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China just open-sourced AI that makes $200 cameras outperform $2,000 sensors. Ant Group dropped: → 3.2M training samples → Full code + models → Fixes depth cameras on glass, mirrors, metal A $200 consumer camera + free AI now beats $800 pro sensors. China is giving away the entire... show more

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284,434 görüntüleme • 6 ay önce •via X (Twitter)

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China is trying to pop the US AI bubble. Xi just announced China will provide 30 countries with 5,000 AI training and seminar opportunities while building international AI application cooperation centers. China and the US have a different definition of "winning" the AI race. The US strategy: Spend trillions building and controlling the AI infrastructure the world will depend on through a proprietary ecosystem. China is taking the opposite approach: Build cheaper open-source models with similar performance and encourage the world to build on top of them. The bet is simple: The winner of AI will not necessarily be whoever owns the infrastructure. .... It will be whoever builds the best companies on top of it. Just like in the post-dot-com era... The biggest winners were companies like Amazon, Google, and Microsoft that built businesses on top of open infrastructure.

China is trying to pop the US AI bubble. Xi just announced China will provide 30 countries with 5,000 AI training and seminar opportunities while building international AI application cooperation centers. China and the US have a different definition of "winning" the AI race. The US strategy: Spend trillions building and controlling the AI infrastructure the world will depend on through a proprietary ecosystem. China is taking the opposite approach: Build cheaper open-source models with similar performance and encourage the world to build on top of them. The bet is simple: The winner of AI will not necessarily be whoever owns the infrastructure. .... It will be whoever builds the best companies on top of it. Just like in the post-dot-com era... The biggest winners were companies like Amazon, Google, and Microsoft that built businesses on top of open infrastructure.

Lukas Ekwueme

194,866 görüntüleme • 8 gün önce

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

Rohan Paul

25,312 görüntüleme • 1 ay önce

One smartphone at the core of a robot. The idea is simple but interesting: use a used smartphone as the main controller for a hobby robot, in this case a hexapod. Instead of adding separate boards and sensors, the phone already brings a lot of what robots need: - IMU and environmental sensors - WiFi, Bluetooth, and cellular connectivity - Multiple cameras and GPS - Battery, display, GPU, and a fairly powerful processor All of this is already calibrated and supported by mature software tools and frameworks. What makes this approach worth looking at is not just convenience. It also reuses hardware that would otherwise end up as e-waste. Billions of smartphones are discarded every year. Nice example of leveraging existing consumer hardware for robotics. Project by Mehdi Alizadeh from YouTube: —- Weekly robotics and AI insights. Subscribe free:

One smartphone at the core of a robot. The idea is simple but interesting: use a used smartphone as the main controller for a hobby robot, in this case a hexapod. Instead of adding separate boards and sensors, the phone already brings a lot of what robots need: - IMU and environmental sensors - WiFi, Bluetooth, and cellular connectivity - Multiple cameras and GPS - Battery, display, GPU, and a fairly powerful processor All of this is already calibrated and supported by mature software tools and frameworks. What makes this approach worth looking at is not just convenience. It also reuses hardware that would otherwise end up as e-waste. Billions of smartphones are discarded every year. Nice example of leveraging existing consumer hardware for robotics. Project by Mehdi Alizadeh from YouTube: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

150,850 görüntüleme • 8 ay önce

NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.

NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.

Big Brain AI

59,540 görüntüleme • 7 ay önce

Yann LeCun just said something at Davos that nobody is talking about. The man who built Meta's AI for 12 years. The godfather of deep learning. Said this on stage, in front of the world's most powerful people: "The best open source AI models right now come from China. Everybody in the research community is using Chinese models." Here's what makes this more uncomfortable. He said it in the same breath as warning that the US is making a catastrophic mistake. OpenAI — increasingly closed. Anthropic — never open. Google — more closed every year. Meta — changing direction. Meanwhile China's labs are publishing. Open sourcing. Sharing. And the global research community is downloading their models. LeCun's warning was blunt: "We are slowing down progress in the West because of this." The irony is brutal. America built the open internet. Linux runs the world. The entire software stack humanity depends on is open source. And now the country that invented openness is closing its most important technology. While China does the opposite. The next AI revolution won't be won by the country with the most money. It will be won by the country whose models researchers actually use. Right now that answer is changing.

