AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More [Sebastian Raschka] - 762

In this episode, Sebastian Raschka, independent LLM researcher and author, joins us to break down how the LLM landscape has changed over the past year and what is likely to matter most in 2026. We discuss the shift from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration. Sebastian explains why methods like self-consistency, self-refinement, and verifiable-reward reinforcement learning have become central to progress in domains like math and coding, and where those approaches still fall short. We also explore agentic workflows in practice, including where multi-agent systems add real value and where reliability constraints still dominate system design. The conversation covers architecture trends such as mixture-of-experts, attention efficiency strategies, and the practical impact of long-context models, alongside persistent challenges like continual learning. We close with Sebastian’s perspective on maintaining strong coding fundamentals in the age of AI assistants and a preview of his new book, Build A Reasoning Model (From Scratch). 🗒️ For the full list of resources for this episode, visit the show notes page: https://twimlai.com/go/762. 🔔 Subscribe to our channel for more great content just like this: https://youtube.com/twimlai?sub_confi... 🗣️ CONNECT WITH US! =============================== Subscribe to the TWIML AI Podcast: https://twimlai.com/podcast/twimlai/ Follow us on Twitter:   / twimlai   Follow us on LinkedIn:   / twimlai   Join our Slack Community: https://twimlai.com/community/ Subscribe to our newsletter: https://twimlai.com/newsletter/ Want to get in touch? Send us a message: https://twimlai.com/contact/ 📖 CHAPTERS =============================== 00:00 - Introduction 01:56 - Recent advancements in LLMs 04:34 - Model releases and practical impact 10:48 - Model improvements 14:20 - OpenClaw/Moltbot 16:07 - Building custom tools with LLMs 25:20 - Vibe coding and why learning coding fundamentals still matters 27:44 - Reality of "one-shot" claims on social media 29:31 - 2026 key themes in LLMs 30:46 - Reasoning 32:58 - Verifiable rewards 38:33 - Verification paradigm beyond math and code 41:56 - Inference scaling 47:02 - Self-refinement and self-consistency 50:24 - Agentic systems 53:16 - Multi-agent systems 56:04 - Gaps and improvements in agentic systems 58:28 - Future of LLM architecture 59:55 - Mixture of Experts (MoE), multi-head latent attention, and sparse attention 1:05:11 - Continual learning 1:08:44 - Long-context LLMs 1:11:23 - Predictions 1:13:37 - Build A Reasoning Model (From Scratch) book 🔗 LINKS & RESOURCES =============================== The Big LLM Architecture Comparison - https://magazine.sebastianraschka.com... The State Of LLMs 2025: Progress, Problems, and Predictions - https://magazine.sebastianraschka.com... Build A Reasoning Model (From Scratch) - https://mng.bz/Nwr7 Hands-On Machine Learning Education with Sebastian Raschka - 565 - https://twimlai.com/podcast/twimlai/h... 📸 Camera: https://amzn.to/3TQ3zsg 🎙️Microphone: https://amzn.to/3t5zXeV 🚦Lights: https://amzn.to/3TQlX49 🎛️ Audio Interface: https://amzn.to/3TVFAIq 🎚️ Stream Deck: https://amzn.to/3zzm7F5