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a Google researcher walked into MIT and made an AI do math correctly by adding seven words to the prompt. the seven words: "you are an MIT mathematician." drop them, model gets it wrong. add them, right. same model. same question. every time. Carter Smith. runs Gemini at Google.... show more

Raytar

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20,399 次观看 • 2 个月前 •via X (Twitter)

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he tested 5760 architectures at Google for a full year. the winner was the original Transformer from 2017. Hyung Won Chung told that story at MIT with a small smile. then went to OpenAI and trained o1. 1 hour. free. by one of the few people on earth who actually moves the frontier. meanwhile your feed is full of guys writing architecture threads who have never trained a model anyone uses. he just told MIT that 99% of AI research is theater. your AI worldview was built by men who read his papers. badly. now you can read him directly. you will rewatch this. save it now.

he tested 5760 architectures at Google for a full year. the winner was the original Transformer from 2017. Hyung Won Chung told that story at MIT with a small smile. then went to OpenAI and trained o1. 1 hour. free. by one of the few people on earth who actually moves the frontier. meanwhile your feed is full of guys writing architecture threads who have never trained a model anyone uses. he just told MIT that 99% of AI research is theater. your AI worldview was built by men who read his papers. badly. now you can read him directly. you will rewatch this. save it now.

Raytar

107,152 次观看 • 2 个月前

Google rejected my friend three times. One lecture later, they offered him $620k. This is the lecture where Karpathy shows how LLMs actually think, not how to prompt them. He called me right after and said: “This is the first time it actually made sense.” Three days later he applied again. Every question they asked about, he already knew from that video.

Google rejected my friend three times. One lecture later, they offered him $620k. This is the lecture where Karpathy shows how LLMs actually think, not how to prompt them. He called me right after and said: “This is the first time it actually made sense.” Three days later he applied again. Every question they asked about, he already knew from that video.

Raytar

15,299 次观看 • 1 个月前

She said she needs money. He opened Google Maps. Found a burger spot with no website. Gave AI the name and two photos. Fifteen minutes later a full website was sitting on his screen. Called the owner. "I made you a website." The owner said how much. "$1,500." Paid same day. That was a Tuesday. By Friday he'd done it three more times. There are 3 million restaurants on Google Maps right now with no website. All of them need one. None of them know how easy it is to make. He knows. She quit her job within a month.

She said she needs money. He opened Google Maps. Found a burger spot with no website. Gave AI the name and two photos. Fifteen minutes later a full website was sitting on his screen. Called the owner. "I made you a website." The owner said how much. "$1,500." Paid same day. That was a Tuesday. By Friday he'd done it three more times. There are 3 million restaurants on Google Maps right now with no website. All of them need one. None of them know how easy it is to make. He knows. She quit her job within a month.

Raytar

75,304 次观看 • 2 个月前

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

Brian Roemmele

85,386 次观看 • 4 个月前

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

432,845 次观看 • 1 个月前

JUST IN: Google releases Gemini 1.5, a powerful MoE model. It's a huge breakthrough. The model has the longest context window ever seen: 1 million tokens. It can process 1 hour of video, 11 hours of audio, 30,000 lines of code, or 700,000 words in a single prompt. When tested on text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing LLMs. You can can sign up in AI Studio to try it out.

JUST IN: Google releases Gemini 1.5, a powerful MoE model. It's a huge breakthrough. The model has the longest context window ever seen: 1 million tokens. It can process 1 hour of video, 11 hours of audio, 30,000 lines of code, or 700,000 words in a single prompt. When tested on text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing LLMs. You can can sign up in AI Studio to try it out.

Lior Alexander

83,432 次观看 • 2 年前

A roofer in LA paid $1,700 for a website. It took the guy who made it 9 minutes. $11,900 last month. 7 clients. $29 in costs. He finds businesses on Google Maps that have no website. Types one prompt. AI builds the whole site. He sends a link and a price. He had to ask his mom "how to send an invoice" before his first client. He's 19. works maybe 2 hours a week. You're reading this on a phone that can do the same thing.

A roofer in LA paid $1,700 for a website. It took the guy who made it 9 minutes. $11,900 last month. 7 clients. $29 in costs. He finds businesses on Google Maps that have no website. Types one prompt. AI builds the whole site. He sends a link and a price. He had to ask his mom "how to send an invoice" before his first client. He's 19. works maybe 2 hours a week. You're reading this on a phone that can do the same thing.