Yann LeCun just said something at Davos that nobody is talking about. The man who built Meta's AI for 12 years. The godfather of deep learning. Said this on stage, in front of the world's most powerful people: "The best open source AI models right now come from China. Everybody in the research community is using Chinese models." Here's what makes this more uncomfortable. He said it in the same breath as warning that the US is making a catastrophic mistake. OpenAI — increasingly closed. Anthropic — never open. Google — more closed every year. Meta — changing direction. Meanwhile China's labs are publishing. Open sourcing. Sharing. And the global research community is downloading their models. LeCun's warning was blunt: "We are slowing down progress in the West because of this." The irony is brutal. America built the open internet. Linux runs the world. The entire software stack humanity depends on is open source. And now the country that invented openness is closing its most important technology. While China does the opposite. The next AI revolution won't be won by the country with the most money. It will be won by the country whose models researchers actually use. Right now that answer is changing.

Shruti

31,597 görüntüleme • 3 ay önce

$🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.$

🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.

CryptoGoos

43,154 görüntüleme • 2 ay önce

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Karl Mehta

14,983 görüntüleme • 1 ay önce

Zhipu AI just dropped a $3 model that beats GPT-5.4. It is called GLM 5.1. It just took 3rd place on the Code Arena charts. Claude Max costs up to 200 dollars a month. GLM 5.1 gives you 94 percent of Claude's power for just 3 dollars. But here is the crazy part. It works completely alone for 8 hours straight. You give it a huge task in the morning. You walk away and do your actual job. At the end of the day, your code is done, tested, and fixed. It is not a simple chatbot. It is an AI worker that builds landing pages and email flows for you. Stop doing the busy work yourself. Get the exact steps to build your own AI systems in my free group. Link in my bio.

Zhipu AI just dropped a $3 model that beats GPT-5.4. It is called GLM 5.1. It just took 3rd place on the Code Arena charts. Claude Max costs up to 200 dollars a month. GLM 5.1 gives you 94 percent of Claude's power for just 3 dollars. But here is the crazy part. It works completely alone for 8 hours straight. You give it a huge task in the morning. You walk away and do your actual job. At the end of the day, your code is done, tested, and fixed. It is not a simple chatbot. It is an AI worker that builds landing pages and email flows for you. Stop doing the busy work yourself. Get the exact steps to build your own AI systems in my free group. Link in my bio.

Julian Goldie SEO

12,606 görüntüleme • 4 ay önce

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Mia Bao

44,564 görüntüleme • 1 ay önce

I've been posting about $SONY for a GTA VI re-rate. But did you know it's also a god tier robotics long. $SONY built a robot that defeats human ping pong pros. It's called Ace. Sony AI trained it purely in simulation, no human footage. It reacts in 20ms vs 230ms for a pro, and this year it took down a top 25 world ranked player under official ITTF rules. This is the same lab that beat also Gran Turismo world champions in 2022. Every robot, every car, every phone camera needs eyes. $SONY makes the eyes. The company is world #1 in image sensors for years running. Image sensors are ~17% of conglomerate revenue But ~25% of operating profit Record ~$2.2B last year, up 29%. Then last month they signed a JV with TSMC to build next gen image sensors in Japan. Sony holds the controlling stake. The stated target? Physical AI. The best foundry on earth is now co-building Sony's sensors for the exact wave Ace is a preview of. So the market prices $SONY as a memory constrained game console maker at ~16x forward earnings, down ~17% on the year and 50% from November alone. What you're getting is 50% of the market in the eyes for robotics, and a near pure margin cut on 7/10 GTA copies sold until PC enters the mix.

