This talk presents an end-to-end solution for detecting unattended objects in public transport hubs to enhance social security. The project, developed in a three-week challenge, focuses on proactively identifying unattended items using existing camera infrastructure. We will cover the entire pipeline, from data anonymization and preprocessing to building a data labeling platform, object detection with YOLO, and tracking objects over time. The presentation will also discuss the evaluation of the system.

Large‑language‑model agents are only as useful as the context and tools they can reach.

Anthropic’s Model Context Protocol (MCP) proposes a universal, bidirectional interface that turns every external system—SQL databases, Slack, Git, web browsers, even your local file‑system—into first‑class “context providers.”

In just 30 minutes we’ll step from high‑level buzzwords to hands‑on engineering details:

• How MCP’s JSON‑RPC message format, streaming channels, and version‑negotiation work under the hood.
• Why per‑tool sandboxing via isolated client processes hardens security (and what happens when an LLM tries rm ‑rf /).
• Techniques for hierarchical context retrieval that stretch a model’s effective window beyond token limits.
• Real‑world patterns for accessing multiple tools—Postgres, Slack, GitHub—and plugging MCP into GenAI applications.

Expect code snippets and lessons from early adoption.

You’ll leave ready to wire your own services into any MCP‑aware model and level‑up your GenAI applications—without the N×M integration nightmare.