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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... show more

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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 • 20 дней назад

My biggest fear for AI is concentration of power. Open-source is a solution. Watch this video to understand what's going on: 0:00 Intro 0:52 NVIDIA's letter 1:36 What is open-source? 3:53 How open-source works 5:48 Kimi K3 6:38 Closed vs Open 8:39 Open-source in the U.S. 12:10 The AI stack 13:05 What if open source wins? 14:08 Jevon's Paradox 14:56 How the AI stack is affected 18:04 User experience 19:38 AI safety 30:30 China 35:50 Distillation 41:40 Banning Chinese models? 43:38 Anthropic's response

My biggest fear for AI is concentration of power. Open-source is a solution. Watch this video to understand what's going on: 0:00 Intro 0:52 NVIDIA's letter 1:36 What is open-source? 3:53 How open-source works 5:48 Kimi K3 6:38 Closed vs Open 8:39 Open-source in the U.S. 12:10 The AI stack 13:05 What if open source wins? 14:08 Jevon's Paradox 14:56 How the AI stack is affected 18:04 User experience 19:38 AI safety 30:30 China 35:50 Distillation 41:40 Banning Chinese models? 43:38 Anthropic's response

Matthew Berman

13,685 просмотров • 19 дней назад

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇

LMSYS Org

201,687 просмотров • 21 дней 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

102,101 просмотров • 21 дней назад

OpenAI's chairman Brett Taylor on the new Chinese model Kimi K3. the timeline thinks it's a frontier killer. he says every CEO he talks to asks the same two things: am I getting value from my token spend, AND what's my moat in the age of AI. on token efficiency, he says the frontier models still finish the same task in far fewer tokens. worth a listen.

OpenAI's chairman Brett Taylor on the new Chinese model Kimi K3. the timeline thinks it's a frontier killer. he says every CEO he talks to asks the same two things: am I getting value from my token spend, AND what's my moat in the age of AI. on token efficiency, he says the frontier models still finish the same task in far fewer tokens. worth a listen.

Damian Player

469,842 просмотров • 28 дней 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 • 28 дней назад

Kimi K3 is out. 2.8T parameters. 1.4 TB of weights, frontier-level performance. Serving it on your own infra is the hard part - you need multi-node inference spread across every cluster you can grab capacity from. And then autoscaling, cache-aware routing, PD disaggregation - an entire serving stack around the model. SkyPilot Endpoints ships a complete stack on day 0 for Kimi.ai's Kimi K3: • Runs on GPUs you own - hyperscalers, neoclouds or on-prem. • Production-ready out of the box - autoscaling, KV-aware routing, PD disaggregation, observability • Fault-tolerant by design - endpoint stays up even when GPUs fail Frontier open models, on GPUs you own. 🔗

Kimi K3 is out. 2.8T parameters. 1.4 TB of weights, frontier-level performance. Serving it on your own infra is the hard part - you need multi-node inference spread across every cluster you can grab capacity from. And then autoscaling, cache-aware routing, PD disaggregation - an entire serving stack around the model. SkyPilot Endpoints ships a complete stack on day 0 for Kimi.ai's Kimi K3: • Runs on GPUs you own - hyperscalers, neoclouds or on-prem. • Production-ready out of the box - autoscaling, KV-aware routing, PD disaggregation, observability • Fault-tolerant by design - endpoint stays up even when GPUs fail Frontier open models, on GPUs you own. 🔗

SkyPilot

14,478 просмотров • 21 дней назад

KIMI K3 is here, and it blew my mind 🤯 yes, it is expensive and very slow Took over 30 minutes, and blew an ENTIRE $20 kimi sub before it even finished the one prompt for this game. BUT OMG... the output geniuinly has fable 5 level magic built into it, this model falls between gpt 5.6 and fable 5 imo. you can play the game at kimisurfers (dot) com right now, and see what I mean. This model is too expensive to replace fable 5, BUTT it shows that the chinese open source labs are CATCHING anthropic and open ai FASTTT.

KIMI K3 is here, and it blew my mind 🤯 yes, it is expensive and very slow Took over 30 minutes, and blew an ENTIRE $20 kimi sub before it even finished the one prompt for this game. BUT OMG... the output geniuinly has fable 5 level magic built into it, this model falls between gpt 5.6 and fable 5 imo. you can play the game at kimisurfers (dot) com right now, and see what I mean. This model is too expensive to replace fable 5, BUTT it shows that the chinese open source labs are CATCHING anthropic and open ai FASTTT.

