Grumpy old Coders

David and Thomas dive into the critical role of context management and agentic memory with large language models. They explore the core mechanics of AI agents, breaking down the differences between low-level tools, Model Context Protocol (MCP) servers, and high-level skills, before tackling the token cost challenges of stateless LLMs. The hosts analyze human-inspired cognitive strategies like context "compaction" and background "dreaming". The episode closes with thoughts on codebase visualization using Graphify and Obsidian, and a humorous nod to Inception-style agent recursion. Outline: 00:21 – Heatwave talk & audio troubleshooting 07:16 – Chatbots vs. AI agents 12:20 – Defining tools, MCP, and skills 20:51 – Context boundaries & compaction 33:12 – Cognitive concepts and "dreaming" 40:14 – Exploring several tools 1:11:15 – Closing