I've been posting about $SONY for a GTA VI re-rate. But did you know it's also a god tier robotics long. $SONY built a robot that defeats human ping pong pros. It's called Ace. Sony AI trained it purely in simulation, no human footage. It reacts in 20ms vs 230ms for a pro, and this year it took down a top 25 world ranked player under official ITTF rules. This is the same lab that beat also Gran Turismo world champions in 2022. Every robot, every car, every phone camera needs eyes. $SONY makes the eyes. The company is world #1 in image sensors for years running. Image sensors are ~17% of conglomerate revenue But ~25% of operating profit Record ~$2.2B last year, up 29%. Then last month they signed a JV with TSMC to build next gen image sensors in Japan. Sony holds the controlling stake. The stated target? Physical AI. The best foundry on earth is now co-building Sony's sensors for the exact wave Ace is a preview of. So the market prices $SONY as a memory constrained game console maker at ~16x forward earnings, down ~17% on the year and 50% from November alone. What you're getting is 50% of the market in the eyes for robotics, and a near pure margin cut on 7/10 GTA copies sold until PC enters the mix.

Michael Sikand

76,696 görüntüleme • 1 ay önce

SITUATION EXPLAINED: Why is China building a sovereign AI stack to compete with Nvidia? We asked Glinert 🇺🇸 🏭 and Mitchell Nahmias, co-founders of Sphere Semi. "What I see as the only real viable crack in Nvidia's armor is what's coming out of China, which is ironic given Jensen's famously kind of weird stance on China." "If you think about the things that Nvidia can do, the way that neural nets are trained into CUDA, the way they've built the data center rack into Nvidia, the way they've built memory infrastructure into Nvidia. The only thing that is incredible about China is they've built this sovereign stack for all of these things." "They are now in many ways seemingly able to actually have independent training capabilities for their own models that are entirely outside of Nvidia." "There is no threat to him but Xi Jinping. That he should take serious."

SITUATION EXPLAINED: Why is China building a sovereign AI stack to compete with Nvidia? We asked Glinert 🇺🇸 🏭 and Mitchell Nahmias, co-founders of Sphere Semi. "What I see as the only real viable crack in Nvidia's armor is what's coming out of China, which is ironic given Jensen's famously kind of weird stance on China." "If you think about the things that Nvidia can do, the way that neural nets are trained into CUDA, the way they've built the data center rack into Nvidia, the way they've built memory infrastructure into Nvidia. The only thing that is incredible about China is they've built this sovereign stack for all of these things." "They are now in many ways seemingly able to actually have independent training capabilities for their own models that are entirely outside of Nvidia." "There is no threat to him but Xi Jinping. That he should take serious."

MTS

26,278 görüntüleme • 1 ay önce

Tesla’s vision-only approach to solve FSD got laughed at for many years. People called it reckless, said just cameras would never be enough, and even claimed Tesla was cutting corners. Fast forward to today, it’s pretty clear who was right. “It’s so obvious you can solve this with cameras. Why wouldn’t you solve with cameras? It’s 2026. The self-driving problem is not a sensor problem, it’s an AI problem. The cameras have enough information already. It’s a problem of extracting the information, which is an AI problem.” - Ashok Elluswamy, VP of AI at Tesla. Just like us humans, we drive with eyes, we walk with eyes, literally we do everything with our eyes. We don’t have lidar spinning and laser beams on our heads. We simply see the world and our brain figures it out… all from vision. For so many years, the industry treated autonomy like it was a hardware problem. Like “if cameras struggle, just add more sensors.” That’s exactly how companies like Waymo ended up with sensor towers that look like crazy science projects. So ugly btw too… That approach might have made sense in 2008, but not today! Bc back then, AI wasn’t good enough to truly understand the world. So engineers had no choice but to lean on lidar and radar for help. But today, cameras capture insane amounts of information, I’m talking billions of pixels every second. The thing that was missing was AI intelligence. And when AI can understand why a car is slowing or why a pedestrian is hesitating, or real world things, you just need a better brain instead of more sensors. And now, everyone else is realizing this… the future of autonomy was always about understanding the world through vision, just like we humans do. That’s the bet that Elon and Tesla made early on… he was already years ahead of everyone, when everyone was doubling down on LiDAR and radar, even when so many people mocked them for switching early. Tesla vision for the win!!! 🥇

