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Jensen Huang recently visited Starbase, Texas, to personally deliver NVIDIA’s newest AI supercomputer, the DGX Spark, to Elon Musk. Despite its compact size, the DGX Spark is designed to deliver up to 1 petaflop of AI computing performance and supports up to 128GB of unified memory. The system is built around NVIDIA’s Grace Blackwell architecture and is designed to run advanced AI models locally without relying entirely on cloud infrastructure. The moment is particularly interesting because it brings together two of the most influential companies in AI and robotics. NVIDIA provides much of the computing power behind the modern AI boom, while Tesla is developing autonomous vehicles, Optimus humanoid robots, and large-scale AI systems through xAI. Almost a decade ago, Jensen Huang personally delivered one of NVIDIA’s earliest DGX systems to OpenAI. Today, he’s delivering the next generation of AI computing directly to Elon Musk at Starbase. As AI models continue to grow in capability, powerful local AI systems like the DGX Spark could become increasingly important for developers, researchers, robotics teams, and AI startups. The future of AI won’t just live in massive data centers. More of it may soon fit on a desk. • Follow @meteoric.ai for more content on AI and technology. Media: NVIDIA #ai #nvidia #elonmusk #technology #innovation
Jensen Huang recently visited Starbase, Texas, to personally deliver NVIDIA’s newest AI supercomputer, the DGX Spark, to Elon Musk. Despite its compact size, the DGX Spark is designed to deliver up to 1 petaflop of AI computing performance and supports up to 128GB of unified memory. The system is built around NVIDIA’s Grace Blackwell architecture and is designed to run advanced AI models locally without relying entirely on cloud infrastructure. The moment is particularly interesting because it brings together two of the most influential companies in AI and robotics. NVIDIA provides much of the computing power behind the modern AI boom, while Tesla is developing autonomous vehicles, Optimus humanoid robots, and large-scale AI systems through xAI. Almost a decade ago, Jensen Huang personally delivered one of NVIDIA’s earliest DGX systems to OpenAI. Today, he’s delivering the next generation of AI computing directly to Elon Musk at Starbase. As AI models continue to grow in capability, powerful local AI systems like the DGX Spark could become increasingly important for developers, researchers, robotics teams, and AI startups. The future of AI won’t just live in massive data centers. More of it may soon fit on a desk. • Follow @meteoric.ai for more content on AI and technology. Media: NVIDIA #ai #nvidia #elonmusk #technology #innovation
The data center rack is where all the computing happens. A rack is a standardized enclosure that houses servers, switches, storage devices, and power distribution units (PDUs). Modern AI and hyperscale data centers can have rack power densities ranging from 5 kW to over 100 kW, making efficient cooling and cable management more important than ever. In this video, you’ll learn: ✅ What a data center rack is ✅ Standard 19-inch rack and rack units (U) ✅ Components inside a server rack ✅ Hot aisle & cold aisle arrangement ✅ High-density AI racks and their cooling requirements Follow for more real-world data center engineering, HVAC, and mission-critical infrastructure content. #datacenter #datacenters #serverrack #rack #server GPU CPU PDU AI AIinfrastructure hyperscale cloudcomputing datacentercooling engineering mechanicalengineering thermalmanagement digitalinfrastructure missioncritical technology tech networking serverroom buildingservices facilitymanagement usa
The data center rack is where all the computing happens. A rack is a standardized enclosure that houses servers, switches, storage devices, and power distribution units (PDUs). Modern AI and hyperscale data centers can have rack power densities ranging from 5 kW to over 100 kW, making efficient cooling and cable management more important than ever. In this video, you’ll learn: ✅ What a data center rack is ✅ Standard 19-inch rack and rack units (U) ✅ Components inside a server rack ✅ Hot aisle & cold aisle arrangement ✅ High-density AI racks and their cooling requirements Follow for more real-world data center engineering, HVAC, and mission-critical infrastructure content. #datacenter #datacenters #serverrack #rack #server GPU CPU PDU AI AIinfrastructure hyperscale cloudcomputing datacentercooling engineering mechanicalengineering thermalmanagement digitalinfrastructure missioncritical technology tech networking serverroom buildingservices facilitymanagement usa