Max Blade

128,812 просмотров • 1 месяц назад

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" everyone talks about hasn't formed yet > the real challenge in multimodality nobody mentions > why K2 was closer to a base model — the real potential wasn't unlocked yet this is only part of the interview, translated to English — and everything he hinted at just shipped as Kimi K3, 2.8 trillion parameters, the biggest open model ever full breakdown below

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" everyone talks about hasn't formed yet > the real challenge in multimodality nobody mentions > why K2 was closer to a base model — the real potential wasn't unlocked yet this is only part of the interview, translated to English — and everything he hinted at just shipped as Kimi K3, 2.8 trillion parameters, the biggest open model ever full breakdown below

Kirill

533,393 просмотров • 29 дней назад

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

85,538 просмотров • 4 месяцев назад

on the frontier of tech & culture in conversation with signüll ! 00:00 signüll account origins 06:25 on being motivated by process vs. outcomes 07:49 many-to-one leverage 14:49 on becoming more open with more visibility 16:57 AI researchers and policy discussions 22:35 a post kimi k3 world 26:57 american competition vs protectionism 34:12 the role of incentives in AI discourse 36:11 bringing more people into the AI wave 43:24 on signüll's spirituality 47:56 the centralization of talent 55:30 the effects of lower cost intelligence 1:02:00 signüll is hiring for skye and investing! DM him

on the frontier of tech & culture in conversation with signüll ! 00:00 signüll account origins 06:25 on being motivated by process vs. outcomes 07:49 many-to-one leverage 14:49 on becoming more open with more visibility 16:57 AI researchers and policy discussions 22:35 a post kimi k3 world 26:57 american competition vs protectionism 34:12 the role of incentives in AI discourse 36:11 bringing more people into the AI wave 43:24 on signüll's spirituality 47:56 the centralization of talent 55:30 the effects of lower cost intelligence 1:02:00 signüll is hiring for skye and investing! DM him

ashe

90,387 просмотров • 13 дней назад

Kimi K3 passed Fable 5 on Code Arena just 6 weeks after Fable's release, landing #1 in 6 of 7 frontend domains. We sat down with Arena CEO and co-founder Anastasios Nikolas Angelopoulos for a rapid fire conversation on what the data from real-world use tells us 👇 0:00 Biggest release of the year, or overreaction? 0:33 Kimi K3 beats Fable on Code Arena: verifying the votes 1:35 1679 points: a 17-place jump from K2.6 2:37 #1 in 6 of 7 frontend domains — but still #2 in gaming 3:15 Permanent shift or snapshot? Labs distilling Chinese models 4:23 Regulating Chinese models: the case for and against 6:30 $3 in, $15 out: open source is no longer 10% of the price 7:30 If you're Anthropic or OpenAI, what's your move? 8:09 How long before the leaderboard flips again?

Kimi K3 passed Fable 5 on Code Arena just 6 weeks after Fable's release, landing #1 in 6 of 7 frontend domains. We sat down with Arena CEO and co-founder Anastasios Nikolas Angelopoulos for a rapid fire conversation on what the data from real-world use tells us 👇 0:00 Biggest release of the year, or overreaction? 0:33 Kimi K3 beats Fable on Code Arena: verifying the votes 1:35 1679 points: a 17-place jump from K2.6 2:37 #1 in 6 of 7 frontend domains — but still #2 in gaming 3:15 Permanent shift or snapshot? Labs distilling Chinese models 4:23 Regulating Chinese models: the case for and against 6:30 $3 in, $15 out: open source is no longer 10% of the price 7:30 If you're Anthropic or OpenAI, what's your move? 8:09 How long before the leaderboard flips again?

Arena.ai

50,903 просмотров • 24 дней назад

WATCH — Even the Government Gets Robbed: Why the US Needs Bitcoin Custody Standards | Bitcoin Policy Hour Ep 44 On this episode of the Bitcoin Policy Hour, the team breaks down China's Kimi K3 AI model and the US response, the bipartisan backlash against data centers, and the ARMA bill that would give Treasury real Bitcoin custody standards. Feat. Zack Cohen Zack Shapiro Bayman11771 Chapters: 04:02 How the US Might Curb Chinese Models 10:04 "FINRA for AI" & the Open-Source Debate 16:25 Why China Went Open Source 19:52 Xi Jinping's Global AI Governance Play 27:31 Robots, Health Care & the Industrial AI Layer 30:04 America's Bipartisan Data Center Backlash 39:38 AI's PR Problem 42:38 Mastering AI Prompting 51:10 ARMA, the Bitcoin Reserve Bill & DC Departures

