Malmö AI Devs

In this session, I’ll explore how the Model Context Protocol (MCP) could transform how we design and use web applications. MCP is an open standard that allows AI tools to connect and collaborate across different systems. Through a live demo, you’ll see how to create and use MCP tools to build real-world workflows that automate tasks like scheduling meetings or logging time without navigating endless menus and web sites.

Momentum around MCP is growing fast. Major tech companies and startups are investing heavily in this ecosystem, recognizing that seamless AI interoperability is key to the next wave of software. What makes MCP unique is how it gives AI agents a shared, safe language for discovering and using capabilities across systems, turning the web into a space where agents can act directly and human interfaces appear only when they add value.

Join me for a practical, forward-looking session on what this shift means for developers, designers, and anyone curious about the future of the web. You’ll leave with concrete examples, fresh ideas, and a glimpse of where software might be heading next.

Speaker:
Andreas Ronge - has been building software for almost 30 years, working across telecom, banking, education, enterprise systems, and digital media. He has worked on everything from embedded systems to modern full-stack apps, with a strong focus on well-crafted code, testing, and thoughtful development practices. Passionate about mentorship and innovation, Andreas enjoys sharing knowledge with teams and on stage.

He combines deep technical experience with a practical mindset, always aiming to turn new ideas into scalable, real-world solutions. He created the Neo4j Ruby driver (neo4j.rb) and helped grow local developer communities through Malmö’s Elixir and Ruby meetups. In recent years, Andreas has explored how AI can support real-world development, testing tools from Aider to Claude Code and building client prototypes with agent frameworks and AI platforms. His current focus is on helping developers stay in control and take ownership of what large language models produce and finding reliable ways to verify their results.