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I'm excited to announce a new course on specdriven development built in partnership with Jet Brains. As opposed to Vive coding, specdriven development is a disciplined agentic coding workflow. Start with a markdown file or a long prompt explaining exactly what to build and let your agent code based on that spec. Many of the best developers build this way. This course teaches you how to write highquality specs for developing in both new and existing code bases. I'm delighted our instructor for th

Entropic, the company that literally markets itself as the safe AI company, just leaked Claude Code's entire codebase, 512,000 lines of source code, their most important product wide open through an npm package because someone forgot an npm ignore file. Not a hack, not a whistleblower, a $350 billion company shipping source maps to production because their bundler generates them by default and nobody turned it off. To be honest, this feels like a dream. I use Club Code almost daily. It's my main

I'm thrilled to introduce this course on efficient inference with SG lang where you learn about both text and image generation. This is built in partnership with OMIS and radics. Running LMS in production is expensive and much of that cost comes from potentially redundant computation. Every new message forces the model to reprocess the same system prompt and context again from scratch. SG lang is an open- source inference framework that eliminates that waste by caching computation that's already

As AI become more and more autonomous, won't they just amplify their biases and make everything worse? Recently, I've got that question a lot in different forms. That sounds reasonable. If a model already has biases, and now we give them even more power, memory, tools, long-term planning, and the ability to act in the real world, doesn't that just scale the problem? And I think many people watching this are wondering the same thing, even if they don't see it like that. So in this video I want to

If you've been hearing harness engineering everywhere lately and your first thought is, "Okay, so this is probably just prompt engineering with a nicer name." That's the first misconception to kill. It's not. And it's not just a new term. And the reason this matters is not just vocabulary either. It tells you where AI is actually heading. By the end of this video, you should be able to tell the difference between prom engineering, context engineering, and hardness engineering. And more important

Welcome to TensorFlow from basics to mastery. Some of you may have taken deep learning or machine learning from me and learned about the amazing things you can now do with deep learning and machine learning. One of the best tools you can use to implement these algorithms is TensorFlow. Learning algorithms can be quite complicated and today programming frameworks like TensorFlow, PyTorch, Cafe and many others can save you a lot of time. These tools can be complicated and what this set of courses

I want you to meet my friends Ununice and Lucas, the instructors of this specialization. >> Hi everyone, >> welcome to the specialization. >> I've known Ununice for many years. He is an instructor at Stanford University and had also helped me create the deep learning specialization. Over the years, Ununice has taught hundreds of students learning AI and machine learning and NLP and other topics for the first time. and he's also mentored students on a huge variety of AI related

Welcome to AI for everyone. AI is changing the way we work and live. And this non-technical course will teach you how to navigate the rise of AI. Whether you want to know what's behind the buzzwords or whether you want to perhaps use AI yourself either in a personal context or in a corporation or other organization, this course will teach you how. And if you want to understand how AI is affecting society and how you can navigate that, you also learn that from this course. In this first week, we'