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Anthropic Quant Andrej Karpathy: "Most people use tools that they don't understand - the ones who strip everything down to basics - end up faster than everyone else " "the best code is the code anyone can read " he couldn't fix a bug in 2 hours, so instead... show more

codila

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378,561 次观看 • 27 天前 •via X (Twitter)

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Andrej Karpathy could have charged $10,000 for this course. He put it on YouTube. The man who built Tesla Autopilot from scratch. Co-founded OpenAI. Understands AI at a level most engineers at Google and Meta never reach. Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts. Then dropped it for free. The gap between people who watch it this week and those who save it for later is not 2 hours. It is everything those 2 hours quietly unlock for the rest of your career.

Andrej Karpathy could have charged $10,000 for this course. He put it on YouTube. The man who built Tesla Autopilot from scratch. Co-founded OpenAI. Understands AI at a level most engineers at Google and Meta never reach. Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts. Then dropped it for free. The gap between people who watch it this week and those who save it for later is not 2 hours. It is everything those 2 hours quietly unlock for the rest of your career.

NeilXbt

520,162 次观看 • 2 个月前

Claude Code went from research preview to a $1B product in 6 months. Cowork is the same thing, built for people who don’t code. This free 18-minute video teaches you how to set it up from scratch. No code. No fluff. No $500 upsell. Watch it. Bookmark it. The few who actually do will run their entire week on autopilot:

Claude Code went from research preview to a $1B product in 6 months. Cowork is the same thing, built for people who don’t code. This free 18-minute video teaches you how to set it up from scratch. No code. No fluff. No $500 upsell. Watch it. Bookmark it. The few who actually do will run their entire week on autopilot:

Evan Luthra

31,725 次观看 • 2 个月前

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,399 次观看 • 7 个月前

Anthropic Engineer Andrej Karpathy: "Stop training from scratch. Take what the model already learned - adapt it - ship it At OpenAI we replaced months of work with a few lines of code. Better results every time " the Karpathy formula for working with AI: step 1 → stop building from zero. the model already learned the hard part - just point it at your task step 2 → stop adding complexity, the simplest architecture beat everything in 2014 - simpler always wins step 3 → train only the last layer and freeze everything else - minutes instead of months. better results he taught this at Stanford 10 years ago - then applied it at OpenAI, Tesla, and Anthropic the advice never changed - because it never stopped working watch - bookmark, then read article below ↓

Anthropic Engineer Andrej Karpathy: "Stop training from scratch. Take what the model already learned - adapt it - ship it At OpenAI we replaced months of work with a few lines of code. Better results every time " the Karpathy formula for working with AI: step 1 → stop building from zero. the model already learned the hard part - just point it at your task step 2 → stop adding complexity, the simplest architecture beat everything in 2014 - simpler always wins step 3 → train only the last layer and freeze everything else - minutes instead of months. better results he taught this at Stanford 10 years ago - then applied it at OpenAI, Tesla, and Anthropic the advice never changed - because it never stopped working watch - bookmark, then read article below ↓

codila

24,203 次观看 • 10 天前

OpenAI co-founder Andrej Karpathy: "You literally have to put in 10,000 hours to learn Claude" 11-min workshop from Goat of AI - how he's actually uses it He reveales all the features that he use in this video bookmark & watch - the most prodactive way for your evening

OpenAI co-founder Andrej Karpathy: "You literally have to put in 10,000 hours to learn Claude" 11-min workshop from Goat of AI - how he's actually uses it He reveales all the features that he use in this video bookmark & watch - the most prodactive way for your evening

Morty

118,827 次观看 • 28 天前

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 次观看 • 21 天前

Boris Cherny, the creator of Claude Code at Anthropic, just explained why most people aren't getting real results from Claude in this talk he breaks down exactly how most people skipped the setup entirely: - 90% of code at Anthropic is written by Claude itself - the 14% you lose to CLAUDE.md before typing a word - lawyers, designers, and finance people are building real things with it - no CS degree. no background. nothing Boris Cherny: "Claude Code is 100% written by Claude Code, Cowork is 100% written by Claude Code" if you've been using Claude for months and never configured a single thing, you skipped the 20 steps that change everything. probably 25 instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed my breakdown of all 25 features is below

