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🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems... that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.show more

TheNewPhysics

54,686 subscribers

23,196 次观看 • 2 个月前 •via X (Twitter)

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Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

60,278 次观看 • 5 个月前

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

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What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

Comcast

15,708 次观看 • 5 个月前

Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?

Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?

Perceptron Network

31,985 次观看 • 7 个月前

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 次观看 • 3 个月前

You're looking at one of the strongest arguments for divine creation I've ever seen. Scientists call this a “zinc spark”, a burst of zinc released when a human egg is activated, one of the earliest events in the transition from egg to embryo. Think about what follows. From something microscopic begins an incredibly complex biological process that can ultimately produce a human being capable of thought, memory, emotion, creativity, love, and consciousness. Science can explain the mechanisms behind what we're watching. Evolution can explain how biological systems developed over time. But neither changes what I see here. I see extraordinary complexity, order, and purpose unfolding from the very beginning of human development. The more science reveals about how life works, the harder it becomes for me to look at it as blind chance. I see design. I see purpose. I see divine creation. What do you see?

You're looking at one of the strongest arguments for divine creation I've ever seen. Scientists call this a “zinc spark”, a burst of zinc released when a human egg is activated, one of the earliest events in the transition from egg to embryo. Think about what follows. From something microscopic begins an incredibly complex biological process that can ultimately produce a human being capable of thought, memory, emotion, creativity, love, and consciousness. Science can explain the mechanisms behind what we're watching. Evolution can explain how biological systems developed over time. But neither changes what I see here. I see extraordinary complexity, order, and purpose unfolding from the very beginning of human development. The more science reveals about how life works, the harder it becomes for me to look at it as blind chance. I see design. I see purpose. I see divine creation. What do you see?

fOx

60,016 次观看 • 5 天前

Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything

Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything

Whole Mars Catalog

45,801 次观看 • 1 年前

I don't care about what you want. I care about what you repeat. Because your brain does not run on intention. It runs on repetition. Every thought you keep returning to, your brain treats as a signal. And the more you send that signal, the stronger and faster that neural pathway becomes. Here is the science behind it. When you repeatedly activate a neural pathway, your brain wraps a fatty substance called myelin around it. Think of it like insulation around an electrical wire. The more myelin, the faster the signal travels. The faster the signal travels, the more automatic that thought or behavior becomes. This is Hebb's Law. Neurons that fire together, wire together. And here is the part most people miss. Your brain does not care what you are reinforcing. It does not filter for good or bad. It just responds to what you keep repeating. Repeat confidence. Your brain builds it. Repeat self-doubt. Your brain builds that too. The same biological process that wires in focus, discipline, and certainty is the exact same one wiring in anxiety, fear, and limitation. You are not wired a certain way forever. You are wired for whatever you keep practicing. So stop asking what you want to change. Start asking what you are willing to repeat. ✨🙌🏾💫

I don't care about what you want. I care about what you repeat. Because your brain does not run on intention. It runs on repetition. Every thought you keep returning to, your brain treats as a signal. And the more you send that signal, the stronger and faster that neural pathway becomes. Here is the science behind it. When you repeatedly activate a neural pathway, your brain wraps a fatty substance called myelin around it. Think of it like insulation around an electrical wire. The more myelin, the faster the signal travels. The faster the signal travels, the more automatic that thought or behavior becomes. This is Hebb's Law. Neurons that fire together, wire together. And here is the part most people miss. Your brain does not care what you are reinforcing. It does not filter for good or bad. It just responds to what you keep repeating. Repeat confidence. Your brain builds it. Repeat self-doubt. Your brain builds that too. The same biological process that wires in focus, discipline, and certainty is the exact same one wiring in anxiety, fear, and limitation. You are not wired a certain way forever. You are wired for whatever you keep practicing. So stop asking what you want to change. Start asking what you are willing to repeat. ✨🙌🏾💫

🧬Maxpein🧬

53,231 次观看 • 20 天前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

ard

424,888 次观看 • 25 天前

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.

