Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

2.8 trillion parameters. Number one on frontend code arena. The largest open-weight model ever. Sputnik moment? It’s totally mind boggling that Elon Musk can spend $16 billion on Colossus 2 for a 20 trillion parameter model, and there's a 1% cost distilled version that’s coming out of China within... show more

Dave Blundin

10,837 subscribers

11,513 views • 16 days ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

101,398 views • 10 days ago

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Aakash Gupta

508,149 views • 3 months ago

This is us trying to record any video we do 😂 But seriously, this experience has meant the world to us, and now we get to give it our all, one last time. Our final dance will be a celebration of this journey and it’s a thank you to YOU! We are so grateful that you’ve voted to keep us here week after week. It’s going to be a special week, see you for the Finale!

This is us trying to record any video we do 😂 But seriously, this experience has meant the world to us, and now we get to give it our all, one last time. Our final dance will be a celebration of this journey and it’s a thank you to YOU! We are so grateful that you’ve voted to keep us here week after week. It’s going to be a special week, see you for the Finale!

Robert Irwin

83,660 views • 8 months ago

Didn’t get a chance to improve the procedural cloud shader yet, but I should this week. ☁️ Here’s another look. It’s still R&D and should be out for free on Gumroad next week if all goes well.✨ I might make a more advanced paid version later, but that’s not the goal right now. #b3d

Didn’t get a chance to improve the procedural cloud shader yet, but I should this week. ☁️ Here’s another look. It’s still R&D and should be out for free on Gumroad next week if all goes well.✨ I might make a more advanced paid version later, but that’s not the goal right now. #b3d

Thomas Marcos

15,258 views • 1 year ago

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 views • 17 days ago

Trump wants the Pentagon budget increased by 50% for next year. That’s $1.5 trillion. We just found out this week that the Pentagon wasted $93 BILLION IN ONE MONTH on things like shellfish, parties and a piano, and you think we’re going to give them more money to waste? Barrels of NO.

Trump wants the Pentagon budget increased by 50% for next year. That’s $1.5 trillion. We just found out this week that the Pentagon wasted $93 BILLION IN ONE MONTH on things like shellfish, parties and a piano, and you think we’re going to give them more money to waste? Barrels of NO.

Dittie

12,947 views • 4 months ago

NEW: Elon Musk says he thinks he can reduce the deficit by $1 Trillion next year, says it will cause a cascading effect for Americans. “I think we can take a trillion dollars out of the out of the deficit next year.” “We have a $2 trillion deficit, which is a far in excess of economic growth. If we can get that deficit in half, from 2 trillion to 1 trillion, and we can get the economic growth to match that 1 trillion growth in the money supply, that means there will be no inflation.” “Interest rates will drop, credit card interest down, mortgage interest down, car payment interest down, and prices at the store stay the same.” “That is a great outcome for people and I think we can do it.”

NEW: Elon Musk says he thinks he can reduce the deficit by $1 Trillion next year, says it will cause a cascading effect for Americans. “I think we can take a trillion dollars out of the out of the deficit next year.” “We have a $2 trillion deficit, which is a far in excess of economic growth. If we can get that deficit in half, from 2 trillion to 1 trillion, and we can get the economic growth to match that 1 trillion growth in the money supply, that means there will be no inflation.” “Interest rates will drop, credit card interest down, mortgage interest down, car payment interest down, and prices at the store stay the same.” “That is a great outcome for people and I think we can do it.”

Collin Rugg

4,342,922 views • 1 year ago

You don't need a trillion-parameter model that understands physics to answer a customer support call. But that's not even the main reason we build small models. The real one: LLMs store everything. Literally everything all of it sitting in the weights. Humans don't work that way, and your agent shouldn't either. Full podcast, link in thread.

You don't need a trillion-parameter model that understands physics to answer a customer support call. But that's not even the main reason we build small models. The real one: LLMs store everything. Literally everything all of it sitting in the weights. Humans don't work that way, and your agent shouldn't either. Full podcast, link in thread.

Sudarshan Kamath

19,885 views • 10 days ago

“We spent $16 trillion on the lockdowns. We got nothing for it. $8 trillion on the Ukraine war. That’s $24 trillion that they had to print to pay for nothing. That money, the way they’re paying it back, it’s a hidden tax called inflation, and it hits the poor and the middle class” “We need to stop being an empire” When’s the last time you heard a Democrat coherently explain the harms of printing money and empire-building?

“We spent $16 trillion on the lockdowns. We got nothing for it. $8 trillion on the Ukraine war. That’s $24 trillion that they had to print to pay for nothing. That money, the way they’re paying it back, it’s a hidden tax called inflation, and it hits the poor and the middle class” “We need to stop being an empire” When’s the last time you heard a Democrat coherently explain the harms of printing money and empire-building?