Tesla’s vision-only approach to solve FSD got laughed at for many years. People called it reckless, said just cameras would never be enough, and even claimed Tesla was cutting corners. Fast forward to today, it’s pretty clear who was right. “It’s so obvious you can solve this with cameras. Why wouldn’t you solve with cameras? It’s 2026. The self-driving problem is not a sensor problem, it’s an AI problem. The cameras have enough information already. It’s a problem of extracting the information, which is an AI problem.” - Ashok Elluswamy, VP of AI at Tesla. Just like us humans, we drive with eyes, we walk with eyes, literally we do everything with our eyes. We don’t have lidar spinning and laser beams on our heads. We simply see the world and our brain figures it out… all from vision. For so many years, the industry treated autonomy like it was a hardware problem. Like “if cameras struggle, just add more sensors.” That’s exactly how companies like Waymo ended up with sensor towers that look like crazy science projects. So ugly btw too… That approach might have made sense in 2008, but not today! Bc back then, AI wasn’t good enough to truly understand the world. So engineers had no choice but to lean on lidar and radar for help. But today, cameras capture insane amounts of information, I’m talking billions of pixels every second. The thing that was missing was AI intelligence. And when AI can understand why a car is slowing or why a pedestrian is hesitating, or real world things, you just need a better brain instead of more sensors. And now, everyone else is realizing this… the future of autonomy was always about understanding the world through vision, just like we humans do. That’s the bet that Elon and Tesla made early on… he was already years ahead of everyone, when everyone was doubling down on LiDAR and radar, even when so many people mocked them for switching early. Tesla vision for the win!!! 🥇

Teslaconomics

19,357 görüntüleme • 6 ay önce

🇨🇳📱 China Isn’t Talking About the AI Future: It’s Already Living It While Western media is still debating what AI wearables might look like, China is already living in that future, in shops, offices, classrooms, metros and even family homes. A new report shows just how far ahead China actually is: - 70+ Chinese companies have launched smart-glasses since Meta’s 2023 release. - Devices from Inmo, Rokid, Xiaomi, Alibaba are everywhere, some selling overseas, others powering China’s domestic AI ecosystems. - Features like paying for items by glancing at a QR code already exist in China. - China’s AI hardware sector is booming while others are still imagining prototypes. Kai-Fu Lee sums it up perfectly: “China is the nation of manufacturing… the next phase of competition will move to devices.” Exactly. The U.S. builds models. China builds the whole world those models have to run in. Why China Is Leading the AI Device Race? 1️⃣ The world’s strongest hardware ecosystem No country on Earth can design, prototype, manufacture and scale AI devices as fast as China. The ability to build millions of units in months gives China a massive advantage the U.S. simply doesn’t have. 2️⃣ Seamless digital integration Smart glasses plug directly into China’s existing infrastructure: - QR payments - WeChat / Alipay ecosystems - AI-enhanced navigation - Real-time translation and AR overlays The West has apps. China has an entire society built for AI adoption. 3️⃣ Relentless experimentation China is testing everything from: - enterprise AI recorders (DingTalk A1) - Plaud-style pocket transcribers - education-focused AI translators - even the quirky “Native Language Star” muzzle device for English practice Other countries theorise about “the next big thing.” China builds 20 versions of it by lunchtime. 4️⃣ The feedback loop advantage More devices, more usage, more data, faster improvement. China’s scale accelerates AI development in ways the U.S. cannot match. 5️⃣ AI wearables are already normal here Smart eyewear, AI notetakers, live translators, all in daily use. This isn’t sci-fi. This is Tuesday in Beijing. The Global Reality: For China, the AI device race is not hypothetical, it’s happening at street level. For many outside China? They can only imagine this kind of tech and often don’t believe it when they hear about it. Anyone living in China already sees the future everywhere, in convenience stores, on commuters, in classrooms and in the hands of office workers. Meanwhile, most of the world is still arguing about “what AI devices might eventually look like.” The Hard Truth: If the next “iPhone moment” of the AI age is going to come from anywhere, the smart money is on China, a country where hardware innovation, manufacturing power and real-world adoption all move at a speed the West simply can’t match.

