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before you watch the World Cup final tonight, watch this 30-minute interview with Kimi founder Zhilin Yang it's the clearest look I've seen at how K2 was actually built and where AI models go next > why compute scaling still beats every other method > why the "data flywheel"... show more

Kirill

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534,038 просмотров • 1 месяц назад •via X (Twitter)

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Nobody expected the largest AI model ever built to be free. Kimi K3 just shipped at 2.8 trillion parameters. Full weights go public in ten days. No API waitlist. No enterprise sales call. No usage tier. Closed labs spent two years selling "frontier intelligence" as something you rent. Open source just proved you can own it outright. The gap everyone bet on didn't shrink. It closed.

Nobody expected the largest AI model ever built to be free. Kimi K3 just shipped at 2.8 trillion parameters. Full weights go public in ten days. No API waitlist. No enterprise sales call. No usage tier. Closed labs spent two years selling "frontier intelligence" as something you rent. Open source just proved you can own it outright. The gap everyone bet on didn't shrink. It closed.

Shawn Chauhan

51,064 просмотров • 1 месяц назад

Moonshot released Kimi K3 as an open-weight model. Most people saw “2.8 trillion parameters.” Far fewer understand what was actually built. We broke K3 into six visual explainers, simple enough for anyone to follow and detailed enough for engineers. Every video was generated in one shot by Simi, the fastest infra to generate explainer video in the entire world. Brought to you by Lamina Labs. Part 1/6: What Kimi K3 actually is.

Moonshot released Kimi K3 as an open-weight model. Most people saw “2.8 trillion parameters.” Far fewer understand what was actually built. We broke K3 into six visual explainers, simple enough for anyone to follow and detailed enough for engineers. Every video was generated in one shot by Simi, the fastest infra to generate explainer video in the entire world. Brought to you by Lamina Labs. Part 1/6: What Kimi K3 actually is.

Sudip

19,970 просмотров • 25 дней назад

Kimi CEO Zhilin Yang: "Claude didn't win on reasoning - they bet everything on agents but the layer everyone skips - a great agent needs a great base model, that's all we do at Kimi 3 " in 90-min workshop he explains why the smartest agent still fails - if you can't configure it correctly his one big idea: most people are still solving the old one "the real goal? we want K2 to help build K3 - without agent skills, that's impossible" watch & bookmark - then learn the article on best agent system ↓

Kimi CEO Zhilin Yang: "Claude didn't win on reasoning - they bet everything on agents but the layer everyone skips - a great agent needs a great base model, that's all we do at Kimi 3 " in 90-min workshop he explains why the smartest agent still fails - if you can't configure it correctly his one big idea: most people are still solving the old one "the real goal? we want K2 to help build K3 - without agent skills, that's impossible" watch & bookmark - then learn the article on best agent system ↓

codila

1,224,576 просмотров • 1 месяц назад

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Md Ismail Šojal 🕷️

60,713 просмотров • 1 месяц назад

Kimi CEO Zhilin Yang nailed it: "Every AI lab like Anthropic thinks the model is what matters most. That's wrong. It's how you organize the people building it that wins. And that's about Kimi 3.” In this keynote, Moonshot AI founder breaks down exactly how to stop treating the model as the only thing that matters and start winning through superior organization instead. The key? Long context is basically the AI era’s version of RAM - the same jump from 128K to gigabytes, just compressed into a couple of years instead of forty. He traces the idea back to a 1970s Intel chip nobody wanted to buy to show that your biggest advantage, and perhaps your only advantage, is your organization. One of the clearest, most practical frameworks for AI strategy and org design I’ve seen. Instead of another Netflix series tonight, watch this talk.