Liam McCollum

8,825,201 views • 3 years ago

One of the new Lutonians being open that he came over on a dinghy because of all the free stuff. But he said “it’s not enough for them”. Imagine thinking these are refugees fleeing war, madness.

One of the new Lutonians being open that he came over on a dinghy because of all the free stuff. But he said “it’s not enough for them”. Imagine thinking these are refugees fleeing war, madness.

I Am British 🇬🇧

66,820 views • 9 months ago

Then came one of the biggest financial updates of the meeting...from Treasury Secretary Scott Bessent. What he revealed was staggering. Bessent said that America is now pulling in RECORD tariff revenues...between $500 BILLION and $1 TRILLION a year...and the budget deficit is already down 26% from Biden’s last year in office. These are not small numbers. Bessent explained how Trump flipped the system on its head. The days of unbalanced international trade are over, and with that comes a massive influx of cash directly affecting America’s deficit. “On the international front you have a leveled the international trading system whereby countries took advantage of us and that’s over. It’s OVER.” “The Treasury Department is in is taking in record tariff revenues that I had been saying was running at a rate of 300 billion a year.” “You chastised me for saying that—number is too low. And as usual, you we’re right, that we had a substantial, jump from July to August, and I think we’re going to see a bigger jump from August to September.” “So I think we could be on our way, well, over half a trillion, maybe the, towards the trillion dollar number.” “This administration, your administration, has made a meaningful dent in the budget deficit.” Even the CBO, which often clashes with the Trump administration, had to concede: the deficit is projected to drop by $4 trillion over the next decade. “The average budget deficit during this term is 26% less than the last 12 months under Biden and even the CBO, and we don’t agree with CBO on everything as you said, last Friday—had to admit that they believe over the next ten years the budget deficit will be 4 trillion lower than they had previously scored.” And according to Bessent, that number could go even higher. “4 trillion: 3.3 trillion of tariff income, 700 billion of lower interest cost, and, you know, I would expect that that number could go up from here.”

Then came one of the biggest financial updates of the meeting...from Treasury Secretary Scott Bessent. What he revealed was staggering. Bessent said that America is now pulling in RECORD tariff revenues...between $500 BILLION and $1 TRILLION a year...and the budget deficit is already down 26% from Biden’s last year in office. These are not small numbers. Bessent explained how Trump flipped the system on its head. The days of unbalanced international trade are over, and with that comes a massive influx of cash directly affecting America’s deficit. “On the international front you have a leveled the international trading system whereby countries took advantage of us and that’s over. It’s OVER.” “The Treasury Department is in is taking in record tariff revenues that I had been saying was running at a rate of 300 billion a year.” “You chastised me for saying that—number is too low. And as usual, you we’re right, that we had a substantial, jump from July to August, and I think we’re going to see a bigger jump from August to September.” “So I think we could be on our way, well, over half a trillion, maybe the, towards the trillion dollar number.” “This administration, your administration, has made a meaningful dent in the budget deficit.” Even the CBO, which often clashes with the Trump administration, had to concede: the deficit is projected to drop by $4 trillion over the next decade. “The average budget deficit during this term is 26% less than the last 12 months under Biden and even the CBO, and we don’t agree with CBO on everything as you said, last Friday—had to admit that they believe over the next ten years the budget deficit will be 4 trillion lower than they had previously scored.” And according to Bessent, that number could go even higher. “4 trillion: 3.3 trillion of tariff income, 700 billion of lower interest cost, and, you know, I would expect that that number could go up from here.”

The Vigilant Fox 🦊

238,588 views • 11 months ago

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.

Milk Road AI

200,440 views • 3 months ago

“There are two crypto assets worth more than $100 billion: BTC and ETH. In many ways, Ethereum is in a unique position. Ethereum has had a lot of success. The problem is, it had that success five years ago… so the market is saying “you won round one, but what’s round two?” So internally, our prompt has been “what does it take for ETH to become a trillion dollar asset?” There aren’t many individual trillion dollar assets on earth, so what does it take for ETH to be a trillion dollar asset? For Ethlabs, that will be one of our north stars. We’d be a bit full of ourselves if we came out and said that we had the answer today; it’s going to be a huge project for the entire community. But Ethlabs can act as the bridge. We can be the entity that goes and talks to the people that want to take on that mission. Then we can go and talk to the people that help steward Ethereum and work together to make that outcome happen.” — ansgar.eth talks Ethlabs and a trillion dollar Ethereum on Bankless.

