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PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than...

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Dr. Filippo Cademartiri

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11,820 次观看 • 1 年前 •via X (Twitter)

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LN Tanenbaum MD FACR 的头像
LN Tanenbaum MD FACR1 年前

Other than the high resolution and the dose benefits what justifies the very high cost and operational challenges over a spectral CT?

Vinson Yew 的头像
Vinson Yew1 年前

Are all of these non contrast?! And how long does it take to acquire that angiogram and wall motion stuff

Dr. Filippo Cademartiri 的头像
Dr. Filippo Cademartiri1 年前

It's without and with contrast. The examination is all the same (3-5 min between entering and exiting the scan room).

Alberto Alonso Burgos 的头像
Alberto Alonso Burgos1 年前

Absolutely agree with this @FCademartiri . It's like going from black-and-white to colour photography. We started working with him in September at our hospital. Still so much to explore!

Dr. Filippo Cademartiri 的头像
Dr. Filippo Cademartiri1 年前

Exactly!

Raffaele Di Giacomo, PhD 的头像
Raffaele Di Giacomo, PhD1 年前

Indeed, Photon Counting CT is quite revolutionary. While it introduces new capabilities, particularly in resolution and functional imaging, it also raises interesting questions about its integration into clinical practice. How might this affect diagnostic approaches or patient outcomes compared to traditional CT? For those delving deeper into the biomedical implications of such innovations, is a valuable resource, capable of generating comprehensive biomedical reviews. #Medicine #ImagingInnovation

Aydin Omer 的头像
Aydin Omer1 年前

I don't think so; I have compared the two many times when examining the temporal bone, and there was almost no difference in daily routine 😑

Dr. Filippo Cademartiri 的头像
Dr. Filippo Cademartiri1 年前

Did you check the quality of the images of the mid and inner ear?

Paul Langton 的头像
Paul Langton1 年前

IMO, very useful for heavily calcified coronaries & smaller diameter stents. Makes little difference in the vast majority of CTCAs. Hard to justify the large increase in capital & operating costs. Will be interesting to see how it evolves, particularly functional imaging?

Dr. Filippo Cademartiri 的头像
Dr. Filippo Cademartiri1 年前

Not really. The point is that it is not a CT. It works as CT with the highest performance, it is an MR in several fields and in the next future it will be a PET as well. This is how you should frame the topic.

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654,903 次观看 • 1 个月前

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21,800 次观看 • 1 年前

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Rafael Grossmann, MD, MSHS, FACS 🇻🇪🇺🇸

108,028 次观看 • 2 年前

At 35 I was diagnosed with metastatic breast cancer. 80% of my tumor shows neuroendocrine differentiation. The Ga-68 scan lit up like a Christmas tree, clear proof of high somatostatin receptor expression. The World Health Organization classified this as a distinct subtype back in 2019. American and European guidelines already have specific treatment pathways… but in Spain we still don’t. So I’m being treated as standard breast cancer by default. That gap could cost me access to therapies that science already knows may work for my exact molecular profile. Time is not on my side with metastasis. I can’t wait years for the system to catch up. My oncology team is fighting hard for me within the current protocols, they are not the problem. The guidelines are. I’ve put all my molecular data, imaging and case details on so other patients and researchers can use it. If you share this video, you’re helping me get the right treatments faster… and you might be saving someone else’s life too. Please watch until the end and share if you can. Together we can push for change. 💪🎗️
1:56

At 35 I was diagnosed with metastatic breast cancer. 80% of my tumor shows neuroendocrine differentiation. The Ga-68 scan lit up like a Christmas tree, clear proof of high somatostatin receptor expression. The World Health Organization classified this as a distinct subtype back in 2019. American and European guidelines already have specific treatment pathways… but in Spain we still don’t. So I’m being treated as standard breast cancer by default. That gap could cost me access to therapies that science already knows may work for my exact molecular profile. Time is not on my side with metastasis. I can’t wait years for the system to catch up. My oncology team is fighting hard for me within the current protocols, they are not the problem. The guidelines are. I’ve put all my molecular data, imaging and case details on so other patients and researchers can use it. If you share this video, you’re helping me get the right treatments faster… and you might be saving someone else’s life too. Please watch until the end and share if you can. Together we can push for change. 💪🎗️