PinLink

14,339 次观看 • 6 个月前

Experiencing concerts has changed forever—and Metallica just showed me how. Seriously, do yourself a favor and book a demo at your nearest Apple Store to watch this latest Apple immersive release on Vision Pro. It really is THAT good. While not everyone gets the chance to experience the magic of seeing legends like Metallica perform live on stage and feeling the electrifying energy of the crowd, this technology brings us as close as possible to making that experience accessible to many more people. No matter how much you may doubt this technology, after seeing this, it will be hard to deny that this could become a very common way to experience concerts in the future.

Experiencing concerts has changed forever—and Metallica just showed me how. Seriously, do yourself a favor and book a demo at your nearest Apple Store to watch this latest Apple immersive release on Vision Pro. It really is THAT good. While not everyone gets the chance to experience the magic of seeing legends like Metallica perform live on stage and feeling the electrifying energy of the crowd, this technology brings us as close as possible to making that experience accessible to many more people. No matter how much you may doubt this technology, after seeing this, it will be hard to deny that this could become a very common way to experience concerts in the future.

Phil Traut ᯅ

30,721 次观看 • 1 年前

Just weeks ago I considered this to be a map of Superchargers. The foundation that made EV’s a going concern for the first time. But it’s not. These dots represent fully permitted and built power stations in an era where every AI player is trying to figure out how to get power for compute. This is the largest electrical power moat I’ve ever seen that can fully supply current and future charging needs while collecting and storing cheaper power during off-peak to supply an AI revolution. Elon doesn’t care who knows anymore. 2nd place is… crumbs. AWS was built from excess server space needed during peak demand. Now it’s bigger and more valuable than the core Amazon retail business. Tesla is about to drop a hardware smackdown right in the face of software engineers. AI prototypes are easy. Scaling is hard. If only Tesla had a factory pouring out millions of chips each year that are underutilized 95% of the time. Each with their own battery storage. Imagine that. Tesla could call them ‘cars’. And that power moat? It’s all fully upgradable. It’s primed to be tripled. Only a handful of Tesla charging stations currently have solar and/or Megapacks. For 14 years Tesla has permitted and built the gas stations of the future around the world. Everyone said it would fail. All of them were wrong. And now Tesla is in a process of creating a possible 10x value stamp on those stations that should have never been built. It makes you wonder, When a vehicle now leaves the Tesla factory; how do we even begin to calculate the economic production that it will generate during its lifetime? It’s a Robotaxi, energy storage, and a fully sealed/cooled compute processor. They may as well start shitting gold out of a tailpipe.

Just weeks ago I considered this to be a map of Superchargers. The foundation that made EV’s a going concern for the first time. But it’s not. These dots represent fully permitted and built power stations in an era where every AI player is trying to figure out how to get power for compute. This is the largest electrical power moat I’ve ever seen that can fully supply current and future charging needs while collecting and storing cheaper power during off-peak to supply an AI revolution. Elon doesn’t care who knows anymore. 2nd place is… crumbs. AWS was built from excess server space needed during peak demand. Now it’s bigger and more valuable than the core Amazon retail business. Tesla is about to drop a hardware smackdown right in the face of software engineers. AI prototypes are easy. Scaling is hard. If only Tesla had a factory pouring out millions of chips each year that are underutilized 95% of the time. Each with their own battery storage. Imagine that. Tesla could call them ‘cars’. And that power moat? It’s all fully upgradable. It’s primed to be tripled. Only a handful of Tesla charging stations currently have solar and/or Megapacks. For 14 years Tesla has permitted and built the gas stations of the future around the world. Everyone said it would fail. All of them were wrong. And now Tesla is in a process of creating a possible 10x value stamp on those stations that should have never been built. It makes you wonder, When a vehicle now leaves the Tesla factory; how do we even begin to calculate the economic production that it will generate during its lifetime? It’s a Robotaxi, energy storage, and a fully sealed/cooled compute processor. They may as well start shitting gold out of a tailpipe.