Raytar

430,479 次观看 • 1 个月前

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Julian Goldie SEO

13,010 次观看 • 6 个月前

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 次观看 • 2 个月前

Co-Founder of a16z (the biggest VC fund in the world) Marc Andreessen: "People who want to build their careers should be spending every spare hour talking to AI: alright, train me up" He says this is the habit that separates who gets paid and who gets replaced In 1 hour 44 minutes, he explains why the people winning right now are training themselves on AI every spare hour Open the model Ask it to teach you Practice Ask again Repeat daily That is the new career ladder Bookmark and watch the interview

Co-Founder of a16z (the biggest VC fund in the world) Marc Andreessen: "People who want to build their careers should be spending every spare hour talking to AI: alright, train me up" He says this is the habit that separates who gets paid and who gets replaced In 1 hour 44 minutes, he explains why the people winning right now are training themselves on AI every spare hour Open the model Ask it to teach you Practice Ask again Repeat daily That is the new career ladder Bookmark and watch the interview

spect

151,386 次观看 • 1 个月前

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Ricardo

47,202 次观看 • 15 天前

Persi Diaconis walked into a lecture at the University of Washington, held up a coin, and told a room of physicists that Richard Feynman was fooled by it his entire life. He was right. In 2007, Diaconis proved a coin flip is not 50/50. It lands on the side it started on about 51 percent of the time. The bias comes from the physics of rotation under gravity. Every physicist since Newton had assumed 50/50 without ever testing it. Every trading model built on that assumption is running on the same lie. The lecture was on Feynman's book "The Meaning of it All." Diaconis quoted the most famous line in it: "the first principle is that you must not fool yourself, and you are the easiest person to fool." Then he pointed out that Feynman himself was fooled by every coin he ever flipped. Feynman's own rule would have killed the 50/50 assumption on day one. The market is the same setup at scale. Every model that assumes independent 50/50 outcomes at the base layer is built on a physical impossibility. Order flow, positioning, forced flows, expiries all leave biases larger than 1 percent. Your gut cannot see them. The math already knows they are there. Diaconis's rule: before you trust a random process, check it. Actually check it. Not with a simulation. With a proof or an experiment. The coin is where you start. The chart is where the same rule pays. The only random thing about markets is how thoroughly you refuse to check them.

Persi Diaconis walked into a lecture at the University of Washington, held up a coin, and told a room of physicists that Richard Feynman was fooled by it his entire life. He was right. In 2007, Diaconis proved a coin flip is not 50/50. It lands on the side it started on about 51 percent of the time. The bias comes from the physics of rotation under gravity. Every physicist since Newton had assumed 50/50 without ever testing it. Every trading model built on that assumption is running on the same lie. The lecture was on Feynman's book "The Meaning of it All." Diaconis quoted the most famous line in it: "the first principle is that you must not fool yourself, and you are the easiest person to fool." Then he pointed out that Feynman himself was fooled by every coin he ever flipped. Feynman's own rule would have killed the 50/50 assumption on day one. The market is the same setup at scale. Every model that assumes independent 50/50 outcomes at the base layer is built on a physical impossibility. Order flow, positioning, forced flows, expiries all leave biases larger than 1 percent. Your gut cannot see them. The math already knows they are there. Diaconis's rule: before you trust a random process, check it. Actually check it. Not with a simulation. With a proof or an experiment. The coin is where you start. The chart is where the same rule pays. The only random thing about markets is how thoroughly you refuse to check them.

veles

18,798 次观看 • 19 天前

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

Gipp 🦅

14,871 次观看 • 2 个月前

Anthropic engineer: "Most people will use Sonnet 5 and Fable 5 wrong. You can set them up right in one afternoon and stop overpaying every single day." In 29 minutes, he breaks down every Claude model, and why running them all the same way is a mistake. Watch the session, then save the config below 👇

Anthropic engineer: "Most people will use Sonnet 5 and Fable 5 wrong. You can set them up right in one afternoon and stop overpaying every single day." In 29 minutes, he breaks down every Claude model, and why running them all the same way is a mistake. Watch the session, then save the config below 👇

darkzodchi

577,792 次观看 • 1 个月前

Google laid him off. So he aimed Google own AI at Bitcoin trading and pulled $400,000 profit. It's called TimesFM - a foundation model for time. Think GPT, but instead of the next word, it predicts the next number. He'd spent four years on the team that builds models like this. Then a spring reorg erased the team and walked him out with a cardboard box. Wallet proof: Here's the part that breaks people: the model was never trained on Bitcoin. Not one candle. Google pretrained TimesFM once on 100 billion time-points - search trends, Wikipedia traffic, demand curves, then froze it. You don't retrain it. You prompt it, the way you prompt GPT. Every five minutes his bot hands it the last few hundred BTC prints. It returns a forecast - not a line, but calibrated quantiles: the 10th through 90th percentile of where the next candle lands. A 30-million-parameter quantile head does nothing but draw that distribution. From it he reads one number: P(close Up). 5min Polymarket contract is that same number, priced by a half-awake crowd: edge = model_p − market_p. Then the filters cut hard. fix_quantile_crossing on, so the distribution stays sane. Skip if the spread's too wide or the edge is under 5¢. Fractional Kelly sizes what's left. Hard daily loss cap, no exceptions. No thesis. No conviction. Just a frozen Google model against a lazy order book, every five minutes, around the clock. His wallet's public, taking the same 5-minute edges right now. Two clicks and every trade it makes lands in yours too:

