EP 22 - DGX update, Kimi k2 thinking as new workhorse, deep agents Langchain, and Code Mode for MCP

In this conversation, Tom Spencer and Cameron Rohn explore various themes surrounding AI, including nostalgic references to early AI tools, the evolution of product management with AI, and the innovative capabilities of deep agents and open-source models. They discuss the importance of context in AI communication, the iterative learning process of AI agents, and the challenges of managing expectations with AI outputs. The conversation also delves into the role of long-term memory in AI, the emergence of Kimi K2 Thinking, and the intricacies of fine-tuning AI models for specific tasks. In this conversation, Cameron Rohn and Tom Spencer delve into the intricacies of fine-tuning AI models, discussing the challenges and opportunities that arise in this domain. They explore the innovative applications of fine-tuning, particularly in the context of Nvidia's ecosystem and the implications of dynamic worker loaders and code execution. The discussion also touches on the future of AI applications and user experience, emphasizing the need for better interfaces and the potential for new business models in the AI landscape.Chapters00:00 Nostalgia and AI Beginnings04:28 Exploring AI Agents and Tools09:21 Deep Agents and Workflow Automation29:20 Exploring the DeepAgent CLI32:33 Understanding the Deep Agents UI37:29 Creating Tailored Co-Pilots for Specialists42:30 Integrating Long-Term Memory in Deep Agents46:39 The Future of Agent Management in Organizations54:44 Kimi k2 Thinking and Open source59:04 Fine-Tuning Models for Custom Needs01:16:23 Code Execution and MCP Innovations - Demo01:28:11 Exploring DeepAgents and Code Sandbox Support01:32:56 Creating Multi-Step Workflows with MCP Tools01:37:56 User Experience and Control in AI Tools01:42:54 The Future of AI Applications and Business Models