No Safe Words

56,727 次观看 • 1 个月前

This may not seem like a big deal, but it is. Payton was born with a brain difference called holoprosencephaly. Essentially this means that the front part of her brain did not split into two separate left and right halves during early pregnancy. This causes developmental delays, temperature regulation issues, seizures, and coordination and motor control problems. She has struggled to coordinate midline over her near two years on this earth and it’s a big milestone moment when she can show that she’s developing and growing in this area. One thing that PT has wanted to see for a long time now is her laying on her side and being able to interact with objects in front of her like this…not just accidental batting, but intentional interaction. I’d say we got that with this one! We’re so proud of how much she’s growing and how hard she works!

This may not seem like a big deal, but it is. Payton was born with a brain difference called holoprosencephaly. Essentially this means that the front part of her brain did not split into two separate left and right halves during early pregnancy. This causes developmental delays, temperature regulation issues, seizures, and coordination and motor control problems. She has struggled to coordinate midline over her near two years on this earth and it’s a big milestone moment when she can show that she’s developing and growing in this area. One thing that PT has wanted to see for a long time now is her laying on her side and being able to interact with objects in front of her like this…not just accidental batting, but intentional interaction. I’d say we got that with this one! We’re so proud of how much she’s growing and how hard she works!

Kyle Yates

25,870 次观看 • 8 天前

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 次观看 • 1 年前

We expect politicians to lie most of the time. To betray us for their own gain, and to take advantage of us. We have seen and experienced this more than enough times for us to be wary of it. What we do not expect is for the family of a girl fighting for her life to deceive us and, on top of that, to use her to do so. This man, masquerading as a concerned family member and victim, has been caught claiming that he will use Yumn and stage a video "thikolhun beynun vaa gothah," and keep her there needlessly for "a long time." While I support the fight for justice and rights, this movement has been too muddied up by the elements that have infiltrated it. This is no fight for #JusticeForYumn or any other justice, as we can see in this chatlog of Mooch. I am thoroughly disappointed and disgusted, Mooch. To use her to sensationalise it just to get more cash for you is beyond words. Its no wonder sex offenders like Rashwan and liars like Shiman are organisers. A nice little symbiotic relationship between Mooch and politics.

We expect politicians to lie most of the time. To betray us for their own gain, and to take advantage of us. We have seen and experienced this more than enough times for us to be wary of it. What we do not expect is for the family of a girl fighting for her life to deceive us and, on top of that, to use her to do so. This man, masquerading as a concerned family member and victim, has been caught claiming that he will use Yumn and stage a video "thikolhun beynun vaa gothah," and keep her there needlessly for "a long time." While I support the fight for justice and rights, this movement has been too muddied up by the elements that have infiltrated it. This is no fight for #JusticeForYumn or any other justice, as we can see in this chatlog of Mooch. I am thoroughly disappointed and disgusted, Mooch. To use her to sensationalise it just to get more cash for you is beyond words. Its no wonder sex offenders like Rashwan and liars like Shiman are organisers. A nice little symbiotic relationship between Mooch and politics.

RaajjeLeaks 

193,841 次观看 • 1 年前

WIRED magazine just gave a 10/10 rating to a piece of hardware for the first time in a decade. It is a robot vacuum. Meet Matic. You do not use an app to control it. You literally treat it like a pet. You can point at a coffee spill and say "Hey Matic, clean this." You can even tell it to "follow me" and it walks behind you. It spent 9 years in development to solve the biggest smart home problem: privacy. It has 5 cameras, but it processes everything locally on an Nvidia chip. Your data is deleted in real-time. Hardware is finally getting fun again.

WIRED magazine just gave a 10/10 rating to a piece of hardware for the first time in a decade. It is a robot vacuum. Meet Matic. You do not use an app to control it. You literally treat it like a pet. You can point at a coffee spill and say "Hey Matic, clean this." You can even tell it to "follow me" and it walks behind you. It spent 9 years in development to solve the biggest smart home problem: privacy. It has 5 cameras, but it processes everything locally on an Nvidia chip. Your data is deleted in real-time. Hardware is finally getting fun again.

shirish

2,004,680 次观看 • 5 天前

A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫

A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫

RICE AI ( ◉ - ◉ )

21,024 次观看 • 1 个月前