Follow us @oddsontechnology for more technology history and rare tech archives! Steve Jobs basically described the philosophy behind the MacBook in 1983, decades before Apple would actually use the MacBook name. Speaking at the International Design Conference in Aspen, Jobs discussed Apple’s approach to portable computing. Rather than rushing a cheap, compromised portable computer to market, he described another approach: design the ideal computer first, even if the technology was initially too large, then continuously shrink it as hardware improved. It sounds remarkably familiar when looking back through the history of the Macintosh, PowerBook, iBook, MacBook, MacBook Pro and MacBook Air. The first MacBook would not arrive until 2006, more than two decades later. Jobs was not literally predicting a specific future MacBook model, but the clip captures the product philosophy that would eventually define Apple: figure out what the ideal computer should be, then wait for technology to make it possible. #SteveJobs #Apple #MacBook #iPhone #iPad
Follow us @oddsontechnology for more technology history and rare tech archives! Steve Jobs basically described the philosophy behind the MacBook in 1983, decades before Apple would actually use the MacBook name. Speaking at the International Design Conference in Aspen, Jobs discussed Apple’s approach to portable computing. Rather than rushing a cheap, compromised portable computer to market, he described another approach: design the ideal computer first, even if the technology was initially too large, then continuously shrink it as hardware improved. It sounds remarkably familiar when looking back through the history of the Macintosh, PowerBook, iBook, MacBook, MacBook Pro and MacBook Air. The first MacBook would not arrive until 2006, more than two decades later. Jobs was not literally predicting a specific future MacBook model, but the clip captures the product philosophy that would eventually define Apple: figure out what the ideal computer should be, then wait for technology to make it possible. #SteveJobs #Apple #MacBook #iPhone #iPad
What if I shrink into my computer? If I jump into my computer and I see it’s getting hot – that whirring noise - what is happening? All computers need to get the heat off the chip as quickly as possible and find somewhere to dump it This is a HEAT problem that’s holding back virtually all our cutting edge computers, because most of what’s around me right now is made of… silicon. But now there’s a new material we think might be able to help. To see what, watch our longer video and follow along! #computer #silicon #diamond #heat #computing
What if I shrink into my computer? If I jump into my computer and I see it’s getting hot – that whirring noise - what is happening? All computers need to get the heat off the chip as quickly as possible and find somewhere to dump it This is a HEAT problem that’s holding back virtually all our cutting edge computers, because most of what’s around me right now is made of… silicon. But now there’s a new material we think might be able to help. To see what, watch our longer video and follow along! #computer #silicon #diamond #heat #computing
Jensen Huang is the founder and CEO of NVIDIA, the company that became a central force in AI computing through its GPUs and AI infrastructure. Known for leading NVIDIA’s transformation from a graphics chip company into a global AI technology leader, Huang frequently speaks about how AI will reshape industries, productivity, and the future of work. In this clip, he explains that AI itself may not replace people, but individuals who understand how to effectively use and operate AI tools could gain a major advantage. . . . Jensen Huang AI future, artificial intelligence, NVIDIA CEO, AI productivity, future of work, technology leadership, AI tools, business innovation, automation . . . #ArtificialIntelligence #BusinessInnovation #FutureOfWork
Jensen Huang is the founder and CEO of NVIDIA, the company that became a central force in AI computing through its GPUs and AI infrastructure. Known for leading NVIDIA’s transformation from a graphics chip company into a global AI technology leader, Huang frequently speaks about how AI will reshape industries, productivity, and the future of work. In this clip, he explains that AI itself may not replace people, but individuals who understand how to effectively use and operate AI tools could gain a major advantage. . . . Jensen Huang AI future, artificial intelligence, NVIDIA CEO, AI productivity, future of work, technology leadership, AI tools, business innovation, automation . . . #ArtificialIntelligence #BusinessInnovation #FutureOfWork
Jensen Huang, co-founder and CEO of NVIDIA, saw the potential of GPUs long before the AI boom. Back in 2009, when most people viewed GPUs as gaming hardware, Huang explained how parallel computing would transform scientific computing and solve problems traditional CPUs could not. That vision later became the foundation for modern AI, powered by NVIDIA’s CUDA platform and GPUs. Follow (@entrepreneursonig) for everything related to entrepreneurs. Media: Charlie Rose
