise fluent in nothing but itself. That's the wall every serious AI initiative eventually hits, and it's why so many AI initiatives are underdelivering.

The fix isn't a bigger model or a new platform. It's a set of open standards that have quietly run major parts of the web for decades, built for making data mean something on its own, without a person or a program standing by to translate it. Once your data can say what it is, your AI stops guessing about it. Data-driven interactions become deterministic-first, your entire enterprise data landscape becomes more powerful, your token efficiency goes through the roof, and whole classes of problems in AI disappear. That's the semantic layer.

About
Michael Carducci spent years learning to see things as they actually are; first as a magician, then as a software architect, now as both simultaneously. And somehow that’s not even the whole story.

He’s the author of Mastering Software Architecture (Apress, 2025) and is currently writing The Semantic Layer (Packt, 2026). He has spent over 25 years following interesting problems; through roles from individual contributor to CTO and back again, across industries and continents.

As a speaker, he applies the same toolkit he uses in close-up magic: attention, misdirection, timing, storytelling, and the instinct to take the long way around when that’s where the truth lives. Audiences at hundreds of conferences across four continents have described his talks as the kind that change how you think about a problem rather than just what you know about it.

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Please take a moment to forward this information to co-workers, friends, and social networks. Be sure to register.

This is a combined meetup hosted at the dev2next conference at the Denver Marriot South.

6:30-7:00: Food, Soda, and Networking

7:00-7:15: Announcements & Raffles!

7:15-8:30: AI Superpowers: Model Context Protocol in Java & Spring AI

On their own, Large Language Models (LLMs) are only able to generate responses based on their training. While their training may be vast, it will not include any actual data from your organization's databases and systems. What's more, an LLM may be able to answer questions, they are unable to actually interact with your enterprise and take action.

Enter Model Context Protocol (MCP). MCP defines a standard with which you can collection a set of related tools, prompts, and resources and make those available to an LLM to make use of. With MCP, your LLMs will be able to interact with databases, APIs, and other components in your enterprise to get things done.

Building and using MCP in Java has never been easier than it is now with Spring AI. Spring AI introduced support for MCP in its 1.0 release and improved upon it tremendously in Spring AI 2.0.
In this example-driven session, you'll learn how to build an MCP server in Java using Spring AI, integrate it with clients such as Claude Code and Cursor, and even create your own MCP clients.

About
Craig Walls is a Principal Engineer, Java Champion, Alexa Champion, and the author of Spring AI in Action, Spring in Action, and Build Talking Apps. He's a zealous promoter of the Spring Framework, speaking frequently at local user groups and conferences and writing about Spring. When he's not slinging code, Craig is planning his next trip to Disney World or Disneyland and spending as much time as he can with his wife, two daughters, 1 bird and 3 dogs.