“There are two crypto assets worth more than $100 billion: BTC and ETH. In many ways, Ethereum is in a unique position. Ethereum has had a lot of success. The problem is, it had that success five years ago… so the market is saying “you won round one, but what’s round two?” So internally, our prompt has been “what does it take for ETH to become a trillion dollar asset?” There aren’t many individual trillion dollar assets on earth, so what does it take for ETH to be a trillion dollar asset? For Ethlabs, that will be one of our north stars. We’d be a bit full of ourselves if we came out and said that we had the answer today; it’s going to be a huge project for the entire community. But Ethlabs can act as the bridge. We can be the entity that goes and talks to the people that want to take on that mission. Then we can go and talk to the people that help steward Ethereum and work together to make that outcome happen.” — ansgar.eth talks Ethlabs and a trillion dollar Ethereum on Bankless.

dreamsofdefi

38,401 views • 1 month ago

A reminder for you today to be all in! Give God your whole heart, soul, and mind. It’s on the other side of all in that you’ll find the fullness of what He has for you. 🤍

A reminder for you today to be all in! Give God your whole heart, soul, and mind. It’s on the other side of all in that you’ll find the fullness of what He has for you. 🤍

CeCe Winans

30,885 views • 1 year ago

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Mikli

85,206 views • 5 months ago

Anthropic built a model so powerful they can’t afford to release it. their own leaked blog says Capybara scores “dramatically higher” than Opus 4.6 in coding, reasoning, and cybersecurity. a tier above anything they’ve ever shipped. Dario predicted $10 billion training runs by 2026. Capybara is also rumored to be a 10 trillion parameter model. if this is true, it’s gonna be the most expensive AI ever built. the catch? it’s so compute intensive that Anthropic themselves say it’s “very expensive for us to serve, and will be very expensive for our customers to use.” they didn’t build Jarvis. they built Ultron and now they’re figuring out how to let us near it without going broke or breaking the internet.

Anthropic built a model so powerful they can’t afford to release it. their own leaked blog says Capybara scores “dramatically higher” than Opus 4.6 in coding, reasoning, and cybersecurity. a tier above anything they’ve ever shipped. Dario predicted $10 billion training runs by 2026. Capybara is also rumored to be a 10 trillion parameter model. if this is true, it’s gonna be the most expensive AI ever built. the catch? it’s so compute intensive that Anthropic themselves say it’s “very expensive for us to serve, and will be very expensive for our customers to use.” they didn’t build Jarvis. they built Ultron and now they’re figuring out how to let us near it without going broke or breaking the internet.

sui

1,177,685 views • 4 months ago

When Google first showed up, it was completely free—and we cheered. We willingly traded our privacy and our kids' attention for shiny technology, convincing ourselves there was no hidden cost. Now, China is using that exact same playbook. A company in Beijing just released a 2.8-trillion parameter AI model for free, and we’re about to fall for the trap all over again. When the tech is free, you aren't the customer—you are the product. And this time, the stakes aren't just targeted ads or data harvesting. Whoever builds the AI engine that the future runs on sets the defaults. If our schools, hospitals, and infrastructure start running on a baseline built by China, they won't need troops to conquer us. They won't burn books or send monsters to our doors—they'll just quietly administrate what information we see and what gets left out of the history books.

When Google first showed up, it was completely free—and we cheered. We willingly traded our privacy and our kids' attention for shiny technology, convincing ourselves there was no hidden cost. Now, China is using that exact same playbook. A company in Beijing just released a 2.8-trillion parameter AI model for free, and we’re about to fall for the trap all over again. When the tech is free, you aren't the customer—you are the product. And this time, the stakes aren't just targeted ads or data harvesting. Whoever builds the AI engine that the future runs on sets the defaults. If our schools, hospitals, and infrastructure start running on a baseline built by China, they won't need troops to conquer us. They won't burn books or send monsters to our doors—they'll just quietly administrate what information we see and what gets left out of the history books.

Glenn Beck

76,952 views • 1 day ago

Elon Musk's mandate when he came in was DOGE, which has in a 100 days, become a brand in America. He was brought in to save $2 trillion and Trump wants to get this bill done and it doesn’t reduce the deficit. Remember, America's spending prior, even in Trump's first term, was under a trillion dollars' deficit. Now we're at two trillion plus, and both at Biden and the Trump administration are on a two trillion plus deficit. That frustrated Musk. We should really focus on the signal, not the noise. These are two men that are very important to each other. Richest man on earth, including all of the competitiveness that Musk has brought to the American nation. Does Trump really want not to have that in his portfolio? They've got to work this thing out, but really it's about two men that don't agree on how to spend money. These two have to kiss and make up for the benefit of America.

Kevin O'Leary aka Mr. Wonderful

345,104 views • 1 year ago

When we interviewed Will Stancil on the pod last week, he said it was the first time he has been hopeful in the Trump Era: “They can’t scare us in to stopping, and because of that they just can’t win.” A week later, it’s over. Total Stancil victory.

When we interviewed Will Stancil on the pod last week, he said it was the first time he has been hopeful in the Trump Era: “They can’t scare us in to stopping, and because of that they just can’t win.” A week later, it’s over. Total Stancil victory.

Tyler Austin Harper

645,322 views • 6 months ago

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

43,772 views • 2 days ago