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Bitcoin Magazine

12,978 просмотров • 13 дней назад

The Global AI Race: and the View from Beijing — Manifold #96 Zixuan Li is Director of Product and genAI Strategy at He was educated at Renmin and Tsinghua University in China, and at MIT and Carnegie Mellon. has released frontier open source LLMs but is largely unknown in the West except among AI experts. Steve and Zixuan discuss the AI race from the perspective of a startup in Beijing. Chapter markers: (00:00) - Introduction and Guest Background (05:14) - Evolution and Challenges (10:37) - AI Model Comparison and Industry Insights (34:04) - Pragmatic Views on AGI in China (35:51) - Specialized Models and Scientific Contributions (39:02) - AI Chips and Model Training in PRC (50:54) - Open Source AI and Future Predictions

The Global AI Race: and the View from Beijing — Manifold #96 Zixuan Li is Director of Product and genAI Strategy at He was educated at Renmin and Tsinghua University in China, and at MIT and Carnegie Mellon. has released frontier open source LLMs but is largely unknown in the West except among AI experts. Steve and Zixuan discuss the AI race from the perspective of a startup in Beijing. Chapter markers: (00:00) - Introduction and Guest Background (05:14) - Evolution and Challenges (10:37) - AI Model Comparison and Industry Insights (34:04) - Pragmatic Views on AGI in China (35:51) - Specialized Models and Scientific Contributions (39:02) - AI Chips and Model Training in PRC (50:54) - Open Source AI and Future Predictions

steve hsu

146,691 просмотров • 10 месяцев назад

DataBricks raising at a $188B valuation, CEO Ali Ghodsi joined us on The Exchange to talk about the round as China's Kimi makes waves in AI today: Calls Kimi's newest K3 model a "gamechanger" and a "significant jump" in quality (at a cheaper price) Also comes as costs are increasing "exponentially" for Fortune 500s using AI, revenues are not 💸 "For the really difficult questions let's ask the frontier American models -- and pay a lot for it... for the more mundane tasks... use a cheap open source model."

DataBricks raising at a $188B valuation, CEO Ali Ghodsi joined us on The Exchange to talk about the round as China's Kimi makes waves in AI today: Calls Kimi's newest K3 model a "gamechanger" and a "significant jump" in quality (at a cheaper price) Also comes as costs are increasing "exponentially" for Fortune 500s using AI, revenues are not 💸 "For the really difficult questions let's ask the frontier American models -- and pay a lot for it... for the more mundane tasks... use a cheap open source model."

Kate Rooney

24,842 просмотров • 1 месяц назад

This is my sixth conversation with Gavin Baker. As always with Gavin, the conversation covers a lot of ground, but we spend the most time on watts and wafers. We discuss: - Why the wafer shortage may prevent an AI bubble - Data centers in space (reframed) - Elon's Terafab and the new chip companies challenging Nvidia - Usage-based pricing - The disaggregation of GPUs - DRAM, frontier tokens, and open source Enjoy! Timestamps: 0:00 Intro 7:55 Anthropic and OpenAI Valuations 12:58 Watts, Wafers, and Infrastructure 14:39 Orbital Compute and Data Centers in Space 22:49 Avoiding the AI Bubble 28:26 Terafab and the Future of US Manufacturing 32:16 Returns to the Frontier 37:23 Continual Learning 42:03 New Chip Companies 48:52 Extending GPU Lifespans and Private Credit 51:22 The Application Layer 57:32 The Token Path and Open-Source Dynamics 1:01:37 Cybersecurity 1:05:46 Diversity Breakdown 1:11:59 Assessing the Big Tech Players in AI 1:19:02 Geopolitics, Personal Safety, and the AI Horizon

This is my sixth conversation with Gavin Baker. As always with Gavin, the conversation covers a lot of ground, but we spend the most time on watts and wafers. We discuss: - Why the wafer shortage may prevent an AI bubble - Data centers in space (reframed) - Elon's Terafab and the new chip companies challenging Nvidia - Usage-based pricing - The disaggregation of GPUs - DRAM, frontier tokens, and open source Enjoy! Timestamps: 0:00 Intro 7:55 Anthropic and OpenAI Valuations 12:58 Watts, Wafers, and Infrastructure 14:39 Orbital Compute and Data Centers in Space 22:49 Avoiding the AI Bubble 28:26 Terafab and the Future of US Manufacturing 32:16 Returns to the Frontier 37:23 Continual Learning 42:03 New Chip Companies 48:52 Extending GPU Lifespans and Private Credit 51:22 The Application Layer 57:32 The Token Path and Open-Source Dynamics 1:01:37 Cybersecurity 1:05:46 Diversity Breakdown 1:11:59 Assessing the Big Tech Players in AI 1:19:02 Geopolitics, Personal Safety, and the AI Horizon