🇨🇳📱 China Isn’t Talking About the AI Future: It’s Already Living It While Western media is still debating what AI wearables might look like, China is already living in that future, in shops, offices, classrooms, metros and even family homes. A new report shows just how far ahead China actually is: - 70+ Chinese companies have launched smart-glasses since Meta’s 2023 release. - Devices from Inmo, Rokid, Xiaomi, Alibaba are everywhere, some selling overseas, others powering China’s domestic AI ecosystems. - Features like paying for items by glancing at a QR code already exist in China. - China’s AI hardware sector is booming while others are still imagining prototypes. Kai-Fu Lee sums it up perfectly: “China is the nation of manufacturing… the next phase of competition will move to devices.” Exactly. The U.S. builds models. China builds the whole world those models have to run in. Why China Is Leading the AI Device Race? 1️⃣ The world’s strongest hardware ecosystem No country on Earth can design, prototype, manufacture and scale AI devices as fast as China. The ability to build millions of units in months gives China a massive advantage the U.S. simply doesn’t have. 2️⃣ Seamless digital integration Smart glasses plug directly into China’s existing infrastructure: - QR payments - WeChat / Alipay ecosystems - AI-enhanced navigation - Real-time translation and AR overlays The West has apps. China has an entire society built for AI adoption. 3️⃣ Relentless experimentation China is testing everything from: - enterprise AI recorders (DingTalk A1) - Plaud-style pocket transcribers - education-focused AI translators - even the quirky “Native Language Star” muzzle device for English practice Other countries theorise about “the next big thing.” China builds 20 versions of it by lunchtime. 4️⃣ The feedback loop advantage More devices, more usage, more data, faster improvement. China’s scale accelerates AI development in ways the U.S. cannot match. 5️⃣ AI wearables are already normal here Smart eyewear, AI notetakers, live translators, all in daily use. This isn’t sci-fi. This is Tuesday in Beijing. The Global Reality: For China, the AI device race is not hypothetical, it’s happening at street level. For many outside China? They can only imagine this kind of tech and often don’t believe it when they hear about it. Anyone living in China already sees the future everywhere, in convenience stores, on commuters, in classrooms and in the hands of office workers. Meanwhile, most of the world is still arguing about “what AI devices might eventually look like.” The Hard Truth: If the next “iPhone moment” of the AI age is going to come from anywhere, the smart money is on China, a country where hardware innovation, manufacturing power and real-world adoption all move at a speed the West simply can’t match.

James Wood 武杰士

17,430 görüntüleme • 8 ay önce

Google just dropped a free coding tool that replaces your entire dev workflow. And nobody's talking about it. Here's what Google Antigravity does: → AI agents build full apps autonomously → Plans, codes, tests, fixes bugs automatically → Works across multiple files simultaneously → Opens browser and verifies everything works → 6 different AI models in one platform One developer built a working game in minutes. Another AI tool failed completely. Save this video, you'll understand where coding is headed. Want the SOP? DM me. 💬

Google just dropped a free coding tool that replaces your entire dev workflow. And nobody's talking about it. Here's what Google Antigravity does: → AI agents build full apps autonomously → Plans, codes, tests, fixes bugs automatically → Works across multiple files simultaneously → Opens browser and verifies everything works → 6 different AI models in one platform One developer built a working game in minutes. Another AI tool failed completely. Save this video, you'll understand where coding is headed. Want the SOP? DM me. 💬

