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STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The...

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CyrilXBT

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Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.
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Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

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Godfather of AI: "If you sleep well tonight, you may not have understood this lecture." This 47-minute lecture is the best thing I saw about AI in the last few months. It will definitely help you understand how it actually works and where it's going. Geoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it. The part nobody wanted to hear: > AI is already developing abilities its creators didn't intend > in most cognitive tasks it's already ahead of us > the question is no longer if it surpasses us but when > the only decision left is which side of that line you're on Right now the average person opens Claude, types something, gets an answer, closes the tab. They think they're using AI. they're using maybe 10% of it. I went through his entire lecture, built a practical system from what he was describing. 18 steps to actually use Claude the right way, with copy-paste prompts that work today. Full guide in the post below.

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Percy Liang teaches Stanford students how AI actually thinks now we get to learn it too. same lectures Stanford undergrads pay nearly $90,000 a year for. it teaches the math underneath every AI tool you use: / search and planning -> why agents loop the way they do / MDPs and RL -> why models reward-hack and how to spot it / probability and Bayes nets -> why outputs are calibrated (or aren't) / machine learning fundamentals -> what fine-tuning actually changes
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Percy Liang teaches Stanford students how AI actually thinks now we get to learn it too. same lectures Stanford undergrads pay nearly $90,000 a year for. it teaches the math underneath every AI tool you use: / search and planning -> why agents loop the way they do / MDPs and RL -> why models reward-hack and how to spot it / probability and Bayes nets -> why outputs are calibrated (or aren't) / machine learning fundamentals -> what fine-tuning actually changes

dunik

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Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
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Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

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is changing its name. 13 years ago, launched with a simple idea: every student should learn computer science — to learn how technology works and how to create it — not just how to use it. After more than 2 billion hours of learning, the focus of CS has moved from coding to AI. As AI reshapes every part of daily life, students need digital fluency: the ability to understand AI, direct it, question it, and create with it — built on the foundations of computer science, AI science, and data science. Today, enters its next chapter as CodeAI. Our curriculum, teacher training, and frameworks, are there already: AI Discoveries and AI Foundations are free and in classrooms now. The K-12 digital sciences pathway is expanding. Our goal is a generation with agency over the systems shaping their lives — prepared to shape the work, civic life, relationships, and meaning that come after. Welcome to CodeAI.

Hadi Partovi

10,876 Aufrufe • vor 3 Monaten

Sam Altman said it straight in a CNBC interview: the companies adopting AI the most are hiring the most, and the ones doing layoffs are the ones adopting AI the least. This completely flips the narrative that AI is killing jobs. At the end of the day, AI is a productivity tool. Yes, some jobs will become redundant and obsolete, that is how it is for every technological revolution. But those who adopt quickly will have a higher chance of making it.
0:38

Sam Altman said it straight in a CNBC interview: the companies adopting AI the most are hiring the most, and the ones doing layoffs are the ones adopting AI the least. This completely flips the narrative that AI is killing jobs. At the end of the day, AI is a productivity tool. Yes, some jobs will become redundant and obsolete, that is how it is for every technological revolution. But those who adopt quickly will have a higher chance of making it.

Lark Davis

16,253 Aufrufe • vor 3 Monaten

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

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SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.

Om Patel

617,133 Aufrufe • vor 4 Monaten

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.
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NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 Aufrufe • vor 10 Monaten

SpaceX just agreed to buy Cursor for $60 billion. Everyone is talking about the product, the models, the valuation. I’m interested in the human side of this AI story. Who built the team behind one of the fastest-growing software companies in history, and how do they think about talent at the frontier of AI? For the first episode of my new podcast, Below the Surface, I sat down with Adam Ward, Cursor 's Head of Talent, to find out. Before Cursor, Adam helped scale Facebook through its IPO, ran global recruiting at Pinterest from 200 to 2000+ people, and built Growth by Design Talent, the advisory firm behind OpenAI, Anthropic, Stripe, Notion, and Figma … until Cursor acquired it last year. We talked about what hiring looks like when the market is moving this fast, how Cursor does it differently, and what it actually takes to build a high-performance team right now. One of my biggest takeaways: the companies that win in AI won’t just have better models. They’ll have better systems for finding, assessing, and enabling exceptional people. Watch the full episode here: Or subscribe wherever you get podcasts: 🎧 🎤
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SpaceX just agreed to buy Cursor for $60 billion. Everyone is talking about the product, the models, the valuation. I’m interested in the human side of this AI story. Who built the team behind one of the fastest-growing software companies in history, and how do they think about talent at the frontier of AI? For the first episode of my new podcast, Below the Surface, I sat down with Adam Ward, Cursor 's Head of Talent, to find out. Before Cursor, Adam helped scale Facebook through its IPO, ran global recruiting at Pinterest from 200 to 2000+ people, and built Growth by Design Talent, the advisory firm behind OpenAI, Anthropic, Stripe, Notion, and Figma … until Cursor acquired it last year. We talked about what hiring looks like when the market is moving this fast, how Cursor does it differently, and what it actually takes to build a high-performance team right now. One of my biggest takeaways: the companies that win in AI won’t just have better models. They’ll have better systems for finding, assessing, and enabling exceptional people. Watch the full episode here: Or subscribe wherever you get podcasts: 🎧 🎤

Joelle Emerson

25,616 Aufrufe • vor 2 Monaten

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 Aufrufe • vor 2 Monaten

ANTHROPIC JUST RELEASED A FREE 23-PAGE GUIDE THAT COMPANIES ARE PAYING $50,000 TO CONSULTANTS FOR. How to build Claude into your entire business infrastructure - 100% FREE. Most companies using AI in 2026 are doing the same thing. Anthropic explains what real enterprise AI infrastructure actually looks like. Not marketing, not a sales deck, just a practical guide from the people who build these systems. What is inside: ↳ How to move Claude from a chat tool to core business infrastructure ↳ How to connect it to your internal databases and live documents ↳ How to design automated workflows for your exact processes ↳ How to handle institutional data safely at scale The most important line in the whole document: The model alone is not enough. The value is in how it connects to your systems, data, and workflows. Save this.
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ANTHROPIC JUST RELEASED A FREE 23-PAGE GUIDE THAT COMPANIES ARE PAYING $50,000 TO CONSULTANTS FOR. How to build Claude into your entire business infrastructure - 100% FREE. Most companies using AI in 2026 are doing the same thing. Anthropic explains what real enterprise AI infrastructure actually looks like. Not marketing, not a sales deck, just a practical guide from the people who build these systems. What is inside: ↳ How to move Claude from a chat tool to core business infrastructure ↳ How to connect it to your internal databases and live documents ↳ How to design automated workflows for your exact processes ↳ How to handle institutional data safely at scale The most important line in the whole document: The model alone is not enough. The value is in how it connects to your systems, data, and workflows. Save this.

Kanika

16,545 Aufrufe • vor 2 Monaten