Archive

Learn more about the product, infrastructure, and harness details used to build Grok Bot, a new type of always-on and proactive agent.

This video locally installs and tests Ornith-1.0, a self-improving family of open-source models for agentic coding.

Sebastian Raschka, Independent AI Researcher and author of Build a Large Language Model from Scratch, joins Hugo to talk about what's changed in AI architecture, from post-training to hybrid models, and why understanding what's under the hood matters more than ever for developers building in the agentic era.

Kshetrajna Raghavan, a principal engineer from Shopify, walks through how they evolved their approach to extracting structured data from millions of merchant stores. One-shot LLM calls hit quality and cost ceilings fast, so the team shifted to an agentic architecture where the model explores stores on its own to find answers.

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version.

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In this deeply informative video, Jeremy Howard, co-founder of fast.ai and creator of the ULMFiT approach on which all modern language models (LMs) are based, takes you on a comprehensive journey through the fascinating landscape of LMs. Starting with the foundational concepts, Jeremy introduces the architecture and mechanics that make these AI systems tick. He then delves into critical evaluations of GPT-4, illuminates practical uses of language models in code writing and data analysis, and offers hands-on tips for working with the OpenAI API. The video also provides expert guidance on…

Alex shares why he chose to found a machine learning company with Clojure, what worked, what didn’t, and how they scaled their technology team from 3 people to over 40. Alex explains where they use Clojure, and where they don’t, and why. How Clojure’s strengths and weaknesses change with your team size. Which assumptions he made about Clojure when founding the company that changed over time, and which turned out to be correct.

has 700+ videos on YouTube covering everything from basic arithmetic and algebra to differential equations, physics, and finance