Julian Goldie SEO

12,695 görüntüleme • 6 ay önce

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

186,551 görüntüleme • 4 ay önce

Robotics is getting its Raspberry Pi moment. This solves one of the biggest frictions in Physical AI: Everything has been fragmented. Different tools for control. Sensors. Calibration. Learning. Now it’s one unified stack: • motor control to VLA models in one system • built for real hardware, not just simulation -> norma_core_dev just entered PUBLIC ALPHA. The real unlock is ElRobot: • 7+1 DOF arm • fully 3D printable • ~220$ cost • ships with CAD, URDF, full build guide Not a demo… Infrastructure. And the part most people will underestimate: auto-calibration for any robot No manual tuning. No trial and error. Just: code → hardware → working system That removes one of the biggest hidden bottlenecks in robotics. Which unlocks: • faster iteration • cheaper experiments • real-world data loops Exactly what Physical AI needs. We’re moving from: “Can we make it work?” to “How fast can we improve it?” Thank you so much for sharing, Erick! 📍Repro: Credit:

Robotics is getting its Raspberry Pi moment. This solves one of the biggest frictions in Physical AI: Everything has been fragmented. Different tools for control. Sensors. Calibration. Learning. Now it’s one unified stack: • motor control to VLA models in one system • built for real hardware, not just simulation -> norma_core_dev just entered PUBLIC ALPHA. The real unlock is ElRobot: • 7+1 DOF arm • fully 3D printable • ~220$ cost • ships with CAD, URDF, full build guide Not a demo… Infrastructure. And the part most people will underestimate: auto-calibration for any robot No manual tuning. No trial and error. Just: code → hardware → working system That removes one of the biggest hidden bottlenecks in robotics. Which unlocks: • faster iteration • cheaper experiments • real-world data loops Exactly what Physical AI needs. We’re moving from: “Can we make it work?” to “How fast can we improve it?” Thank you so much for sharing, Erick! 📍Repro: Credit:

Ilir Aliu

66,329 görüntüleme • 4 ay önce

On Tuesday, I testified before the House Homeland Security Committee on China's strides in robotics and AI. I warned that we lost solar, batteries, and EVs -- now we're at risk of losing robotics and AI. If that happens, it would irreversibly change the balance of power. Five points: 1️⃣ China aims to win the next industrial revolution. PRC leaders believe history is shaped by industrial revolutions. The first, steam power, made Britain dominant. The second and third, electrification and mass manufacturing, made America dominant. China is determined to win the fourth. 2️⃣ In robotics, China is already winning. In 2024, China installed 300,000 new industrial robots. America installed 30,000. China now has over 2 million robots in its factories — five times more than the US. A decade ago, it imported 75% of its robots. Today it makes 60% domestically. This year alone, China may spend $400 billion on industrial policy. The entire US CHIPS Act provided $50 billion across multiple years. If we fall behind here, U.S. reindustrialization becomes farfetched. 3️⃣ In AI, we're ahead — but selling off the advantage. China has more energy, more talent, and makes the edge devices. But America still leads because of chips, according to China's own AI companies. US chips are 4-5x better than China's today. We are debating whether to surrender that edge. 4️⃣ We are inviting risks of cyberespionage and catastrophic cyberattacks. PRC law requires its companies to cooperate with intelligence services and never disclose it. Today's robots carry LiDAR, microphones, and cameras — they are mobile surveillance platforms. But the bigger risk is cyberattack. We know China has compromised our power, gas, water, telecommunications, and transportation infrastructure in preparation for cyberattack. We cannot deploy robots in sensitive facilities from the very country targeting those facilities. 5️⃣ Here's what we must do. Extend ICTS rules to cover Chinese robots. Direct CISA to audit where they're deployed in critical infrastructure. Ban federal procurement of Chinese robotics and AI. Strengthen semiconductor export controls. Stop treating American AI companies with more regulatory scrutiny than Chinese ones. And build allied scale in robotics—a trading bloc with preferential terms for the members that can rival China's scale in in the sector. Thanks to @HomelandDemsIt and House Homeland GOP for the hearing on this topic, and grateful to join Michael Robbins and colleagues from Scale and Boston Dynamics for a great discussion.

