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.
[LIVE] Anthropic Distillation & How Models Cheat (SWE-Bench Dead) | Nathan Lambert & Sebastian Raschka
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#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch).
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OUTLINE:
(00:00) – Introduction
(01:39) – Sponsors, Comments, and Reflections
(16:29) – China vs US: Who wins the AI race?
(25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
(36:11) – Best AI for coding
(43:02) – Open Source vs Closed Source LLMs
(54:41) – Transformers: Evolution of LLMs since 2019
(1:02:38) – AI Scaling Laws: Are they dead or still holding?
(1:18:45) – How AI is trained: Pre-training, Mid-training, and Post-training
(1:51:51) – Post-training explained: Exciting new research directions in LLMs
(2:12:43) – Advice for beginners on how to get into AI development & research
(2:35:36) – Work culture in AI (72+ hour weeks)
(2:39:22) – Silicon Valley bubble
(2:43:19) – Text diffusion models and other new research directions
(2:49:01) – Tool use
(2:53:17) – Continual learning
(2:58:39) – Long context
(3:04:54) – Robotics
(3:14:04) – Timeline to AGI
(3:21:20) – Will AI replace programmers?
(3:39:51) – Is the dream of AGI dying?
(3:46:40) – How AI will make money?
(3:51:02) – Big acquisitions in 2026
(3:55:34) – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
(4:08:08) – Manhattan Project for AI
(4:14:42) – Future of NVIDIA, GPUs, and AI compute clusters
(4:22:48) – Future of human civilization
Navigating AI Frontiers: William Falcon Explores PyTorch Lightning's Evolution
In this episode, we navigate the frontiers of AI alongside William Falcon, CEO of Lightning AI, as he delves into the evolutionary journey of PyTorch Lightning.
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767: Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka
Jon Krohn sits down with Sebastian Raschka to discuss his latest book, Machine Learning Q and AI, the open-source libraries developed by Lightning AI, how to exploit the greatest opportunities for LLM development, and what’s on the horizon for LLMs.
This episode is brought to you by the DataConnect Conference, and by Data Universe, the out-of-this-world data conference. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.
In this episode you will learn:
• All about Machine Learning Q and AI [04:13]
• Sebastian Raschka’s role as Staff Research Engineer at Lightning AI [19:21]
• PyTorch Lightning’s and Lightning Fabric’s capabilities [39:32]
• Large language models: Opportunities and challenges [43:35]
• DoRA vs LoRA [48:56]
• How to be a successful AI educator [1:34:18]
Additional materials: www.superdatascience.com/767
The creator of PyTorch Lightning on the AI hype cycle
William is the CEO of Lightning AI and the creator of PyTorch Lightning, the lightweight PyTorch wrapper for high-performance AI research.
Dive into their docs or explore the developer community.
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Advancing Hands-On Machine Learning Education with Sebastian Raschka - #565
Today we’re joined by Sebastian Raschka, an assistant professor at the University of Wisconsin-Madison and lead AI educator at Grid.ai. In our conversation with Sebastian, we explore his work around AI education, including the “hands-on” philosophy that he takes when building these courses, his recent book Machine Learning with PyTorch and Scikit-Learn, his advise to beginners in the field when they’re trying to choose tools and frameworks, and more.
We also discuss his work on Pytorch Lightning, a platform that allows users to organize their code and integrate it into other technologies, before switching gears and discuss his recent research efforts around ordinal regression, including a ton of great references that we’ll link on the show notes page below!
The complete show notes for this episode can be found at twimlai.com/go/565
Multi-GPU training is hard (without PyTorch Lightning)
William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research that lets you train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! In this episode, we dig deep into Lightning, how it works, and what it is enabling. William also discusses the Grid AI platform (built on top of PyTorch Lightning). This platform lets you seamlessly train 100s of Machine Learning models on the cloud from your laptop.
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Featuring:
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Show Notes:
PyTorch Lightning | Website
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PyTorch Lightning | GitHub
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Grid AI, From the Makers of PyTorch Lightning, Emerges From Stealth With $18.6m Series A to Close the Gap Between AI Research and Production
Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production
Grid.ai Launches Platform to Train Machine Learning Models in the Cloud
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