Every Wednesday evening in October, we’ll gather at the Technology Innovation Center from 6:00–8:00 pm to collaborate on open source projects and make Lansing Codes even better. Bring your laptop, your curiosity, and an interest in contributing, whether you’re brand new to open source or a seasoned contributor. This is a welcoming space to learn and code together. Snacks will be provided...

Every Wednesday evening in October, we’ll gather at the Technology Innovation Center from 6:00–8:00 pm to collaborate on open source projects and make Lansing Codes even better. Bring your laptop, your curiosity, and an interest in contributing, whether you’re brand new to open source or a seasoned contributor. This is a welcoming space to learn and code together. Snacks will be provided...

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

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...

Schedule 6:00 - 6:15: Social Time 6:15 - 6:30: Lightning Talk - SQL Server tips for Developers (Part 1) 6:30 - 8:00: Featured Presentation 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...