Míriam González

11,350 次观看 • 1 个月前

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12:55

It is vital that what happened to my daughter Paloma is properly and independently investigated, not only for her sake but because the clinicians involved continue to work in frontline, high-risk roles including anaesthesia, intensive care, air ambulance medicine, and medical training. Where sworn evidence, drug charts, observation data, and imaging raise serious questions about excessive dosing of paralysing agents, failure to recognise classic signs of adrenaline-related pulmonary oedema, and delays or inconsistencies in clinical decision-making, the issue is no longer private or historical. It becomes a matter of public safety. Patients are entitled to expect that doctors administering powerful drugs such as adrenaline and rocuronium understand their effects, recognise deterioration when it occurs, and act promptly to correct it. If those standards were not met here, and if that has not been transparently examined, then the public may remain at risk. This is why scrutiny, accountability, and full investigation are not optional, and why this matter is plainly in the public interest.

Kate Shemirani

24,117 次观看 • 5 个月前

Through Technology, a Centuries-Old Battle Is Coming to a Head "A Discussion of Open-Source AI with Travis Oliphant" Travis Oliphant Travis Oliphant is an entrepreneur and CEO and co founder of OpenTeams (OpenTeams®), a company that “helps organizations deploy, support, and own [AI] technology at enterprise scale.” One of their tag lines is “Connecting Companies with Communities.” As a data scientist and software developer, Travis is known for his contributions to Python and as the creator of NumPy and a founding contributor to SciPy, which together formed a foundation for modern AI and machine learning. When I was in Salt Lake City in the first week of June, Travis and I went into a studio to record this interview, continuing a conversation we had started when Travis and his team visited the Solari team in the Netherlands in early 2026. Our discussion focuses on how we can understand and manage the growing presence of AI in our lives. Travis has an impressive intellectual and entrepreneurial background. After earning Bachelor and Master of Science degrees in mathematics and electrical engineering at Brigham Young University, he completed a PhD in biomedical engineering at the Mayo Clinic. As an assistant professor at Brigham Young’s Department of Electrical and Computer Engineering from 2001 to 2007, he directed the Biomedical Imaging Lab, where his research centered on computational imaging techniques. He then went on to start several companies, each time identifying the need for a new standard, building the open-source infrastructure, and helping enterprises adopt it at scale. Brilliant, open-minded, deeply caring, and generous, Travis is someone who can help us understand what is happening and what we do about it. My hope is this will be the first of many conversations as we navigate the acceleration in technological innovation (and skullduggery) and seek to ensure that tools such as AI serve the health and prosperity of a human civilization. Full Report: Subscribe to
3:51

Through Technology, a Centuries-Old Battle Is Coming to a Head "A Discussion of Open-Source AI with Travis Oliphant" Travis Oliphant Travis Oliphant is an entrepreneur and CEO and co founder of OpenTeams (OpenTeams®), a company that “helps organizations deploy, support, and own [AI] technology at enterprise scale.” One of their tag lines is “Connecting Companies with Communities.” As a data scientist and software developer, Travis is known for his contributions to Python and as the creator of NumPy and a founding contributor to SciPy, which together formed a foundation for modern AI and machine learning. When I was in Salt Lake City in the first week of June, Travis and I went into a studio to record this interview, continuing a conversation we had started when Travis and his team visited the Solari team in the Netherlands in early 2026. Our discussion focuses on how we can understand and manage the growing presence of AI in our lives. Travis has an impressive intellectual and entrepreneurial background. After earning Bachelor and Master of Science degrees in mathematics and electrical engineering at Brigham Young University, he completed a PhD in biomedical engineering at the Mayo Clinic. As an assistant professor at Brigham Young’s Department of Electrical and Computer Engineering from 2001 to 2007, he directed the Biomedical Imaging Lab, where his research centered on computational imaging techniques. He then went on to start several companies, each time identifying the need for a new standard, building the open-source infrastructure, and helping enterprises adopt it at scale. Brilliant, open-minded, deeply caring, and generous, Travis is someone who can help us understand what is happening and what we do about it. My hope is this will be the first of many conversations as we navigate the acceleration in technological innovation (and skullduggery) and seek to ensure that tools such as AI serve the health and prosperity of a human civilization. Full Report: Subscribe to

