Announcing How Transformer LLMs Work, created with @JayAlammar and @MaartenGr, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here: deeplearning.ai/short-course…

Where is research attention moving according to arXiv papers? We mapped 10,000 of the most-read papers on alphaXiv, weighted by readership, to see how interest shifted from 2025 to 2026. Here’s what we found: 1) Traditional RL post-training is getting less attention. As the focus moves beyond math and reasoning to longer agent tasks, compute demands put this research out of reach for many teams 2) Harnesses, agent systems, memory and distillation are gaining reading share, offering other ways to improve what models can do 3) Interest is also growing in robotics and world models, bringing AI into the physical world

Navier-Stokes Equation, explained with colors p.migdal.pl/equations-explai…

Proud to have helped build Kolibri 🐦 We brought our Merlin-Arthur work on grounding and hallucination reduction into the model. Iteration one: scale brings its challenges, but the training pipeline lets us learn and iterate fast.

Elephant choruses function as group signals encoding context through both their composition and patterns of acoustic convergence @Limor_Raviv nature.com/articles/s41598-0…

🚀 NOW SHIPPING! 📚🤖 An Illustrated Guide to AI Agents by @MaartenGr & @JayAlammar is here! Explore LLMs, tools, memory, planning, reasoning, multi-agent systems & practical code. Order now shroffpublishers.com/books/9… #AIAgents #AI #LLM #MachineLearning

OMG this is awesome! I loved your blog posts explaining models, will get this one right away!

We now run the largest frontier lab composed entirely of autonomous AI researchers. Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose. One discovery we can already share is that the society has discovered a novel result which improves model training by 30%. Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail. Everything is observable. You can open the virtual city in a browser and read what any researcher is working on. Learn more about Primus Society here: lab.cloud/society The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.