Boris Cherny, the creator of Claude Code at Anthropic, just explained why most people aren't getting real results from Claude in this talk he breaks down exactly how most people skipped the setup entirely: - 90% of code at Anthropic is written by Claude itself - the 14% you lose to CLAUDE.md before typing a word - lawyers, designers, and finance people are building real things with it - no CS degree. no background. nothing Boris Cherny: "Claude Code is 100% written by Claude Code, Cowork is 100% written by Claude Code" if you've been using Claude for months and never configured a single thing, you skipped the 20 steps that change everything. probably 25 instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed my breakdown of all 25 features is below

Khairallah AL-Awady

182,466 次观看 • 1 个月前

Andrej Karpathy spent 2 hours explaining what most AI educators won't tell you it's about what happens to humans when AI takes over knowledge work Karpathy has been in AI for 20 years. he built GPT-2, co-founded OpenAI, led Tesla Autopilot the part most people skip past: > the AI you're using today can't actually learn > "year of agents" is a marketing phrase. he calls it the decade of agents > coding assistants are great at boilerplate. they fall apart on novel architecture > the bottleneck isn't compute. it's that current models memorize instead of reason > most people open Claude, ask a question, close the tab > the humans who flourish won't be the ones who fear AI. they'll be the ones who stayed curious and kept building mental models right now most people are outsourcing thinking without building the skill underneath Karpathy's answer isn't to slow AI down, it's to build better humans faster he's designing a Starfleet Academy from scratch: AI-assisted, expert-led, built on first principles the goal is "eurekas per second" - how fast can you get someone from zero to genuine understanding full 2-hour conversation is below the education section alone is worth more than most $2,000 courses you've seen in your feed

Andrej Karpathy spent 2 hours explaining what most AI educators won't tell you it's about what happens to humans when AI takes over knowledge work Karpathy has been in AI for 20 years. he built GPT-2, co-founded OpenAI, led Tesla Autopilot the part most people skip past: > the AI you're using today can't actually learn > "year of agents" is a marketing phrase. he calls it the decade of agents > coding assistants are great at boilerplate. they fall apart on novel architecture > the bottleneck isn't compute. it's that current models memorize instead of reason > most people open Claude, ask a question, close the tab > the humans who flourish won't be the ones who fear AI. they'll be the ones who stayed curious and kept building mental models right now most people are outsourcing thinking without building the skill underneath Karpathy's answer isn't to slow AI down, it's to build better humans faster he's designing a Starfleet Academy from scratch: AI-assisted, expert-led, built on first principles the goal is "eurekas per second" - how fast can you get someone from zero to genuine understanding full 2-hour conversation is below the education section alone is worth more than most $2,000 courses you've seen in your feed

rewind

124,743 次观看 • 1 个月前

Anthropic's Andrej Karpathy as Google intern and Stanford student: "At Google I work on AI i don't fully understand it, but that's exactly why I'm working on it " he filmed a vlog from inside Google and Stanford - biking past Steve Jobs house on the way to work years later that same "AI thing " he couldn't fully explain became - Tesla Autopilot, OpenAI, and Claude he bet his career on a problem nobody had solved - and grew into the field he helped define the lesson: work on what you don't understand yet - that's where the future gets built Stanford → Google → Tesla → OpenAI → Anthropic bookmark and watch this most special vlog!

Anthropic's Andrej Karpathy as Google intern and Stanford student: "At Google I work on AI i don't fully understand it, but that's exactly why I'm working on it " he filmed a vlog from inside Google and Stanford - biking past Steve Jobs house on the way to work years later that same "AI thing " he couldn't fully explain became - Tesla Autopilot, OpenAI, and Claude he bet his career on a problem nobody had solved - and grew into the field he helped define the lesson: work on what you don't understand yet - that's where the future gets built Stanford → Google → Tesla → OpenAI → Anthropic bookmark and watch this most special vlog!

codila

340,683 次观看 • 8 天前

Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in this interview he breaks down exactly how a system changes everything: - the memory and context features that turn Claude into a second brain - the knowledge architecture most users don't know Claude can build - the integration layer that connects Claude to your actual workflow - why typing one question at a time is the most expensive way to use Claude if you've been using Claude for months and still start every conversation from scratch with zero context, you don't have a Claude problem. you have a system problem instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed full guide in the article below

Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in this interview he breaks down exactly how a system changes everything: - the memory and context features that turn Claude into a second brain - the knowledge architecture most users don't know Claude can build - the integration layer that connects Claude to your actual workflow - why typing one question at a time is the most expensive way to use Claude if you've been using Claude for months and still start every conversation from scratch with zero context, you don't have a Claude problem. you have a system problem instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed full guide in the article below

Khairallah AL-Awady

39,176 次观看 • 1 个月前

Boris Cherny, the creator of Claude Code at Anthropic, just explained how he runs thousands of agents from his phone while he sleeps in this talk he breaks down exactly how he manages thousands of agents from his phone: - the 14% you lose to CLAUDE.md before typing a word - the architecture behind running a few thousand agents overnight - why the system matters more than the tool - the knowledge structure that makes everything findable, connected, and useful if you've been using Claude for months and every conversation still starts from scratch with zero memory and zero context, you don't have a system. you have a chat history instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Boris Cherny, the creator of Claude Code at Anthropic, just explained how he runs thousands of agents from his phone while he sleeps in this talk he breaks down exactly how he manages thousands of agents from his phone: - the 14% you lose to CLAUDE.md before typing a word - the architecture behind running a few thousand agents overnight - why the system matters more than the tool - the knowledge structure that makes everything findable, connected, and useful if you've been using Claude for months and every conversation still starts from scratch with zero memory and zero context, you don't have a system. you have a chat history instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady

86,147 次观看 • 1 个月前

Karpathy just said the people who don't use LLMs are already losing he spent 4 minutes explaining why smart people are still going to fall behind not only the people who refuse AI, but also those who think signing up for Claude counts as using it typing a prompt and reading what comes back isn't the skill, anyone can do that the real shift is going from asking AI things to building something that runs without you that's exactly why I wrote an article on Claude features most people don't even know exist read the article below and you'll already be ahead of 99% of people

Karpathy just said the people who don't use LLMs are already losing he spent 4 minutes explaining why smart people are still going to fall behind not only the people who refuse AI, but also those who think signing up for Claude counts as using it typing a prompt and reading what comes back isn't the skill, anyone can do that the real shift is going from asking AI things to building something that runs without you that's exactly why I wrote an article on Claude features most people don't even know exist read the article below and you'll already be ahead of 99% of people

Anatoli Kopadze

1,209,369 次观看 • 26 天前

Anthropic's CEO just leaked the most INSANE revenue numbers in AI history. And what he said about the next 12 months will change how you think about every business decision you're making right now. Dario Amodei told Dwarkesh Patel on the interview that Anthropic went from: - 2023: $0 to $100M - 2024: $100M to $1B - 2025: $1B to $9-10B That's 10x revenue growth. Every. Single. Year. "In January alone, we added another few billion to revenue." One month. A few billion dollars. Think about what that means. Most companies would kill for $1B in annual revenue. Anthropic added multiple billions in 30 days. But Dario said something even more interesting: "We are near the end of the exponential." Not the end of AI progress. The end of people understanding how close we actually are. His exact words: "It is absolutely wild that you have people talking about the same tired political issues, when we are near the end of the exponential." What does "end of the exponential" mean? In 1-3 years, we get what he calls a "country of geniuses in a data center." AI systems that can: - Do end-to-end software engineering - Navigate any computer interface - Learn new skills like humans do - Replace entire categories of knowledge work And here's the contradiction: If Anthropic really believed this was 1-3 years away, why aren't they buying $1 trillion in compute? Dario's answer exposes the real game: "If you're off by only a year in your prediction, you go bankrupt." So even the CEO who's most bullish on AI timelines is hedging. He's buying hundreds of billions in compute. Not trillions. Because the gap between "AI can do the job" and "companies actually pay for it" is massive. He calls it "economic diffusion." I call it the gap that's going to make some people very rich and destroy everyone who ignores it. The models are already better than people think. Claude Code writes 90% of code at Anthropic right now. But Dario says there's a huge difference between: - 90% of code written by AI - 100% of code written by AI - 90% of end-to-end SWE tasks done by AI - 100% of end-to-end SWE tasks done by AI We're moving through that spectrum "very quickly." His prediction: FULL end-to-end software engineering in 1-2 years. But here's what's scary: The technology is advancing faster than anyone outside the AI labs understands. And the revenue is following faster than any technology in history. But it's still not instant. Dario expects 10-20% annual GDP growth. Not 300%. Which means we're in this weird middle zone: Fast enough to destroy unprepared businesses. Slow enough that most people are ignoring it. Dario's big takeaway: If you're running a business right now, you have maybe 12-18 months to figure out how AI changes your model. Not to "add AI features." To fundamentally rethink what you're selling and who can do the work. Because the companies that get this right will 10x. And the ones that don't will be explaining to investors why revenue is flat while everyone else is printing money. The exponential is ending. But most people literally still don't even know it started.

