AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762

AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with 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). The complete show notes for this episode can be found at https://twimlai.com/go/762.

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Building an AI Mathematician with Carina Hong - #754

Building an AI Mathematician with Carina Hong - #754

In this episode, Carina Hong, founder and CEO of Axiom, joins us to discuss her work building an "AI Mathematician." Carina explains why this is a pivotal moment for AI in mathematics, citing a conver...

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High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive...

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Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Today, we're joined by Alexandre Pesant, AI lead at Lovable, who joins us to discuss the evolution and practice of vibe coding. Alex shares his take on how AI is enabling a shift in software developme...

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Dataflow Computing for AI Inference with Kunle Olukotun - #751

Dataflow Computing for AI Inference with Kunle Olukotun - #751

In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss ...

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Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Today, we're joined by Jacob Buckman, co-founder and CEO of Manifest AI to discuss achieving long context in transformers. We discuss the bottlenecks of scaling context length and recent techniques to...

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The Decentralized Future of Private AI with Illia Polosukhin - #749

The Decentralized Future of Private AI with Illia Polosukhin - #749

In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-...

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Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capab...

23 Syys 20251h 3min

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Today, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her...

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