Patrick OShaughnessy

1,676,303 просмотров • 2 месяцев назад

Open source AI just became the biggest policy fight in tech. Our full conversation with Ben Horowitz, where he tells Theo and Sofia why banning open source is nearly impossible, why it's historically the safer path, the risk of one company controlling AI and why distillation isn't the threat people claim. 01:21 The Nvidia-backed open letter on open-weight AI 01:49 The OpenAI–Hugging Face hacking incident 02:27 Why you can't actually ban open source 03:05 Historically, open source has been safer (Linux vs. Windows) 05:17 Jensen's Twitter account and the letter's significance 07:25 China, "sleeper agent" models, and national security risk 09:23 Anthropic defying the US government 15:18 The danger of building your business on Anthropic 18:51 BYD, EVs, and rebuilding US manufacturing 21:30 Why would a company ever release an open model? 23:58 The DeepSeek moment: why nothing actually changed 28:25 Paying 100x more for an AI researcher vs. a janitor 29:06 AI art, the "slop" problem, and a coming renaissance benahorowitz.eth Theo Jaffee sof 𓋹

Open source AI just became the biggest policy fight in tech. Our full conversation with Ben Horowitz, where he tells Theo and Sofia why banning open source is nearly impossible, why it's historically the safer path, the risk of one company controlling AI and why distillation isn't the threat people claim. 01:21 The Nvidia-backed open letter on open-weight AI 01:49 The OpenAI–Hugging Face hacking incident 02:27 Why you can't actually ban open source 03:05 Historically, open source has been safer (Linux vs. Windows) 05:17 Jensen's Twitter account and the letter's significance 07:25 China, "sleeper agent" models, and national security risk 09:23 Anthropic defying the US government 15:18 The danger of building your business on Anthropic 18:51 BYD, EVs, and rebuilding US manufacturing 21:30 Why would a company ever release an open model? 23:58 The DeepSeek moment: why nothing actually changed 28:25 Paying 100x more for an AI researcher vs. a janitor 29:06 AI art, the "slop" problem, and a coming renaissance benahorowitz.eth Theo Jaffee sof 𓋹

MTS

11,211 просмотров • 21 дней назад

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

Matt Turck

63,712 просмотров • 10 дней назад

Kimi K3 shipped and a competitor's stock lost 30% the same week. It wasn't an American one. Z ai fell as much as 30%. MiniMax 16%. Alibaba 4%. The US labs it was supposedly aimed at aren't even listed. K3 is 2.8 trillion parameters, the largest open-weight model ever released. Roughly 75% bigger than DeepSeek's V4 Pro. One million tokens of context, native vision, thinking mode always on. #1 in Frontend Code Arena, top three on every major index. Then Moonshot gave the weights away on July 27. Free to download, free to self-host, yours forever. That reads like generosity until you check who it actually hurt. To a frontier lab, an open model is competition. To the second-best open model, it's an extinction event. Nobody pays for mid-tier open weights when better ones cost nothing. The license is where the business hides. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for you. A contract for anyone big enough to matter. Daily revenue is up at least 6x since launch, and they're raising at $50 billion. Open weights stopped being a gift. They're a pricing weapon now. Go look at what your stack runs on and ask what the giver gets back.

Kimi K3 shipped and a competitor's stock lost 30% the same week. It wasn't an American one. Z ai fell as much as 30%. MiniMax 16%. Alibaba 4%. The US labs it was supposedly aimed at aren't even listed. K3 is 2.8 trillion parameters, the largest open-weight model ever released. Roughly 75% bigger than DeepSeek's V4 Pro. One million tokens of context, native vision, thinking mode always on. #1 in Frontend Code Arena, top three on every major index. Then Moonshot gave the weights away on July 27. Free to download, free to self-host, yours forever. That reads like generosity until you check who it actually hurt. To a frontier lab, an open model is competition. To the second-best open model, it's an extinction event. Nobody pays for mid-tier open weights when better ones cost nothing. The license is where the business hides. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for you. A contract for anyone big enough to matter. Daily revenue is up at least 6x since launch, and they're raising at $50 billion. Open weights stopped being a gift. They're a pricing weapon now. Go look at what your stack runs on and ask what the giver gets back.

wast3

73,047 просмотров • 3 дней назад

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts

sarah guo

3,038,245 просмотров • 5 месяцев назад