Google laid him off. So he aimed Google own AI at Bitcoin trading and pulled $400,000 profit. It's called TimesFM - a foundation model for time. Think GPT, but instead of the next word, it predicts the next number. He'd spent four years on the team that builds models like this. Then a spring reorg erased the team and walked him out with a cardboard box. Wallet proof: Here's the part that breaks people: the model was never trained on Bitcoin. Not one candle. Google pretrained TimesFM once on 100 billion time-points - search trends, Wikipedia traffic, demand curves, then froze it. You don't retrain it. You prompt it, the way you prompt GPT. Every five minutes his bot hands it the last few hundred BTC prints. It returns a forecast - not a line, but calibrated quantiles: the 10th through 90th percentile of where the next candle lands. A 30-million-parameter quantile head does nothing but draw that distribution. From it he reads one number: P(close Up). 5min Polymarket contract is that same number, priced by a half-awake crowd: edge = model_p − market_p. Then the filters cut hard. fix_quantile_crossing on, so the distribution stays sane. Skip if the spread's too wide or the edge is under 5¢. Fractional Kelly sizes what's left. Hard daily loss cap, no exceptions. No thesis. No conviction. Just a frozen Google model against a lazy order book, every five minutes, around the clock. His wallet's public, taking the same 5-minute edges right now. Two clicks and every trade it makes lands in yours too:

cvxv666

49,624 次观看 • 1 个月前

From time to time I still have my accent. There are a ton of words I’m constantly pronouncing wrong or can’t say right at all. This man just figured out the same thing. He’s been saying many of the words he uses daily incorrectly. I know it varies by region, from the NYC accent to the southern accent. I think that’s what makes us unique. Have you tried this as well? Are you saying all the words correctly?

From time to time I still have my accent. There are a ton of words I’m constantly pronouncing wrong or can’t say right at all. This man just figured out the same thing. He’s been saying many of the words he uses daily incorrectly. I know it varies by region, from the NYC accent to the southern accent. I think that’s what makes us unique. Have you tried this as well? Are you saying all the words correctly?

SonnyBoy🇺🇸

89,840 次观看 • 2 个月前

Head of Google AI: "75% of our engineers are running dozens or hundreds of agents. The way you do it is Graph Engineering." in 50-minutes, he shows how one engineer can do a whole team’s work, how far this has gone at Google, and what’s next. this is something you can't skip if you don't want to be left behind. watch it, then read the full guide on graph engineering below.

Head of Google AI: "75% of our engineers are running dozens or hundreds of agents. The way you do it is Graph Engineering." in 50-minutes, he shows how one engineer can do a whole team’s work, how far this has gone at Google, and what’s next. this is something you can't skip if you don't want to be left behind. watch it, then read the full guide on graph engineering below.

Codez

74,944 次观看 • 4 天前

Andrej Karpathy spent 8 years at OpenAI and Tesla. Last week he put everything he knows into one free 2-hour lecture. People pay $15k for bootcamps that teach half of this. You probably don't have 2 hours right now. Don't lose this in the feed. Watch it. Then read the guide below and build your first loop.

Andrej Karpathy spent 8 years at OpenAI and Tesla. Last week he put everything he knows into one free 2-hour lecture. People pay $15k for bootcamps that teach half of this. You probably don't have 2 hours right now. Don't lose this in the feed. Watch it. Then read the guide below and build your first loop.

Raytar

416,400 次观看 • 18 天前

CEOs fly to Davos and pay $500,000 for 30 minutes with Jensen Huang. You just got an hour for free. Without Jensen Huang there is no ChatGPT, no Claude, no Gemini, no Kimi, no AI revolution. Every model you've ever used runs on hardware he built. Leather jacket, Stanford lecture hall, One hour explaining the compute behind intelligence, how it's built, what it costs and where it's going. No connections, no conference badge, no $500,000 wire transfer, just a link and 60 minutes. Just the man who made modern AI physically possible giving it all away for free. The article below connects the dots on where this is all heading and why Kimi is worth paying attention to right now.

CEOs fly to Davos and pay $500,000 for 30 minutes with Jensen Huang. You just got an hour for free. Without Jensen Huang there is no ChatGPT, no Claude, no Gemini, no Kimi, no AI revolution. Every model you've ever used runs on hardware he built. Leather jacket, Stanford lecture hall, One hour explaining the compute behind intelligence, how it's built, what it costs and where it's going. No connections, no conference badge, no $500,000 wire transfer, just a link and 60 minutes. Just the man who made modern AI physically possible giving it all away for free. The article below connects the dots on where this is all heading and why Kimi is worth paying attention to right now.

Defileo🔮

1,162,397 次观看 • 2 个月前

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

Stefan.

2,144,039 次观看 • 5 天前