Anthropic's CEO just leaked the most INSANE revenue numbers in AI history. And what he said about the next 12 months will change how you think about every business decision you're making right now. Dario Amodei told Dwarkesh Patel on the interview that Anthropic went from: - 2023: $0 to $100M - 2024: $100M to $1B - 2025: $1B to $9-10B That's 10x revenue growth. Every. Single. Year. "In January alone, we added another few billion to revenue." One month. A few billion dollars. Think about what that means. Most companies would kill for $1B in annual revenue. Anthropic added multiple billions in 30 days. But Dario said something even more interesting: "We are near the end of the exponential." Not the end of AI progress. The end of people understanding how close we actually are. His exact words: "It is absolutely wild that you have people talking about the same tired political issues, when we are near the end of the exponential." What does "end of the exponential" mean? In 1-3 years, we get what he calls a "country of geniuses in a data center." AI systems that can: - Do end-to-end software engineering - Navigate any computer interface - Learn new skills like humans do - Replace entire categories of knowledge work And here's the contradiction: If Anthropic really believed this was 1-3 years away, why aren't they buying $1 trillion in compute? Dario's answer exposes the real game: "If you're off by only a year in your prediction, you go bankrupt." So even the CEO who's most bullish on AI timelines is hedging. He's buying hundreds of billions in compute. Not trillions. Because the gap between "AI can do the job" and "companies actually pay for it" is massive. He calls it "economic diffusion." I call it the gap that's going to make some people very rich and destroy everyone who ignores it. The models are already better than people think. Claude Code writes 90% of code at Anthropic right now. But Dario says there's a huge difference between: - 90% of code written by AI - 100% of code written by AI - 90% of end-to-end SWE tasks done by AI - 100% of end-to-end SWE tasks done by AI We're moving through that spectrum "very quickly." His prediction: FULL end-to-end software engineering in 1-2 years. But here's what's scary: The technology is advancing faster than anyone outside the AI labs understands. And the revenue is following faster than any technology in history. But it's still not instant. Dario expects 10-20% annual GDP growth. Not 300%. Which means we're in this weird middle zone: Fast enough to destroy unprepared businesses. Slow enough that most people are ignoring it. Dario's big takeaway: If you're running a business right now, you have maybe 12-18 months to figure out how AI changes your model. Not to "add AI features." To fundamentally rethink what you're selling and who can do the work. Because the companies that get this right will 10x. And the ones that don't will be explaining to investors why revenue is flat while everyone else is printing money. The exponential is ending. But most people literally still don't even know it started.