The Solari Report | Catherine Austin Fitts

10,858 次观看 • 17 天前

What if everything we thought we knew about cholesterol and heart disease risk… doesn’t apply to everyone? In this episode, world-renowned cardiologist Dr. Matthew Budoff unpacks the results of a landmark one-year study tracking 100 lean, metabolically healthy individuals on a ketogenic diet with extremely elevated LDL levels. Dr. Budoff is the Program Director, Director of Cardiac CT, and the endowed chair of preventive cardiology at The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center. In this interview, Dr. Scher and Dr. Budoff further break down the results of his new publication, which used advanced imaging to demonstrate that LDL cholesterol and ApoB levels are not associated with plaque progression in Lean-Mass Hyper Responders following a #ketogenicdiet. 📊 Surprising insights: • Elevated LDL and ApoB did not predict plaque progression • Some participants with LDLs over 500 showed no plaque at all • A few participants even experienced plaque regression • Existing plaque—not LDL-C or ApoB—did predict plaque accumulation in this population Dr. Budoff explains what these results mean for clinicians, for patients using ketogenic therapy as a medical intervention, and for the broader conversation around cardiovascular disease risk. “It is important that clinicians, along with the general public, are made aware that personalized, data-driven approaches to assessing risk should be considered based on individual conditions,” said Dr. Budoff. “The existence of this phenotype suggests that alternative markers or tests should be used to establish metabolic health in some cases.” 🎬 These exciting new findings are featured in Dave Feldman and Jen Isenhart’s upcoming documentary, The Cholesterol Code, the story of how a software engineer conducts a groundbreaking study on an unusual group of people—lean, healthy individuals whose doctors are convinced they’ll die young. Real stories of healing with ketogenic diets provide a blueprint for using food as powerful medicine. Visit to learn more about the film and to be the first to hear about private screenings and the general release in the fall. Expert Featured: Dr. Matthew Budoff Matthew Budoff MD Resources Mentioned: Plaque Begets Plaque, ApoB Does Not Diagnostic and Preventative Cardiovascular Imaging Center CMEs Mentioned: Managing Major Mental Illness with Dietary Change: The New Science of Hope Brain Energy: The Metabolic Theory of Mental Illness Learn more about metabolic psychiatry and find helpful resources at
19:42

What if everything we thought we knew about cholesterol and heart disease risk… doesn’t apply to everyone? In this episode, world-renowned cardiologist Dr. Matthew Budoff unpacks the results of a landmark one-year study tracking 100 lean, metabolically healthy individuals on a ketogenic diet with extremely elevated LDL levels. Dr. Budoff is the Program Director, Director of Cardiac CT, and the endowed chair of preventive cardiology at The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center. In this interview, Dr. Scher and Dr. Budoff further break down the results of his new publication, which used advanced imaging to demonstrate that LDL cholesterol and ApoB levels are not associated with plaque progression in Lean-Mass Hyper Responders following a #ketogenicdiet. 📊 Surprising insights: • Elevated LDL and ApoB did not predict plaque progression • Some participants with LDLs over 500 showed no plaque at all • A few participants even experienced plaque regression • Existing plaque—not LDL-C or ApoB—did predict plaque accumulation in this population Dr. Budoff explains what these results mean for clinicians, for patients using ketogenic therapy as a medical intervention, and for the broader conversation around cardiovascular disease risk. “It is important that clinicians, along with the general public, are made aware that personalized, data-driven approaches to assessing risk should be considered based on individual conditions,” said Dr. Budoff. “The existence of this phenotype suggests that alternative markers or tests should be used to establish metabolic health in some cases.” 🎬 These exciting new findings are featured in Dave Feldman and Jen Isenhart’s upcoming documentary, The Cholesterol Code, the story of how a software engineer conducts a groundbreaking study on an unusual group of people—lean, healthy individuals whose doctors are convinced they’ll die young. Real stories of healing with ketogenic diets provide a blueprint for using food as powerful medicine. Visit to learn more about the film and to be the first to hear about private screenings and the general release in the fall. Expert Featured: Dr. Matthew Budoff Matthew Budoff MD Resources Mentioned: Plaque Begets Plaque, ApoB Does Not Diagnostic and Preventative Cardiovascular Imaging Center CMEs Mentioned: Managing Major Mental Illness with Dietary Change: The New Science of Hope Brain Energy: The Metabolic Theory of Mental Illness Learn more about metabolic psychiatry and find helpful resources at

