No pressure if you have none--come and chat and bring your problems...

Get Lit Lansing (previously Demo Camp Lansing) is an interactive fair. Presenters set up a table, and you walk the room, mingle, and talk directly with the people behind each project. There are up to 40 demo stations, plus short talks of 5–7 minutes and a block of 3–5 minute lightning talks. No pitch contest. It's open to all skill levels and backgrounds: MSU students and faculty, Burgess Institute, MSU Technology, SmartZone, local tech groups, local businesses, startups, game developers, robotics clubs, and anyone building something in Lansing. It's free. Get a ticket: https://luma.com/nsgv3u1y Want a table or a talk slot? Sign up to present: https://forms.gle/3aE34iejLinmuGDcA Schedule * 4:30 PM – Doors open and presenters set up their tables * Early evening – Demo floor opens * Mid-evening – Short talks, with time to mingle between blocks * Later – Lightning talks, closing remarks, and a group photo * About 9:00 PM – Wrap-up Dinner is included: subs, chips, cookies, and drinks...

Lansing DevOps Meetup Lansing in-person gathering for Hybrid Michigan User Group event: The topic will be updated once a speaker is confirmed. Meeting includes a main topic and a short Unix/Linux utility command or concept section...

Terms such as model, instructions, orchestration, tools, context, memory, retrieval, and MCP are often used when describing AI agents, but it isn't always clear what role each one plays or how they fit together. This session takes an AI agent apart and follows a request as it moves through the system. You'll see how the model, instructions, orchestration, tools, context and knowledge, and state or memory work together, and what distinguishes an agent from a simple call to a language model. We'll look at how tools let an agent interact with external systems, where MCP fits into that integration, and what MCP does and doesn't provide. Along the way, we'll examine the roles of identity, authorization, human approval, and observability, and discuss where model-driven decisions are useful versus where conventional application logic is still the better choice. By the end of the session, you'll have a practical mental model for the components that make up an AI agent and the responsibilities that still belong to the application around it. Sponsors * [NIS Technologies](http://www.nistechnologies.com) * [.NET Foundation](https://dotnetfoundation...