On Tuesday, I testified before the House Homeland Security Committee on China's strides in robotics and AI. I warned that we lost solar, batteries, and EVs -- now we're at risk of losing robotics and AI. If that happens, it would irreversibly change the balance of power. Five points: 1️⃣ China aims to win the next industrial revolution. PRC leaders believe history is shaped by industrial revolutions. The first, steam power, made Britain dominant. The second and third, electrification and mass manufacturing, made America dominant. China is determined to win the fourth. 2️⃣ In robotics, China is already winning. In 2024, China installed 300,000 new industrial robots. America installed 30,000. China now has over 2 million robots in its factories — five times more than the US. A decade ago, it imported 75% of its robots. Today it makes 60% domestically. This year alone, China may spend $400 billion on industrial policy. The entire US CHIPS Act provided $50 billion across multiple years. If we fall behind here, U.S. reindustrialization becomes farfetched. 3️⃣ In AI, we're ahead — but selling off the advantage. China has more energy, more talent, and makes the edge devices. But America still leads because of chips, according to China's own AI companies. US chips are 4-5x better than China's today. We are debating whether to surrender that edge. 4️⃣ We are inviting risks of cyberespionage and catastrophic cyberattacks. PRC law requires its companies to cooperate with intelligence services and never disclose it. Today's robots carry LiDAR, microphones, and cameras — they are mobile surveillance platforms. But the bigger risk is cyberattack. We know China has compromised our power, gas, water, telecommunications, and transportation infrastructure in preparation for cyberattack. We cannot deploy robots in sensitive facilities from the very country targeting those facilities. 5️⃣ Here's what we must do. Extend ICTS rules to cover Chinese robots. Direct CISA to audit where they're deployed in critical infrastructure. Ban federal procurement of Chinese robotics and AI. Strengthen semiconductor export controls. Stop treating American AI companies with more regulatory scrutiny than Chinese ones. And build allied scale in robotics—a trading bloc with preferential terms for the members that can rival China's scale in in the sector. Thanks to @HomelandDemsIt and House Homeland GOP for the hearing on this topic, and grateful to join Michael Robbins and colleagues from Scale and Boston Dynamics for a great discussion.

Rush Doshi

152,123 görüntüleme • 4 ay önce

higgsfield DROPPED this and it just made ai filmmaking easier than ever. they open-sourced their originals. every flagship video they produced in-house, the entire prompt is now public. the references, the generation settings, the model, the resolution, all of it sitting right there on the project page. for anyone making ai video this is the part that actually matters. the hardest thing was never the tool, it was knowing how to prompt it. what to write to get cinematic lighting, character consistency across shots, real physics, camera movement that doesn't look ai. that knowledge stayed locked inside studios and a few people who figured it out early. now it's just open. open one of their best clips, read the exact prompt that built it, copy it, download the video, rebuild the whole thing yourself.

higgsfield DROPPED this and it just made ai filmmaking easier than ever. they open-sourced their originals. every flagship video they produced in-house, the entire prompt is now public. the references, the generation settings, the model, the resolution, all of it sitting right there on the project page. for anyone making ai video this is the part that actually matters. the hardest thing was never the tool, it was knowing how to prompt it. what to write to get cinematic lighting, character consistency across shots, real physics, camera movement that doesn't look ai. that knowledge stayed locked inside studios and a few people who figured it out early. now it's just open. open one of their best clips, read the exact prompt that built it, copy it, download the video, rebuild the whole thing yourself.

Sulfur

31,111 görüntüleme • 24 gün önce

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 görüntüleme • 22 gün önce

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Dhruv Diddi

31,439 görüntüleme • 4 ay önce