Metabolic Mind

10,423 次观看 • 1 年前

The LUCAS device, also known as the Lund University Cardiopulmonary Assist System, is revolutionizing the way we perform CPR (Cardiopulmonary Resuscitation). This mechanical marvel is designed to deliver high-quality chest compressions during cardiac arrest, ensuring consistent and uninterrupted compressions that are crucial for maintaining blood circulation and oxygen delivery to vital organs. How does LUCAS work? Imagine a portable, easy-to-use device that can seamlessly integrate into your emergency response protocol. The LUCAS device does just that, providing consistent and high-quality chest compressions, even during transport or in challenging environments. This frees up healthcare providers to focus on other critical tasks during resuscitation efforts, optimizing the overall care for the patient. Why is LUCAS so important? Manual chest compressions, while essential, can be inconsistent and tiring for healthcare providers, especially during prolonged resuscitation attempts. The LUCAS device ensures that the patient receives the necessary compressions at the correct depth and rate, enhancing the chances of successful resuscitation. Benefits of using LUCAS: Improved chest compression quality:LUCAS consistently delivers high-quality chest compressions, even in challenging situations. Reduced provider fatigue:By taking over chest compressions, LUCAS allows healthcare providers to focus on other critical tasks. Enhanced patient outcomes:Consistent and effective chest compressions are crucial for improving patient survival rates after cardiac arrest. Where is LUCAS used? The LUCAS device is becoming increasingly popular in hospitals and emergency medical services worldwide. Its portability and ease of use make it ideal for use in various settings, including ambulances, operating rooms, and even during transport. The future of CPR: The LUCAS device represents a significant advancement in CPR technology. Its ability to deliver consistent and high-quality chest compressions is revolutionizing the way we respond to cardiac arrest, ultimately improving patient outcomes. As technology continues to evolve, we can expect even more innovative solutions to emerge in the field of CPR, further enhancing our ability to save lives."
1:00

The LUCAS device, also known as the Lund University Cardiopulmonary Assist System, is revolutionizing the way we perform CPR (Cardiopulmonary Resuscitation). This mechanical marvel is designed to deliver high-quality chest compressions during cardiac arrest, ensuring consistent and uninterrupted compressions that are crucial for maintaining blood circulation and oxygen delivery to vital organs. How does LUCAS work? Imagine a portable, easy-to-use device that can seamlessly integrate into your emergency response protocol. The LUCAS device does just that, providing consistent and high-quality chest compressions, even during transport or in challenging environments. This frees up healthcare providers to focus on other critical tasks during resuscitation efforts, optimizing the overall care for the patient. Why is LUCAS so important? Manual chest compressions, while essential, can be inconsistent and tiring for healthcare providers, especially during prolonged resuscitation attempts. The LUCAS device ensures that the patient receives the necessary compressions at the correct depth and rate, enhancing the chances of successful resuscitation. Benefits of using LUCAS: Improved chest compression quality:LUCAS consistently delivers high-quality chest compressions, even in challenging situations. Reduced provider fatigue:By taking over chest compressions, LUCAS allows healthcare providers to focus on other critical tasks. Enhanced patient outcomes:Consistent and effective chest compressions are crucial for improving patient survival rates after cardiac arrest. Where is LUCAS used? The LUCAS device is becoming increasingly popular in hospitals and emergency medical services worldwide. Its portability and ease of use make it ideal for use in various settings, including ambulances, operating rooms, and even during transport. The future of CPR: The LUCAS device represents a significant advancement in CPR technology. Its ability to deliver consistent and high-quality chest compressions is revolutionizing the way we respond to cardiac arrest, ultimately improving patient outcomes. As technology continues to evolve, we can expect even more innovative solutions to emerge in the field of CPR, further enhancing our ability to save lives."