Kimi CEO Zhilin Yang nailed it: "Every AI lab like Anthropic thinks the model is what matters most. That's wrong. It's how you organize the people building it that wins. And that's about Kimi 3.” In this keynote, Moonshot AI founder breaks down exactly how to stop treating the model as the only thing that matters and start winning through superior organization instead. The key? Long context is basically the AI era’s version of RAM - the same jump from 128K to gigabytes, just compressed into a couple of years instead of forty. He traces the idea back to a 1970s Intel chip nobody wanted to buy to show that your biggest advantage, and perhaps your only advantage, is your organization. One of the clearest, most practical frameworks for AI strategy and org design I’ve seen. Instead of another Netflix series tonight, watch this talk.

Linas Beliūnas

30,887 просмотров • 1 месяц назад

instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale useful whether you've never built an agent in your life or have been using Claude every day for the past year I took the key ideas and turned them into a practical guide on how to actually build with Kimi find it below

instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale useful whether you've never built an agent in your life or have been using Claude every day for the past year I took the key ideas and turned them into a practical guide on how to actually build with Kimi find it below

Kirill

13,846,865 просмотров • 3 месяцев назад

The Kimi AI Model And What If This Carnegie Mellon, PhD student was incentivized to open source AI in the US? The answer is Kimi would have been an open source US model. Yang Zhilin Moonshot’s founder and maker of the Kimi series turned his deep research expertise into one of the world’s most capable open AI systems. After earning his PhD at Carnegie Mellon under leading researchers and interning at Google Brain and Meta, he returned to China and co-founded Moonshot AI in March 2023 with Tsinghua classmates Zhou Xinyu and Wu Yuxin. Why? The US VC world and tax structure did not favor Zhilin’s proposal to open source the AI models as a strategy. So he left. He named the company after his favorite Pink Floyd album, reflecting his ambitious vision for scalable. Yang assembled a core technical team of inventors behind breakthroughs like Transformer-XL and RoPE. Together they focused on turning massive compute into efficient intelligence through innovative architectures. The journey began with the Kimi chatbot in October 2023, rapidly scaling context from 200,000 to millions of characters. This evolved into the Kimi series: K1.5 matched top reasoning models, K2 introduced a 1-trillion-parameter Mixture-of-Experts design trained on 15.5 trillion tokens and released openly, and K2 Thinking added advanced agentic capabilities. Kimi K3 represents the pinnacle, this 2.8-trillion-parameter model uses a sparse MoE architecture with 896 experts (only 16 active per token), new Kimi Delta Attention and attention residuals for efficiency, and a 1-million-token context window. It delivers frontier performance in long-horizon coding, reasoning, and multimodal tasks at competitive cost, with weights set for open release. Yang’s approach emphasizes openness, efficiency, and continuous self-improvement — enabling solo developers and teams to achieve what once required massive resources. By sharing technical insights in public talks, he has accelerated global progress toward more accessible, powerful AI. Imagine if we held open source higher than the fear theater games of Anthropic? We are chasing out some of the best minds. This is how you lose…

The Kimi AI Model And What If This Carnegie Mellon, PhD student was incentivized to open source AI in the US? The answer is Kimi would have been an open source US model. Yang Zhilin Moonshot’s founder and maker of the Kimi series turned his deep research expertise into one of the world’s most capable open AI systems. After earning his PhD at Carnegie Mellon under leading researchers and interning at Google Brain and Meta, he returned to China and co-founded Moonshot AI in March 2023 with Tsinghua classmates Zhou Xinyu and Wu Yuxin. Why? The US VC world and tax structure did not favor Zhilin’s proposal to open source the AI models as a strategy. So he left. He named the company after his favorite Pink Floyd album, reflecting his ambitious vision for scalable. Yang assembled a core technical team of inventors behind breakthroughs like Transformer-XL and RoPE. Together they focused on turning massive compute into efficient intelligence through innovative architectures. The journey began with the Kimi chatbot in October 2023, rapidly scaling context from 200,000 to millions of characters. This evolved into the Kimi series: K1.5 matched top reasoning models, K2 introduced a 1-trillion-parameter Mixture-of-Experts design trained on 15.5 trillion tokens and released openly, and K2 Thinking added advanced agentic capabilities. Kimi K3 represents the pinnacle, this 2.8-trillion-parameter model uses a sparse MoE architecture with 896 experts (only 16 active per token), new Kimi Delta Attention and attention residuals for efficiency, and a 1-million-token context window. It delivers frontier performance in long-horizon coding, reasoning, and multimodal tasks at competitive cost, with weights set for open release. Yang’s approach emphasizes openness, efficiency, and continuous self-improvement — enabling solo developers and teams to achieve what once required massive resources. By sharing technical insights in public talks, he has accelerated global progress toward more accessible, powerful AI. Imagine if we held open source higher than the fear theater games of Anthropic? We are chasing out some of the best minds. This is how you lose…

