Jay Alammar+

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Inside NeurIPS 2025: The Year’s AI Research, Mapped New blog post! NeurIPS 202

Inside NeurIPS 2025: The Year’s AI Research, Mapped New blog post! NeurIPS 2025 papers are out—and it’s a lot to take in. This visualization lets you explore the entire research landscape interactively, with clusters and @cohere.com LLM-generated explanations that make it easier to grasp.

We're also eager to hear what you'd like to see in such a book. What concepts do

We're also eager to hear what you'd like to see in such a book. What concepts do you think are central to understanding, building, and properly using AI agents? Early release coming soon!

This will be our most visually-rich project yet. We will drill into the main con

This will be our most visually-rich project yet. We will drill into the main concepts for understanding and building AI agents -- likely the most promising area of AI as of today. It will cover tools, memory, code generation, reasoning, multimodality, RLVR/GRPO, and much more.

The Illustrated Guide to AI Agents New book announcement! Thrilled that togeth

The Illustrated Guide to AI Agents New book announcement! Thrilled that together with @maartengr.bsky.social , we're writing a new book titled “An Illustrated Guide to AI Agents” and published by @oreilly.bsky.social.

The Illustrated GPT-OSS New post! A visual tour of the architecture, message f

The Illustrated GPT-OSS New post! A visual tour of the architecture, message formatting, and reasoning of the latest GPT. newsletter.languagemodels.co/p/the-illust...

The legendary John Carmack at #upperbound: - Current AI focus is RL (with Richar

The legendary John Carmack at #upperbound: - Current AI focus is RL (with Richard Sutton) solving Atari games - Thinking in line with the Alberta Plan. - It was a misstep to start working too low-level (e.g., at the cuda level). I kept stepping up the stack chain until now in pytorch

🔭 Eynshteyn sun’iy intellekt modeli

“Qisqartirilgan XXI asr” yo‘lida nimani ko‘zdan qochirayotganimiz haqida

Learn how ChatGPT and DeepSeek models work: How Transformer LLMs Work [Free Course]

Enroll for free now: https://bit.ly/4aRnn7Z Github Repo: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models We're ecstatic to bring you "How Transformer LLMs Work" -- a free course with ~90 minutes of video, code, and crisp visuals and animations that explain the modern Transformer architecture, tokenizers, embeddings, and mixture-of-expert models. @MaartenGrootendorst and I have developed a lot of the visual language over the last several years (tens of thousands of iterations for hundreds of figures) for the book. This was informed by many incredible colleagues at Cohere, C4AI, a

The Illustrated DeepSeek-R1 Spent the weekend reading the paper and sorting thr

The Illustrated DeepSeek-R1 Spent the weekend reading the paper and sorting through the intuitions. Here's a visual guide and the main intuitions to understand the model and the process that created it. newsletter.languagemodels.co/p/the-illust...

Alphaxiv is an awesome way to discuss ML papers -- often with the authors themse

Alphaxiv is an awesome way to discuss ML papers -- often with the authors themselves. Here's an intro and demo by @rajpalleti.bsky.social we shot at #Neurips2024 www.youtube.com/watch?v=-Kwl...

AlphaXiv - a great place to discuss ML papers

Alphaxiv is an awesome way to discuss ML papers -- often with the authors themselves. Here's an intro and demo by Raj Palleti we shot at Neurips2024.

It contains a notebook per chapter containing all the code examples in the book.

It contains a notebook per chapter containing all the code examples in the book. Check it out here: github.com/HandsOnLLM/H...

Floored that the repo for Hands-On Large Language Models is now at 3.6k Github s

Floored that the repo for Hands-On Large Language Models is now at 3.6k Github stars! And excited that professors are starting to use the book to teach LLM courses. Reach out to us if we can be of assistance! And if you've liked the book, leave us a review on Amazon or Goodreads!

SWE-Bench has been one of the most important tasks measuring the progress of age

SWE-Bench has been one of the most important tasks measuring the progress of agents tackling software engineering in 2024. I caught up with two of its creators, @ofirpress.bsky.social and Carlos E. Jimenez to share their ideas on the state of LLM-backed agents. www.youtube.com/watch?v=bivZ...