Fatai Ibrahim, RN,MSN 🇳🇬🇺🇸

54,828 次观看 • 1 年前

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,048 次观看 • 7 个月前

Researchers at Tokamak Energy have captured for the first time a real-time, high-speed video of plasma behaviour inside their ST40 spherical tokamak, tracking visible green and red light emissions as the fusion process occurs. This visual insight comes via a camera operating at thousands of frames per second, offering unprecedented detail of how the plasma evolves, interacts with the surrounding lithium blanket and outer regions, and ultimately radiates energy. The imaging enables scientists to observe how the ultra-hot core transitions outward into cooler zones, how magnetic confinement shapes the plasma behaviour, and how impurities or outer-region interactions influence the process. By giving a ‘star-in-a-donut’ view of fusion in action, this breakthrough adds a new diagnostic tool to the development of fusion energy, helping engineers refine the magnetic confinement, optimise plasma stability and better understand the heat and light flows at play. It was slowed down by 100x. All this was for 0.3s A tokamak is one of the most advanced devices ever created to achieve controlled nuclear fusion, the same process that powers the Sun. Its goal is simple in principle but incredibly challenging in practice: heat a gas until it becomes plasma, raise that plasma to over 100 million degrees, and confine it long enough for hydrogen nuclei to fuse and release energy. Because no material container can survive such temperatures, a tokamak uses powerful magnetic fields to hold and shape the plasma like an invisible cage. The device has a distinctive doughnut-shaped (toroidal) chamber surrounded by magnetic coils. When the machine is switched on, electric currents and external magnets work together to create helical magnetic fields that trap the plasma and keep it away from the walls. As the plasma spirals around these magnetic lines, it heats up dramatically. Additional heating comes from methods like radio-frequency waves and neutral-beam injection, pushing the plasma toward the extreme temperatures needed for fusion. Inside this tightly controlled environment, hydrogen isotopes such as deuterium and tritium can collide and fuse, releasing fast neutrons and a burst of energy. The goal of tokamak research is to reach a point where the fusion reactions produce more energy than the system consumes, a milestone known as “net energy gain.” Modern machines like ITER, JET, and Tokamak Energy’s ST40 are bringing this vision closer, using advanced diagnostics, superconducting magnets, and increasingly stable plasma control. 👉
0:33

Researchers at Tokamak Energy have captured for the first time a real-time, high-speed video of plasma behaviour inside their ST40 spherical tokamak, tracking visible green and red light emissions as the fusion process occurs. This visual insight comes via a camera operating at thousands of frames per second, offering unprecedented detail of how the plasma evolves, interacts with the surrounding lithium blanket and outer regions, and ultimately radiates energy. The imaging enables scientists to observe how the ultra-hot core transitions outward into cooler zones, how magnetic confinement shapes the plasma behaviour, and how impurities or outer-region interactions influence the process. By giving a ‘star-in-a-donut’ view of fusion in action, this breakthrough adds a new diagnostic tool to the development of fusion energy, helping engineers refine the magnetic confinement, optimise plasma stability and better understand the heat and light flows at play. It was slowed down by 100x. All this was for 0.3s A tokamak is one of the most advanced devices ever created to achieve controlled nuclear fusion, the same process that powers the Sun. Its goal is simple in principle but incredibly challenging in practice: heat a gas until it becomes plasma, raise that plasma to over 100 million degrees, and confine it long enough for hydrogen nuclei to fuse and release energy. Because no material container can survive such temperatures, a tokamak uses powerful magnetic fields to hold and shape the plasma like an invisible cage. The device has a distinctive doughnut-shaped (toroidal) chamber surrounded by magnetic coils. When the machine is switched on, electric currents and external magnets work together to create helical magnetic fields that trap the plasma and keep it away from the walls. As the plasma spirals around these magnetic lines, it heats up dramatically. Additional heating comes from methods like radio-frequency waves and neutral-beam injection, pushing the plasma toward the extreme temperatures needed for fusion. Inside this tightly controlled environment, hydrogen isotopes such as deuterium and tritium can collide and fuse, releasing fast neutrons and a burst of energy. The goal of tokamak research is to reach a point where the fusion reactions produce more energy than the system consumes, a milestone known as “net energy gain.” Modern machines like ITER, JET, and Tokamak Energy’s ST40 are bringing this vision closer, using advanced diagnostics, superconducting magnets, and increasingly stable plasma control. 👉