Brian Roemmele

37,185 просмотров • 1 месяц назад

instead of watching 2 hours of Netflix tonight, watch this 12-minute Nvidia Computex 2026 keynote from Jensen Huang it's the clearest explanation I've seen of where AI agents, robots, and personal computers are actually heading useful whether you've never built an agent or have been using Claude, Chatgpt, Kimi every day for the past year and if you want to see how China is already running 1 trillion parameter — the complete guide to 7 free Kimi Skills is waiting below too.

instead of watching 2 hours of Netflix tonight, watch this 12-minute Nvidia Computex 2026 keynote from Jensen Huang it's the clearest explanation I've seen of where AI agents, robots, and personal computers are actually heading useful whether you've never built an agent or have been using Claude, Chatgpt, Kimi every day for the past year and if you want to see how China is already running 1 trillion parameter — the complete guide to 7 free Kimi Skills is waiting below too.

Kirill

573,441 просмотров • 2 месяцев назад

A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed. Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days. New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in the game. 00:11 Is Kimi K3 the Third Best Model? 04:04 Why Delay the Weights? 05:30 2.8T Parameters and Serving Constraints 06:48 Frontier Margins and the 3x Price Hike 11:10 New Architecture, What Comes Next 14:09 Will Open Source Catch Closed? 19:51 Built for Chinese Accelerators 22:57 The Harness Is the Product 28:49 We're Still Early

A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed. Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days. New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in the game. 00:11 Is Kimi K3 the Third Best Model? 04:04 Why Delay the Weights? 05:30 2.8T Parameters and Serving Constraints 06:48 Frontier Margins and the 3x Price Hike 11:10 New Architecture, What Comes Next 14:09 Will Open Source Catch Closed? 19:51 Built for Chinese Accelerators 22:57 The Harness Is the Product 28:49 We're Still Early

SemiAnalysis

122,067 просмотров • 1 месяц назад

Andrew Ng at Stanford: “Stop waiting for the perfect model. Build the iteration "loop" and "graphs" around it.” move from prompt → loop → graph → self-improving system • 00:05 - why every AI project needs an iteration loop • 04:08 - the full cycle: data → model → deploy → monitor • 16:02 - why faster iteration can decide who wins • 26:09 - using error analysis to improve the right data • 39:39 - why shipping a simple system beats waiting for perfection • 54:26 - monitoring drift and improving models after deployment 67-minute Stanford lecture, and it’s one of the clearest playbooks on why experienced AI engineers optimize the loop around the model, not just the model itself. It's watch today, then read the full step by step roadmap in the article below

Andrew Ng at Stanford: “Stop waiting for the perfect model. Build the iteration "loop" and "graphs" around it.” move from prompt → loop → graph → self-improving system • 00:05 - why every AI project needs an iteration loop • 04:08 - the full cycle: data → model → deploy → monitor • 16:02 - why faster iteration can decide who wins • 26:09 - using error analysis to improve the right data • 39:39 - why shipping a simple system beats waiting for perfection • 54:26 - monitoring drift and improving models after deployment 67-minute Stanford lecture, and it’s one of the clearest playbooks on why experienced AI engineers optimize the loop around the model, not just the model itself. It's watch today, then read the full step by step roadmap in the article below