SWE-Bench authors reflect on the state of LLM agents at Neurips 2024

The SWE-bench task measures AI agents on software engineering tasks at the level of a github issue. It was one of the most important tasks measuring the progress of agents tackling software engineering tasks in 2024. We caught up with two of its creators, Ofir Press and Carlos E. Jimenez, to share their ideas on the state of LLM-backed agents. --- Check out our book: https://www.llm-book.com/ Mailing List: https://newsletter.languagemodels.co/ Bluesky: https://bsky.app/profile/jayalammar.bsky.social Twitter: https://twitter.com/JayAlammar Blog: https://jalammar.github.io/

Good morning #NeurIPS2024! Stop by the @cohere.com booth at 3PM today (Thursday)

Good morning #NeurIPS2024! Stop by the @cohere.com booth at 3PM today (Thursday) for a signed copy of Hands-On Large Language Models - it will introduce you to LLMs, their applications, as well as Cohere's Embed, Rerank, and Command-R models. Come early as quantities are limited!

I'll be in the Cohere #NeurIPS2024 booth most of this afternoon. Come say hi, as

I'll be in the Cohere #NeurIPS2024 booth most of this afternoon. Come say hi, ask questions, and yes, we're hiring! Tomorrow I'll be signing copies of my book at 3PM! Limited copies available!

Hi NeurIPS! Explore ~4,500 NeurIPS papers in this interactive visualization: j

Hi NeurIPS! Explore ~4,500 NeurIPS papers in this interactive visualization: jalammar.github.io/assets/neuri... (Click on a point to see the paper on the website) Uses @cohere.com models and @lelandmcinnes.bsky.social's datamapplot/umap to help make sense of the overwhelming scale of NeurIPS.

Sure to be thought provoking. The previous interview had fascinating thoughts on

Sure to be thought provoking. The previous interview had fascinating thoughts on scifi (Dune Vs. Foundation), on AI competition for AI safety, and on successful scifi as a self-preventing prophecy.

Ko‘rsatkichlar SI uchun katta muammo

Ko‘rsatkichlarni o‘ylamasdan optimallashtirish turli xil jiddiy zararlarga olib kelishi va hozirgi SI yondashuvlarining aksariyati ko‘rsatkichlarni optimallashtirishga aylangani haqida.

Memlar inqirozi

Nima uchun ChatGPT doimo bir xil narsalarni tavsiya qiladi?

Masshtab: AI sohasidagi bugungi holat

Turli avlod modellariga nazar...

Kaggle bilan yoki kaggle'siz. Shudir masala

Oxir-oqibat, bu IT bo‘limidagi Bobga bog‘liq.

Building LLM Agents with Tool Use

Tool use is a method whichs allows developers to connect Cohere's Command models to external tools like search engines, APIs, databases, and other software tools. Just like how Retrieval-Augmented Generation (RAG) allows a model to use an external data source to improve factual generation, tool use is a capability that allows retrieving data from multiple sources. But it goes beyond simply retrieving information and is able to use software tools to execute code, or even create entries in a CRM system. In this video, we'll see how we can use two tools to create a simple data analyst agent that

Ha, siz backprop’ni tushunishingiz kerak

Haqiqiy maqola Andrey Karpati tomonidan karpathy.medium.com saytida 2016-yil 20-dekabrda e’lon qilingan.

Illyustratsiyali BERT, ELMo va boshq.

NLP qanday qilib Transfer O‘rganishni (Transfer Learning) ishg‘ol qilgani haqida!

AI muhandisining yuksalishi

Favqulodda paydo bo‘layotgan imkoniyatlar yangi sarlavhani yaratmoqda: shunchaki Prompt muhandisligidan tashqariga chiqib, haqiqiy dasturlar yozishimiz kerak.

Software 2.0

Dasturlashni qayta tasavvur qilish: SI qanday qilib dasturiy ta’minotni ishlab chiqishni o‘zgartiradi.

Illyustratsiyali Transformerlar

Mashinalar tilni tushunishni qanday "o‘rganadilar": transformerlar arxitekturasining asosiy tushunchalarini ko‘rib chiqish. Mashhur "The Illustrated Transformer" o‘zbek tilida!

Neyron mashinaviy tarjima modellarini vizualizatsiya qilish (E’tiborga asoslangan seq2seq modellari mexanikasi)

Haqiqiy maqola Jey Alammar tomonidan jalammar.github.io saytida 2018-yil 9-mayda e’lon qilingan.

Sizda hali ham imkon bor (SIga o‘tish uchun)

Sun’iy intellekt (SI) sohasida so‘nggi 5 yil ichida sodir bo‘lgan muhim hodisalar va qanday qilib yetib olish haqida.

Achchiq saboq (The bitter lesson)

Sun’iy Intellekt taraqqiyoti chizgilari: Mashinaviy O‘rganish (ML) da hisoblash quvvati va qidiruvning hukmronligi.