Erika 

162,313 次观看 • 7 个月前

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!
0:47

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,326 次观看 • 2 年前

Last night, Washoe County Sheriff’s Office DFR (Drone as First Responder) pilots used RapidSOS live 911 monitoring to launch one of our unmanned aerial vehicles, commonly known as a drone, in response to a report of a suspicious person in the backyard of a residence in Sparks. The drone was airborne and on scene within three minutes of the call being broadcast to patrol deputies. Once overhead, DFR pilots began canvassing the area and relaying real-time information to responding deputies. Using thermal imaging in low-light conditions, the drone successfully located the suspect in the backyard of a nearby residence on Cromwell Place. Pilots continued to provide live updates on the suspect’s location and behavior, allowing deputies to safely detain the individual without incident. Further investigation revealed the suspect, identified as Ryan Davis, 38, had forcibly entered a residence and moved through multiple neighboring backyards prior to being located. Davis was arrested and transported to the Washoe County Detention Facility, where he was booked on a felony charge of home invasion and an additional charge of trespassing. This incident highlights the outstanding coordination between patrol deputies and our DFR pilots, demonstrating how advanced aerial technology is not used for routine surveillance or to monitor lawful activity. DFRs are deployed for specific, time-limited public safety purposes—their use allows deputies to gain real-time situational awareness before making contact, often reducing the need for force and lowering risk to both the public and responding officers. This technology helps deputies make informed decisions, slow situations down, and choose safer resolutions whenever possible. At its core, this program is about saving time, saving lives, and improving outcomes—for victims, for our community members, and for our deputies. By using technology responsibly and transparently, we can respond more effectively to emergencies. #WCSO #DFR #Pilots #DroneasFirstResponder #PublicSafety #CommunitySafety #ProudSheriff
0:50

Last night, Washoe County Sheriff’s Office DFR (Drone as First Responder) pilots used RapidSOS live 911 monitoring to launch one of our unmanned aerial vehicles, commonly known as a drone, in response to a report of a suspicious person in the backyard of a residence in Sparks. The drone was airborne and on scene within three minutes of the call being broadcast to patrol deputies. Once overhead, DFR pilots began canvassing the area and relaying real-time information to responding deputies. Using thermal imaging in low-light conditions, the drone successfully located the suspect in the backyard of a nearby residence on Cromwell Place. Pilots continued to provide live updates on the suspect’s location and behavior, allowing deputies to safely detain the individual without incident. Further investigation revealed the suspect, identified as Ryan Davis, 38, had forcibly entered a residence and moved through multiple neighboring backyards prior to being located. Davis was arrested and transported to the Washoe County Detention Facility, where he was booked on a felony charge of home invasion and an additional charge of trespassing. This incident highlights the outstanding coordination between patrol deputies and our DFR pilots, demonstrating how advanced aerial technology is not used for routine surveillance or to monitor lawful activity. DFRs are deployed for specific, time-limited public safety purposes—their use allows deputies to gain real-time situational awareness before making contact, often reducing the need for force and lowering risk to both the public and responding officers. This technology helps deputies make informed decisions, slow situations down, and choose safer resolutions whenever possible. At its core, this program is about saving time, saving lives, and improving outcomes—for victims, for our community members, and for our deputies. By using technology responsibly and transparently, we can respond more effectively to emergencies. #WCSO #DFR #Pilots #DroneasFirstResponder #PublicSafety #CommunitySafety #ProudSheriff

Washoe Sheriff

11,171 次观看 • 5 个月前

Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:
3:06

Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:

Sword Health

27,974 次观看 • 4 个月前

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.
0:52

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.

Kenny Workman

113,534 次观看 • 1 年前