Jensen Huang, co-founder and CEO of NVIDIA, saw the potential of GPUs long before the AI boom. Back in 2009, when most people viewed GPUs as gaming hardware, Huang explained how parallel computing would transform scientific computing and solve problems traditional CPUs could not. That vision later became the foundation for modern AI, powered by NVIDIA’s CUDA platform and GPUs. Follow (@entrepreneursonig) for everything related to entrepreneurs. Media: Charlie Rose
Neural scaling laws show how AI models improve as we increase data, model size, and computing power. The key idea is that these improvements follow predictable patterns. Larger models trained on more data usually achieve better performance in a measurable way. This helps researchers decide how to use limited computing resources and find the best balance between model size, data, and compute. Credits: Welch Labs
Neural scaling laws show how AI models improve as we increase data, model size, and computing power. The key idea is that these improvements follow predictable patterns. Larger models trained on more data usually achieve better performance in a measurable way. This helps researchers decide how to use limited computing resources and find the best balance between model size, data, and compute. Credits: Welch Labs
Larry Page predicted years ago that AI progress would depend more on computing power than on constantly inventing new algorithms. Back in 2007, he argued that intelligence might come from giving systems enough compute and data rather than searching for a single breakthrough idea. Looking back, that prediction was surprisingly accurate. Much of the progress in modern AI has come from scaling up chips, data centers, and training data rather than completely reinventing the underlying technology. Follow us (👉@artificialintelligenceee) for everything latest from the AI world. Media: Google TechTalks
Larry Page predicted years ago that AI progress would depend more on computing power than on constantly inventing new algorithms. Back in 2007, he argued that intelligence might come from giving systems enough compute and data rather than searching for a single breakthrough idea. Looking back, that prediction was surprisingly accurate. Much of the progress in modern AI has come from scaling up chips, data centers, and training data rather than completely reinventing the underlying technology. Follow us (👉@artificialintelligenceee) for everything latest from the AI world. Media: Google TechTalks
For someone who created two of the most important pieces of software in modern computing, Linus Torvalds has a surprisingly wholesome choice for his desktop wallpaper. During an appearance with Linus Tech Tips, people noticed that the creator of Linux and Git wasn't using an elaborate Linux themed background or some ultra technical programmer setup. Instead, his wallpaper was simply a peaceful nighttime nature photograph that he had taken himself. It's a small detail, but it feels oddly fitting. Torvalds' work now sits at the foundation of an enormous part of the digital world, from servers and cloud infrastructure to supercomputers, Android devices, software development, and the wider open source ecosystem. Yet the image he chooses to look at every day is essentially just a beautiful photo he captured in nature. Sometimes the most wholesome moments in tech history have nothing to do with code, computers, or groundbreaking inventions. Sometimes, they're simply a reminder that behind some of the technology shaping the modern world is a person who just wanted to use a nice photo they took as their wallpaper. Media: Linus Tech Tips | YT Follow @archiveofceos for inspiring stories, timeless business wisdom, and lessons from the world's greatest leaders.
For someone who created two of the most important pieces of software in modern computing, Linus Torvalds has a surprisingly wholesome choice for his desktop wallpaper. During an appearance with Linus Tech Tips, people noticed that the creator of Linux and Git wasn't using an elaborate Linux themed background or some ultra technical programmer setup. Instead, his wallpaper was simply a peaceful nighttime nature photograph that he had taken himself. It's a small detail, but it feels oddly fitting. Torvalds' work now sits at the foundation of an enormous part of the digital world, from servers and cloud infrastructure to supercomputers, Android devices, software development, and the wider open source ecosystem. Yet the image he chooses to look at every day is essentially just a beautiful photo he captured in nature. Sometimes the most wholesome moments in tech history have nothing to do with code, computers, or groundbreaking inventions. Sometimes, they're simply a reminder that behind some of the technology shaping the modern world is a person who just wanted to use a nice photo they took as their wallpaper. Media: Linus Tech Tips | YT Follow @archiveofceos for inspiring stories, timeless business wisdom, and lessons from the world's greatest leaders.