Nega Sun’iy Intellekt biz o‘ylaganimizdan qiyinroq?

Haqiqiy Sun’iy Intellekt yaratish yo‘lidagi baland-pastliklar va ortiqcha baho berishning xatarlari.

Bitta tanganing ikkita tarafi: Jeremi Hovard ning fast.ai va Andryu Ng ning deeplearning.ai kurslari

Qanday qilib fast.ai va deeplearning.ai kurslarining ikkalasini ham o‘rganib ‘overfit’ bo‘lib qolmaslik haqida.

What makes LLM tokenizers different from each other? GPT4 vs. FlanT5 Vs. Starcoder Vs. BERT and more

Tokenizers are one of the key components of Large Language Models (LLMs). One of the best ways to understand what they do, is to compare the behavior of different tokenizers. In this video, Jay takes a carefully crafted piece of text (that contains English, code, indentation, numbers, emoji, and other languages) and passes it through different trained tokenizers to reveal what they succeed and fail at encoding, and the different design choices for different tokenizers and what they say about their respective models. --- Contents: 0:00 Introduction 1:25 The carefully polished text to test to

ChatGPT has Never Seen a SINGLE Word (Despite Reading Most of The Internet). Meet LLM Tokenizers.

Despite processing internet-scale text data, large language models never see words as we do. Yes, they consume text, but another piece of software called a tokenizer is what actually takes in the text and translates it into a different format that the language model actually operates on. In this video, Jay goes examines a language model tokenizer to give you a sense of how they work. Follow our upcoming book, Hands-On Large Language Models, for more details about tokenizers and LLMs in general. Updates on the book coming on https://jayalammar.substack.com/ My co-author: https://twitter.com/Ma

prompt chains are important for building large language model applications

What is Llama Index? how does it help in building LLM applications? #languagemodels #chatgpt

How to manage LLM prompts with tools like LangChain #languagemodels #chatgpt

Are language models with more parameters better? #shorts #chatgpt

What is LangChain? Where does it fit with LLMs like ChatGPT and Cohere? #shorts

Tools like Langchain (https://github.com/hwchase17/langchain/tree/master/langchain) help you build applications on top of large language models. #shorts

AI is Eating The World - This is Where YOU Can Use it to Compete (AI Product Moats)

Over a decade ago, the phrase “software is eating the world” described how software was rapidly becoming the center of many industries beyond the technology sector. The leading book retailers, video services providers, music companies, entertainment companies, and even movie production companies were essentially software companies. That trend is still going strong. In this video, Jay shares observations on the value in the AI technology stack and focuses on where some of the technical moats might be. The previous video in this series (https://www.youtube.com/watch?v=AeW9r3lopp0) discusse

What is Generative AI? 4 Important Things to Know (about ChatGPT, MidJourney, Cohere & future AIs)

What's the big deal with Generative AI? Is it the future or the present? In this video, Jay goes over four key reflections on how best to think of the current state of AI products and features, and avoid pitfalls people tend to make with new tech. Blog post: https://txt.cohere.ai/generative-ai-future-or-present/ What is Neural Search? Nils Reimers - Sentence Transformers and Embedding Evaluation https://www.youtube.com/watch?v=Z_4rohX4Ki8&ab_channel=Cohere --- Twitter: https://twitter.com/JayAlammar Blog: https://jalammar.github.io/ Mailing List: https://jayalammar.substack.com/ --

AI Art Explained: How AI Generates Images (Stable Diffusion, Midjourney, and DALLE)

Learn how AI image generation works. This video goes over the AI components of AI image generation models like Stable Diffusion and explains how they work and how they're trained. Blog post: https://jalammar.github.io/illustrated-stable-diffusion/ --- Twitter: https://twitter.com/JayAlammar Blog: https://jalammar.github.io/ Mailing List: https://jayalammar.substack.com/ -- Introduction (0:00) Text-to-image and image-to-image (1:32) The components of Stable Diffusion - high-level overview (3:06) The three models inside the AI Image Generator (5:48) Generating images with reverse diffusion

Nemesis 2 Intro Remade with AI Generated Images

This is a version of the intro cinematic to an old video game (Nemesis 2 on the MSX system). The graphics are remade using AI Generated images. Read more about the process in: https://jalammar.github.io/ai-image-generation-tools/ Original: https://www.youtube.com/watch?v=nWdTIHpUORE&ab_channel=Zebpro