What are the current techniques being employed to improve the performance of LLM-based systems? How is the industry shifting from post-training towards context engineering and multi-agent orchestration? This week on the show, Jodie Burchell, data scientist and Python Advocacy Team Lead at JetBrains, returns to discuss the current AI coding landscape. In our last conversation, Jodie covered how LLMs were approaching the limits of scaling laws. This time, we recap last year’s big focus on reasoning models and a post-training method called “reinforcement learning from verifiable rewards” (RLVR). We also cover test-time compute, where models spend more time reasoning through steps and considering multiple approaches to solve a problem. We touch on Agent Context Protocol (ACP), agent orchestration layers, and context engineering. We also share some concerns about the hype cycle, maintaining all that code being generated, and running local models. Course Spotlight: Vector Databases and Embeddings With ChromaDB Learn how to use ChromaDB, an open-source vector database, to store embeddings and give context to large language models in Python. Topics: 00:00:00 – Introduction 00:02:02 – Build a Language-Learning Agent course 00:02:55 – Update on the past six months of LLMs 00:05:32 – Reinforcement Learning From Verifiable Rewards 00:07:32 – Test Time Compute 00:08:36 – 2025 and the rise of agents 00:14:24 – Benchmarks shifting 00:15:23 – Andrew Karpathy and jagged intelligence 00:19:16 – Not evolving or growing animals but summoning ghosts 00:23:34 – Diminishing gains in newer models 00:24:23 – Context Engineering 00:35:01 – Multi-agent systems and diversity of models 00:36:56 – Video Course Spotlight 00:38:34 – Current generation of coding agents 00:44:00 – Fast vs deep reasoning 00:45:18 – Agent Context Protocol 00:50:19 – Working through the hype cycle 00:55:43 – Open-source contribution pollution 00:57:21 – Local models 00:58:36 – Rick Beato comparing how the music industry failed 01:08:41 – LLMs are an amazing development 01:11:33 – Keynote talk on AI summers and winters 01:12:45 – PyCon US and EuroPython 01:14:11 – Thanks and goodbye Show Links: AI Agent Course - Build a Language‑Learning Agent with OpenAI, LangGraph, Ollama & MCP - YouTube Episode #264: Large Language Models on the Edge of the Scaling Laws Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs Reinforcement learning with verifiable rewards (RLVR) What is test-time compute and how to scale it? Overfitting - Wikipedia 2025 LLM Year in Review - karpathy Animals vs Ghosts - karpathy Agent Context Protocols Enhance Collective Inference