Ricardo

62,002 次观看 • 5 个月前

anthropic's head of product just revealed how they're able to ship faster than any other AI company. their secret: "side quest maxxing." here's how it works: instead of long-term roadmaps, anthropic runs on unplanned afternoon experiments. anyone on the team gets full freedom to spend an afternoon prototyping an idea and show it to the team. you get to skip the approval process entirely. then, employees at anthropic try it. if they keep using it the next day and the day after that, it gets polished into a real feature. if nobody touches it again, it dies. that's the whole process. claude code on desktop started as one engineer's afternoon project. he wanted it to work on desktop so he built a prototype. people on the team started using it immediately. so they shipped it. the todo list feature started the same way. someone built it, the team adopted it internally, and it became one of the most-used parts of the product. plugins started when one engineer shared a spec with claude code and the prototype that came back was close to production-ready. went from idea to working feature in a single session. they also killed standup meetings. instead of telling people what you're working on, you just show a working demo. all walk no talk basically the team structure makes this possible. > designers ship code. > engineers make product decisions. > product managers build prototypes. everyone can take an idea from concept to working demo without waiting on anyone else. the biggest features at a $380b company came from afternoon experiments that nobody asked for. honestly this matches my own experience cooking with ai. some of the best workflows i use every day came from just fucking around. opening a session with zero intention and asking claude what it can do, or jamming on a random idea to see where it goes. if you're only using ai for tasks you already have in mind, you're missing the best part. open a session with no agenda. ask it to surprise you. try building something stupid. half the time it goes nowhere. the other half it becomes the thing you use most. you need to be sidequestmaxxing.

anthropic's head of product just revealed how they're able to ship faster than any other AI company. their secret: "side quest maxxing." here's how it works: instead of long-term roadmaps, anthropic runs on unplanned afternoon experiments. anyone on the team gets full freedom to spend an afternoon prototyping an idea and show it to the team. you get to skip the approval process entirely. then, employees at anthropic try it. if they keep using it the next day and the day after that, it gets polished into a real feature. if nobody touches it again, it dies. that's the whole process. claude code on desktop started as one engineer's afternoon project. he wanted it to work on desktop so he built a prototype. people on the team started using it immediately. so they shipped it. the todo list feature started the same way. someone built it, the team adopted it internally, and it became one of the most-used parts of the product. plugins started when one engineer shared a spec with claude code and the prototype that came back was close to production-ready. went from idea to working feature in a single session. they also killed standup meetings. instead of telling people what you're working on, you just show a working demo. all walk no talk basically the team structure makes this possible. > designers ship code. > engineers make product decisions. > product managers build prototypes. everyone can take an idea from concept to working demo without waiting on anyone else. the biggest features at a $380b company came from afternoon experiments that nobody asked for. honestly this matches my own experience cooking with ai. some of the best workflows i use every day came from just fucking around. opening a session with zero intention and asking claude what it can do, or jamming on a random idea to see where it goes. if you're only using ai for tasks you already have in mind, you're missing the best part. open a session with no agenda. ask it to surprise you. try building something stupid. half the time it goes nowhere. the other half it becomes the thing you use most. you need to be sidequestmaxxing.

Ole Lehmann

105,994 次观看 • 2 个月前

JENSEN HUANG WAS ASKED WHO IS USING AI BETTER THAN ANYONE ELSE IN THE WORLD His answer wasn't OpenAI. It wasn't Google. It wasn't even Nvidia itself. Here's what he said on CNBC: "Nobody uses AI better than Meta." He explained that Meta went from a classical recommender system running on CPUs to a generative AI agentic system making recommendations across the entire platform. "Everything from the way social media works and the way they recommend ads and help advertisers create content has fundamentally been changed. And their earnings show it." "That's the reason why they're investing so hard. They see a much larger future potential for it." Mark Zuckerberg $META is spending $70B+ on AI this year.

JENSEN HUANG WAS ASKED WHO IS USING AI BETTER THAN ANYONE ELSE IN THE WORLD His answer wasn't OpenAI. It wasn't Google. It wasn't even Nvidia itself. Here's what he said on CNBC: "Nobody uses AI better than Meta." He explained that Meta went from a classical recommender system running on CPUs to a generative AI agentic system making recommendations across the entire platform. "Everything from the way social media works and the way they recommend ads and help advertisers create content has fundamentally been changed. And their earnings show it." "That's the reason why they're investing so hard. They see a much larger future potential for it." Mark Zuckerberg $META is spending $70B+ on AI this year.