Morlex

22,601 просмотров • 12 дней назад

This 1-hour Stanford lecture covers more about the tech inside Claude than most $1,000 AI bootcamps it answers the exact questions they'll ask you in an AI product or engineering interview: > why can Claude see images but only reply with text? > why does making the model "smarter" at reading make it better at generating output? > what is the "MoT" architecture that lets Claude learn vision without losing logic? > why is physical world interaction still the hardest problem to solve? the person who understands this framework doesn't just use Claude - they explain how it's built save this before your next interview

This 1-hour Stanford lecture covers more about the tech inside Claude than most $1,000 AI bootcamps it answers the exact questions they'll ask you in an AI product or engineering interview: > why can Claude see images but only reply with text? > why does making the model "smarter" at reading make it better at generating output? > what is the "MoT" architecture that lets Claude learn vision without losing logic? > why is physical world interaction still the hardest problem to solve? the person who understands this framework doesn't just use Claude - they explain how it's built save this before your next interview

Mr. Buzzoni

77,996 просмотров • 2 месяцев назад

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

102,765 просмотров • 26 дней назад

Before the spotlight… there was real life. In “Why Are You Here?”, Joseline Hernandez talks about growing up in the projects of Puerto Rico and the moments that built the woman the world sees today. It’s honest. It’s raw. And it’s part of her story. If you haven’t seen it yet, go now! Part 4 drops March 24 watch parts 1-3 of “Why Are You Here?” now Only on Whyareyouheretv📺 👀 Click the link in bio or go to to subscribe. Join #WAYHTV $5.99/month • $59.99/year

Before the spotlight… there was real life. In “Why Are You Here?”, Joseline Hernandez talks about growing up in the projects of Puerto Rico and the moments that built the woman the world sees today. It’s honest. It’s raw. And it’s part of her story. If you haven’t seen it yet, go now! Part 4 drops March 24 watch parts 1-3 of “Why Are You Here?” now Only on Whyareyouheretv📺 👀 Click the link in bio or go to to subscribe. Join #WAYHTV $5.99/month • $59.99/year

Whyareyouheretv

62,566 просмотров • 5 месяцев назад

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

47,790 просмотров • 1 месяц назад

Claude Fable 5 vs Kimi K3 — same 3D build, 3.3x cheaper. someone prototyped a 3D stadium seat-view picker with Fable 5 — preview exactly what you'll see from your seat before buying a ticket. I rebuilt the same thing with Kimi K3 in two hours, at 3.3x lower cost. no more static seat maps. pick a seat, see the actual view in 3D, then buy. works for cinemas, concerts, cricket grounds, Wimbledon — any venue. K3 dropped this week: 2.8 trillion parameters, open weights July 27. 3D is the future, and it just got a lot cheaper to build. Live Fable 5: Live Kimi K3: Code: full Kimi K3 breakdown below.

Claude Fable 5 vs Kimi K3 — same 3D build, 3.3x cheaper. someone prototyped a 3D stadium seat-view picker with Fable 5 — preview exactly what you'll see from your seat before buying a ticket. I rebuilt the same thing with Kimi K3 in two hours, at 3.3x lower cost. no more static seat maps. pick a seat, see the actual view in 3D, then buy. works for cinemas, concerts, cricket grounds, Wimbledon — any venue. K3 dropped this week: 2.8 trillion parameters, open weights July 27. 3D is the future, and it just got a lot cheaper to build. Live Fable 5: Live Kimi K3: Code: full Kimi K3 breakdown below.

