Code Conversations

Cybersecurity is evolving fast . Jeff Crume breaks down trends like Shadow AI, polymorphic malware, and post-quantum cryptography, uncovering the risks shaping 2026. Learn how innovations like AI and quantum computing are redefining security and what it means for the future.

Are your AI models secure? Jeff Crume explains OWASP's Top 10 for LLMs, including risks like prompt injection and data leaks. Discover actionable tips like firewalls and access controls to safeguard your AI systems from attacks and vulnerabilities. Ref: https://www.youtube.com/watch?v=gUNXZMcd2jU

AI can generate software faster than ever, but speed alone doesn't create trust. Jeff Crume explains how developers secure AI-generated code using shift-left security, dependency validation, continuous testing, and outcome-based verification. Learn five key principles for building safer AI-assisted software development workflows.

AI models alone aren’t what makes systems powerful. Martin Keen explains the difference between AI models and agentic harness components like tools, memory, and loops. Learn how generative AI agents work and what drives real system performance. Ref: https://www.youtube.com/watch?v=ZELPNFXJ4_o

The agent hit a login page, panicked, reported success anyway, and the upvote never happened. Tejas Kumar's diagnosis: not a prompt problem. A harness problem. The demo builds a browser agent on GPT-3.5 Turbo (consciously choosing a VERY old model to show how good harness eng can improve it a lot) against Hacker News and layers in a harness without touching the prompt once. Guardrails cap iter...

Most developers have heard of prompt engineering. Many know about context engineering and RAG. But the real frontier in AI today is harness engineering — the structured environment that transforms an ordinary LLM into a powerful agentic system. In this episode of the Code to Care series, I walk through the complete evolution of how developers have learned to work with large language models (LLMs):...

Agent harnessing and harness engineering is a growing topic - and yet the term requires more clarification on what it is and why agentic systems evolved the way it did to where we are today.

Choosing a local LLM engine can make or break performance. Cedric Clyburn breaks down Llama.cpp versus vLLM for real‑world local inference. Learn which tool fits personal hardware, production scale, and AI agent workloads. Ref: https://www.youtube.com/watch?v=0ujh7hfutq0

AI promises speed, but where are the real gains? Cedric Clyburn breaks down why productivity stalls across the software development lifecycle despite faster coding. Learn how redesigning SDLC workflows with AI agents improves outcomes, testing, and delivery. Ref: https://www.youtube.com/watch?v=4wMRXmLpdA8

We've all been using AI chatbots, but AI agents can now move from knowing to doing. Cedric Clyburn breaks down how AI agents, LLMs, tools, and the agentic loop enable real autonomous workflows. Learn how automation, security, and modern AI systems are shaping the future of tech. Ref: https://www.youtube.com/watch?v=L7FF8Zgab3M

As AI agents become more capable, the skills needed for AI jobs are shifting. Bri Kopecki breaks down the 7 skills you need to move from prompt engineering to full agent engineering, including system design, retrieval, reliability, and security. Learn how to build AI agents that actually work in production. Ref: https://www.youtube.com/watch?v=mtiOK2QG9Q0&t=10s

LangChain became immensely popular when it was launched in 2022, but how can it impact your development and application of AI models, Large Language Models (LLM) in particular. In this video Martin Keen shares an overview of the features and uses of LangChain.

Get ready for a showdown between LangChain and LangGraph, two powerful frameworks for building applications with large language models (LLMs.) Master Inventor Martin Keen compares the two, taking a look at their unique features, use cases, and how they can help you create innovative, context-aware solutions.

Agentic AI and RAG are redefining how LLMs think and act 🤖. Live from TechXchange in Orlando, Martin Keen & Cedric Clyburn unpack how vector databases, data integration, and context engineering enable smarter AI systems. See how this pairing powers innovation in automation.

How did search evolve into agentic AI? Sam Anthony explains RAG's evolution, from simple retrieval to adaptive systems powered by LLMs. Learn how semantic search, hybrid retrieval, and AI agents enable multi-step research, decision-making, and synthesis.

We're all using AI agents, but they still lack the procedural knowledge real work needs. Martin Keen explains how agent skills, LLMs, RAG, and MCP help agents follow workflows, automate tasks, and make smarter decisions. Learn how to build better AI agents.

How do AI agents learn and take action? Live from TechXchange in Orlando, Melissa Hadley breaks down how MCP and RAG help large language models connect to data — one to retrieve knowledge, the other to execute tasks. Explore how these frameworks combine to power smarter, connected AI systems.

How do AI chatbots deliver better responses? Martin Keen explains RAG 🛠️, fine-tuning , and prompt engineering methods that extend knowledge, refine responses, and build domain expertise. Learn how these strategies optimize large language models and improve AI outputs today! Ref: https://www.youtube.com/watch?v=zYGDpG-pTho

Are your AI agents struggling to collaborate? 🤔 Martin Keen and Anna Gutowska reveal how advanced frameworks enable seamless agent communication and integration with tools. Discover how A2A connects agents and MCP links them to resources for smarter, streamlined workflows. 🚀

Both CLI and MCP are ways for AI agents to interact with the world. Martin Keen explains how agents use CLI commands or MCP tools for tasks across real developer workflows. Learn how context windows, schemas, and practical examples shape smarter AI development. 🚀 Ref: https://www.youtube.com/watch?v=g9JIUM0MHgQ

Are massive context windows replacing RAG? 🤔 Martin Keen breaks down RAG vs. long context in LLM workflows. Explore how vector databases, semantic search, and embedding models impact AI performance to help you choose the right solution for your applications. 🚀

AI agents are having a moment, and understanding MCP and ADK is key to building them well. Cedric Clyburn and Anna Gutowska explain how MCP powers tool integration while ADK structures reliable multi‑agent systems 🤖. Learn when to use each to build more capable and predictable AI agents.

Ready to become a certified GenAI engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdGhCF Learn about the technology → https://ibm.biz/BdMsRT Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time they can become incomplete--or worse, generate answers that are just plain wr...

MCP or API: Which transforms AI integration? Martin Keen explains how the Model Context Protocol (MCP) revolutionizes AI agents by enabling dynamic discovery, tool execution, and seamless external data retrieval. Discover how MCP simplifies LLM workflows and outpaces traditional APIs. Ref: https://www.youtube.com/watch?v=7j1t3UZA1TY

Tim Berglund is back at the lightboard with MCP (Model Context Protocol). MCP really is a big deal, but most people are missing the point. It's not just about enhancing desktop applications with agentic functionality—it's about exposing reusable tools and resources (including real-time data from sources like Apache Kafka) to build agentic microservices. Doing this takes us beyond basic LLM...