WOLF

724,740 次观看 • 1 个月前

THIS IS ANDREJ KARPATHY'S OBSIDIAN VAULT : THE BRAIN ANTHROPIC JUST HIRED FOR MILLIONS This is every idea, every decision and every connection he's made over years of work, visualized in real time. Everything else gets lost. The pattern you noticed last March. The connection between two problems you solved a year apart. The idea that would've changed everything if you'd remembered it in the room. This system keeps all of it. Every note links to another. Every link becomes a path. Every path is a decision he can retrace in seconds while everyone else starts from zero. Thousands of nodes. Hundreds of live connections. Years of thinking that never decayed, never died in a Slack thread, never got buried in a meeting nobody remembers. The breakthroughs aren't luck. They're the system handing him back a link he made eighteen months ago and forgot he had. No team. No wiki. No standups. One person and a tool that remembers everything he can't. Most people open Obsidian and make three notes they never open again. He built an operating system for his own thinking. Anthropic just paid millions for the output - this is the machine that produced it.

THIS IS ANDREJ KARPATHY'S OBSIDIAN VAULT : THE BRAIN ANTHROPIC JUST HIRED FOR MILLIONS This is every idea, every decision and every connection he's made over years of work, visualized in real time. Everything else gets lost. The pattern you noticed last March. The connection between two problems you solved a year apart. The idea that would've changed everything if you'd remembered it in the room. This system keeps all of it. Every note links to another. Every link becomes a path. Every path is a decision he can retrace in seconds while everyone else starts from zero. Thousands of nodes. Hundreds of live connections. Years of thinking that never decayed, never died in a Slack thread, never got buried in a meeting nobody remembers. The breakthroughs aren't luck. They're the system handing him back a link he made eighteen months ago and forgot he had. No team. No wiki. No standups. One person and a tool that remembers everything he can't. Most people open Obsidian and make three notes they never open again. He built an operating system for his own thinking. Anthropic just paid millions for the output - this is the machine that produced it.

Avid

64,799 次观看 • 1 个月前

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.

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.

Dustin

82,737 次观看 • 3 个月前

INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 3 hours with this. Claude Code FULL COURSE that teaches you how to BUILD apps, teams, and anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and Bookmark it now.

INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 3 hours with this. Claude Code FULL COURSE that teaches you how to BUILD apps, teams, and anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and Bookmark it now.

Khairallah AL-Awady

231,107 次观看 • 3 个月前

Andrej Karpathy (Founding Member of OpenAI): "you literally have to put in 10,000 hours" to get good at AI he gave out his 10,000 hours of learning. for free. this GitHub repo is the only guide you need to build your own ChatGPT from scratch. This will teach you more than most AI courses at universities teach in a year. Watch this video and save the build below no matter what. It will be the most productive thing to do this entire weekend.

Andrej Karpathy (Founding Member of OpenAI): "you literally have to put in 10,000 hours" to get good at AI he gave out his 10,000 hours of learning. for free. this GitHub repo is the only guide you need to build your own ChatGPT from scratch. This will teach you more than most AI courses at universities teach in a year. Watch this video and save the build below no matter what. It will be the most productive thing to do this entire weekend.

Avid

101,202 次观看 • 18 天前

Anthropic pays $750,000 a year for engineers who can build LLMs from scratch. Not prompt engineers. Not fine-tuners. Not RAG specialists. Engineers who understand the architecture well enough to build the thing from zero. Stanford just put a 2-hour lecture on exactly that on the internet for free. Scaling laws. Data collection. Architecture design. Post-training alignment. The theoretical foundation behind every model you use daily taught by the people who research it at one of the best CS programs on earth. Free. From Stanford. Two hours. The engineers earning $750,000 at Anthropic understand everything in this lecture. Most people using AI every day understand none of it. Watch it tonight. Full implementation guide in the article below.

Anthropic pays $750,000 a year for engineers who can build LLMs from scratch. Not prompt engineers. Not fine-tuners. Not RAG specialists. Engineers who understand the architecture well enough to build the thing from zero. Stanford just put a 2-hour lecture on exactly that on the internet for free. Scaling laws. Data collection. Architecture design. Post-training alignment. The theoretical foundation behind every model you use daily taught by the people who research it at one of the best CS programs on earth. Free. From Stanford. Two hours. The engineers earning $750,000 at Anthropic understand everything in this lecture. Most people using AI every day understand none of it. Watch it tonight. Full implementation guide in the article below.

CyrilXBT

36,692 次观看 • 29 天前