Chunking Isn’t Dead. One Size Doesn’t Fit All

Every Learner Gets Their Own Andrew

The video course had a good run. Ryan Keenan from DeepLearning.ai thinks it's over. In this episode, recorded live at AI Dev 26 in San Francisco, Yuval sits down with Ryan to talk about what replaces the current online courses - and why the answer isn't another format: it's a conversation. They dig into why Jupyter Notebooks are out, why React is in, how Andrew Ng's voice clone became a teaching assistant, and what it actually takes to make learning feel personal at scale. The one-size-fits-all era of online education is ending. What comes next is wilder than you'd think.

Everything But the Model: Harness Engineering

Everything you need to know about harness engineering, in less than 30 minutes. In this episode, Yuval sits down with Mike Chambers from AWS to unpack harness engineering - the term that's quietly taken over the AI developer conversation. They dig into what a harness actually is (spoiler: it's everything outside the LLM), how it differs from context engineering and scaffolding, and why getting agents into production has been so hard. Along the way: MCP's rocky debut, OpenClaw's calendar-clearing chaos, and why multi-tenanted agent architecture is harder than it sounds - but doesn't have to be.

Chunking Isn’t Dead. One Size Doesn’t Fit All

Chunking is still one of the least-discussed but most decisive parts of RAG. In this episode, we break down why no single chunk size works for all questions, how different queries benefit from different window sizes, and why fixed-window indexing quietly limits retrieval performance. We walk through a multi-window chunking approach, show how rank fusion ties it together, and explain why better agents can’t fix retrieval when the data is indexed the wrong way.

Stop Shipping Agents With Chat UIs

Chat was a great prototype. It’s a terrible product.   In this episode, Yuval sits down with CopilotKit co-founder Atai to unpack why most agentic apps stall at “chat + vibes”, and why the real bottleneck in production AI isn’t models or reasoning. It’s UI.   They break down what actually changed in the last year, why agents fundamentally break the request-response paradigm, and how a new generation of protocols is emerging to connect agents to real users. The conversation covers: * AG-UI, Model Context Protocol (and MCP Apps) and Agent-to-Agent (A2A) Protocols * The messy (but inevitable) transition from text-only chat to component-rich, voice-enabled, agentic applications. If you’ve built an agent that works but users still bounce, this episode explains why, and what the new “glue layer” of AI UIs is starting to look like.

MCP Was Built for Tools, Not for Agents That Write

MCP standardized tool calling for agents but breaks down once agents start mutating state. In this episode, Yuval sits with Eran Gat from AI21 to dig into what happens when writing agents run in parallel, why shared environments fall apart, and how workspace isolation becomes a missing execution layer. Using real coding workloads and benchmarks, we walk through the architectural trade-offs behind making concurrent agents actually work.