Geoffrey Hinton (born 1947) is widely considered the “Godfather of AI.” A pioneering computer scientist and cognitive psychologist, Hinton’s work on artificial neural networks laid the foundation for modern AI systems. He is a recipient of the Turing Award, often referred to as the Nobel Prize of computing, and his breakthroughs helped make technologies like ChatGPT, Gemini, Claude, and other large language models possible. Which is why his message is so striking. Despite helping create the technology, Hinton says we still don’t fully understand how AI works internally. Researchers can train advanced neural networks, measure their performance, and observe their behavior. But the exact way these systems develop their reasoning, representations, and decision-making processes remains only partially understood. Hinton compares it to building something incredibly powerful before fully understanding every mechanism inside it. His concern isn’t what AI can do today. It’s what happens as these systems become increasingly capable while remaining, in many ways, a black box. For one of AI’s most influential pioneers, that uncertainty may be one of the most important challenges facing the industry. • Follow @meteoric.ai for more content on AI and technology. Media: 60 Minutes #ai #artificialintelligence #geoffreyhinton #technology #future
Geoffrey Hinton (born 1947) is widely considered the “Godfather of AI.” A pioneering computer scientist and cognitive psychologist, Hinton’s work on artificial neural networks laid the foundation for modern AI systems. He is a recipient of the Turing Award, often referred to as the Nobel Prize of computing, and his breakthroughs helped make technologies like ChatGPT, Gemini, Claude, and other large language models possible. Which is why his message is so striking. Despite helping create the technology, Hinton says we still don’t fully understand how AI works internally. Researchers can train advanced neural networks, measure their performance, and observe their behavior. But the exact way these systems develop their reasoning, representations, and decision-making processes remains only partially understood. Hinton compares it to building something incredibly powerful before fully understanding every mechanism inside it. His concern isn’t what AI can do today. It’s what happens as these systems become increasingly capable while remaining, in many ways, a black box. For one of AI’s most influential pioneers, that uncertainty may be one of the most important challenges facing the industry. • Follow @meteoric.ai for more content on AI and technology. Media: 60 Minutes #ai #artificialintelligence #geoffreyhinton #technology #future
This meme humorously compares the computing power available during the Apollo era with the expectations of modern computer users. It points out that the Apollo Guidance Computer, which helped NASA successfully land astronauts on the Moon in 1969, operated with only a few kilobytes of memory. Despite these incredibly limited resources by today's standards, it was capable of supporting one of humanity's greatest technological achievements. In contrast, many modern devices come equipped with gigabytes of RAM that are millions of times more powerful than the computers used during the Moon missions. Yet people still find themselves frustrated when a web browser with several open tabs begins to slow down. The meme exaggerates this contrast for comedic effect, highlighting how dramatically technology has advanced over the past few decades. The joke also reflects how software has evolved alongside hardware. Modern applications, websites, and operating systems perform far more complex tasks than the programs that guided Apollo spacecraft. As a result, they consume much more memory and processing power, even for everyday activities like browsing the internet. While the comparison is simplified, it serves as a fascinating reminder of what engineers were able to accomplish with the technology of their time. The fact that humans reached the Moon with computers far less powerful than today's smartphones remains one of the most impressive achievements in the history of science and engineering. #ai #ram #NASA #billionaire #moon
This meme humorously compares the computing power available during the Apollo era with the expectations of modern computer users. It points out that the Apollo Guidance Computer, which helped NASA successfully land astronauts on the Moon in 1969, operated with only a few kilobytes of memory. Despite these incredibly limited resources by today's standards, it was capable of supporting one of humanity's greatest technological achievements. In contrast, many modern devices come equipped with gigabytes of RAM that are millions of times more powerful than the computers used during the Moon missions. Yet people still find themselves frustrated when a web browser with several open tabs begins to slow down. The meme exaggerates this contrast for comedic effect, highlighting how dramatically technology has advanced over the past few decades. The joke also reflects how software has evolved alongside hardware. Modern applications, websites, and operating systems perform far more complex tasks than the programs that guided Apollo spacecraft. As a result, they consume much more memory and processing power, even for everyday activities like browsing the internet. While the comparison is simplified, it serves as a fascinating reminder of what engineers were able to accomplish with the technology of their time. The fact that humans reached the Moon with computers far less powerful than today's smartphones remains one of the most impressive achievements in the history of science and engineering. #ai #ram #NASA #billionaire #moon
In 1989, a “portable” laptop could could cost thousands of dollars. This old Computer Chronicles clip shows early laptop computers selling for around $5,000 to $11,000, with specs like monochrome screens, 20 to 40 megabyte hard drives, floppy drives, and optional modems. At the time, that kind of portable computing power was mostly a luxury business tool, not something the average person was buying for school, work, or watching videos on the couch. That is what makes consumer technology so interesting. A device that used to cost thousands of dollars and feel futuristic can eventually become cheaper, faster, lighter, and normal enough to throw in a backpack. Not everything gets more affordable over time, but consumer technology is one area where competition, scale, and innovation have completely changed what people can buy.
In 1989, a “portable” laptop could could cost thousands of dollars. This old Computer Chronicles clip shows early laptop computers selling for around $5,000 to $11,000, with specs like monochrome screens, 20 to 40 megabyte hard drives, floppy drives, and optional modems. At the time, that kind of portable computing power was mostly a luxury business tool, not something the average person was buying for school, work, or watching videos on the couch. That is what makes consumer technology so interesting. A device that used to cost thousands of dollars and feel futuristic can eventually become cheaper, faster, lighter, and normal enough to throw in a backpack. Not everything gets more affordable over time, but consumer technology is one area where competition, scale, and innovation have completely changed what people can buy.
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