Kirill

207,041 просмотров • 1 месяц назад

This is Moonshot AI/Kimi K3's founder & CEO Yang Zhilin at GTC 2026 The 34-year-old researcher studied at Tsinghua, then earned his Carnegie Mellon Ph.D. Then he worked with Jason Weston at Meta AI and Quoc Le at Google Brain. His work also connected him to Yoshua Bengio and Yann LeCun on the topics of reasoning and representation. Eventually, Yang returned to China and co-founded Moonshot AI in Beijing a few years later. His earlier Transformer-XL and XLNet work addressed how language models forget distant context. Moonshot AI carried that research focus forward through Kimi K2’s 1T-parameter mixture-of-experts design. XLNet, was later cited more than 10,000 times and is still his best-known academic paper. Kimi K3 expands the formula with native vision, 1M-token context, and only 16 active experts. His career has become a textbook example of how quickly global research networks can reshape the way countries compete in AI. ---- From "Kimi AI" YouTube channel, (full video link in comment)

This is Moonshot AI/Kimi K3's founder & CEO Yang Zhilin at GTC 2026 The 34-year-old researcher studied at Tsinghua, then earned his Carnegie Mellon Ph.D. Then he worked with Jason Weston at Meta AI and Quoc Le at Google Brain. His work also connected him to Yoshua Bengio and Yann LeCun on the topics of reasoning and representation. Eventually, Yang returned to China and co-founded Moonshot AI in Beijing a few years later. His earlier Transformer-XL and XLNet work addressed how language models forget distant context. Moonshot AI carried that research focus forward through Kimi K2’s 1T-parameter mixture-of-experts design. XLNet, was later cited more than 10,000 times and is still his best-known academic paper. Kimi K3 expands the formula with native vision, 1M-token context, and only 16 active experts. His career has become a textbook example of how quickly global research networks can reshape the way countries compete in AI. ---- From "Kimi AI" YouTube channel, (full video link in comment)

Rohan Paul

74,021 просмотров • 1 месяц назад

He didn't believe the Kimi K3 hype. So he ran it against Claude Fable 5 on camera. Same prompts, both models, 12 minutes, results raw on screen. No cherry-picking, nowhere to hide. The context makes it spicy: Moonshot itself admits K3 trails Fable 5 overall while posting wins on coding benchmarks. 2.8 trillion parameters against the model everyone pays for. Benchmarks are marketing until somebody runs them at home. This is somebody running them at home. Watch the middle section where the outputs go up side by side. The gap is visible before he says a word. The verdict lands in the last minute. No spoilers. Leaderboards don't write code. Models do.

He didn't believe the Kimi K3 hype. So he ran it against Claude Fable 5 on camera. Same prompts, both models, 12 minutes, results raw on screen. No cherry-picking, nowhere to hide. The context makes it spicy: Moonshot itself admits K3 trails Fable 5 overall while posting wins on coding benchmarks. 2.8 trillion parameters against the model everyone pays for. Benchmarks are marketing until somebody runs them at home. This is somebody running them at home. Watch the middle section where the outputs go up side by side. The gap is visible before he says a word. The verdict lands in the last minute. No spoilers. Leaderboards don't write code. Models do.

Merlow

148,055 просмотров • 1 месяц назад

As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

fintex

109,936 просмотров • 23 дней назад

“I can’t see why [the stock market] wouldn’t go down at least 40-50%,” says Edward Dowd. Dowd warns the downturn was already in motion – before the Iran war. Housing rolling over. AI bubble cracking. Private credit frozen. China slowing. Watch the full interview on The Real Story with Michelle Makori for a breakdown how all of this can unfold ⬇️

“I can’t see why [the stock market] wouldn’t go down at least 40-50%,” says Edward Dowd. Dowd warns the downturn was already in motion – before the Iran war. Housing rolling over. AI bubble cracking. Private credit frozen. China slowing. Watch the full interview on The Real Story with Michelle Makori for a breakdown how all of this can unfold ⬇️

Miles Franklin Precious Metals

32,968 просмотров • 4 месяцев назад