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Simplify data collection with low cost,hand-held parallel jaw gripper #Pika AgileX Robotics Pika Gripper: ±1.5mm accuracy, dual-camera system Pika Sense: Lightweight (550g) Support ROS1/2 Perfect for researchers and developers driving innovation. #Robotics #EmbodiedAI #ROS

AgileX Robotics

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17,709 görüntüleme • 1 yıl önce •via X (Twitter)

Bilim & Teknoloji #Pika #Robotics #EmbodiedAI #ROS

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VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

RoboHub🤖

12,961 görüntüleme • 9 ay önce

🚀After Years of building battle-tested autonomy for drones, we’re excited to make JETCORE-FC publicly available ! Our flagship compute module along with Flight control carrier board designed for real-time perception, autonomy, and edge AI workloads. Built around NVIDIA Jetson, JETCORE-FC powers our own drones that fly without GPS, without radio, and without compromise. Now, we’re bringing the same power to the hands of developers, researchers, and system integrators. 🔧 Plug-and-play with: High-speed interfaces for camera, LIDAR, and IMU Ruggedized I/O and low-latency networking Full support for ROS, PX4, and custom Linux pipelines Whether you’re building next-gen robotics, defense-grade UGVs, or intelligent drones, JETCORE-FC gives you the reliability and performance we trust in the field. 🛒 Get yours now → #Vecros #JETCORE #EdgeAI #NVIDIAJetson #Robotics #Autonomy #Drones #AI #GPSTech #DroneComputing #SpatialAI NVIDIA NVIDIA Robotics Rashree Besta prem sai RobuIN

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VECROS

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Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

21,046 görüntüleme • 7 ay önce

📣Introducing the portable #exoskeleton controller #myControllerS570! 🤖Perfect for motion data collection and remote #teleoperation, it’s a flexible and cost-effective solution for #EmbodiedIntelligence technology. Key Features: ✅ 2-inch display for real-time feedback. ✅ Connect via USB, Wireless, or Bluetooth. ✅ Compatible with #Python and #ROS. ✅ 12 DOF for precise human arm motion simulation. Applications: ✅ #Research and Development. ✅ #Education and Training. 🎓 ✅ #Robot Remote Teleoperation. 📡 ✨ Special Offer: The first 20 controllers will get a 30% discount! 🚀Get yours👉 #AI #Robotics #TechNews #Christmas

📣Introducing the portable #exoskeleton controller #myControllerS570! 🤖Perfect for motion data collection and remote #teleoperation, it’s a flexible and cost-effective solution for #EmbodiedIntelligence technology. Key Features: ✅ 2-inch display for real-time feedback. ✅ Connect via USB, Wireless, or Bluetooth. ✅ Compatible with #Python and #ROS. ✅ 12 DOF for precise human arm motion simulation. Applications: ✅ #Research and Development. ✅ #Education and Training. 🎓 ✅ #Robot Remote Teleoperation. 📡 ✨ Special Offer: The first 20 controllers will get a 30% discount! 🚀Get yours👉 #AI #Robotics #TechNews #Christmas

Elephant Robotics - Robotic Arms

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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 görüntüleme • 6 ay önce

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal

Adithya Murali

24,106 görüntüleme • 1 yıl önce

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 görüntüleme • 1 yıl önce

Once we started to work with large global retailers, we needed a better way to scale this process. Ideally, the staff at the store could do this themselves — rather than us flying our team across the world — and then we could lower the cost and timelines. So we built a self-serve version of our survey app, with a tutorial mode designed for beginners. Over time, we collected millions of data points, and so we were able to develop an algorithm which would auto-correct mistakes. In other words, if the surveyor accidentally placed their ground-truth location in the wrong place on the map, we could use our algorithms to detect it, and correct it. So now we have WiFi, and with and our efforts on producing a high quality survey, we have the best WiFi positioning available. With WiFi on its own, it’s achieving 3 meter accuracy. This is a great foundation to build on. WiFi + Motion data To refine this down to 1-meter accuracy, we realised that we could combine WiFi with the same technology behind self-driving cars and robotics: a motion system called SLAM (Simultaneous Localization and Mapping). SLAM uses the accelerometer, gyroscope and camera system to understand precise device motion. Imagine a car driving through a tunnel, using the motion since its last GPS ping to keep location accurate until it comes out the other side. On a phone, this technology is very reliable, and measures device motion with high precision. But SLAM is measuring motion within its own coordinate space, it’s not aligned with the real world. SLAM tracks the user’s relative motion, like “moved forward 2 meters, then turned left”, but does “forward” mean “north”, or some other direction? It’s not calibrated, so it could mean any location, any direction. We can’t rely on the compass to help us out with this, because phone compasses are notoriously incorrect — everyone knows the frustration of being sent the wrong way down a street. So our job was to align this motion data with the triangulation data we were receiving from WiFi. We designed an algorithm that could simulate every possibility, filter the unlikely scenarios, and hone in your location, using WiFi as an anchor. So WiFi gives us the initial blue dot, SLAM gives us motion, and as the user starts walking and we receive more data, our algorithms can refine location accuracy down to a consistent 1-meter accuracy. We’ve tested these algorithms in many locations, on hundreds of hours of ground-truth data:

Once we started to work with large global retailers, we needed a better way to scale this process. Ideally, the staff at the store could do this themselves — rather than us flying our team across the world — and then we could lower the cost and timelines. So we built a self-serve version of our survey app, with a tutorial mode designed for beginners. Over time, we collected millions of data points, and so we were able to develop an algorithm which would auto-correct mistakes. In other words, if the surveyor accidentally placed their ground-truth location in the wrong place on the map, we could use our algorithms to detect it, and correct it. So now we have WiFi, and with and our efforts on producing a high quality survey, we have the best WiFi positioning available. With WiFi on its own, it’s achieving 3 meter accuracy. This is a great foundation to build on. WiFi + Motion data To refine this down to 1-meter accuracy, we realised that we could combine WiFi with the same technology behind self-driving cars and robotics: a motion system called SLAM (Simultaneous Localization and Mapping). SLAM uses the accelerometer, gyroscope and camera system to understand precise device motion. Imagine a car driving through a tunnel, using the motion since its last GPS ping to keep location accurate until it comes out the other side. On a phone, this technology is very reliable, and measures device motion with high precision. But SLAM is measuring motion within its own coordinate space, it’s not aligned with the real world. SLAM tracks the user’s relative motion, like “moved forward 2 meters, then turned left”, but does “forward” mean “north”, or some other direction? It’s not calibrated, so it could mean any location, any direction. We can’t rely on the compass to help us out with this, because phone compasses are notoriously incorrect — everyone knows the frustration of being sent the wrong way down a street. So our job was to align this motion data with the triangulation data we were receiving from WiFi. We designed an algorithm that could simulate every possibility, filter the unlikely scenarios, and hone in your location, using WiFi as an anchor. So WiFi gives us the initial blue dot, SLAM gives us motion, and as the user starts walking and we receive more data, our algorithms can refine location accuracy down to a consistent 1-meter accuracy. We’ve tested these algorithms in many locations, on hundreds of hours of ground-truth data:

Andrew Hart

90,946 görüntüleme • 11 ay önce

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 görüntüleme • 9 ay önce

CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?

CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?

Shruti

18,622 görüntüleme • 3 ay önce

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 görüntüleme • 1 yıl önce

Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇

Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇

Alex Lieberman

81,801 görüntüleme • 6 ay önce

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

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$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

101,296 görüntüleme • 7 ay önce

the Andrej Karpathy code: - be Team Human - choose things that scale - scale them to all of humanity things = { Tesla camera vision | humanoid robots | transformers | AI education Eureka Labs} Favorite pull quotes from Andrej Karpathy this morning: - Elon Musk was right on self driving: - "Waymo looks like it's winning right now but I think when we look in 10 years and who's actually at scale and where most of the revenue is coming from I still think [Tesla is] ahead" - "Tesla has a software problem, Waymo has a hardware problem"... "a waymo car has a lot of very expensive LIDAR and other sort of sensors built into the car so it can do what it does...[but] if you can just use cameras which is the Tesla approach then you effectively get rid of enormous cost complexity and you can do it in in many different types of cars". - on Optimus: - "cars are robots" - Tesla isn't a car company, it "is a robotics at scale company" - "early versions of Optimus thought it was a car" - same computer, same cameras, it was walking but thought it was driving - first applications will be in factories where it doesnt "crush grandma" - excited for Optimus to solve the Nat Friedman challenge of the quiet leaf blower robot - on transformers: - "Transformers are this beautiful like blob of tissue you can just get just arbitrary tasks and you just need the data you need to put it in the right form" - "the scaling laws are actually to a large extent a property of the Transformer. Before the Transformer, people were playing with LSTMs and stacking them, you don't actually get clean scaling laws... the Transformer was the first thing that actually just scales. - Architecture is no longer a bottleneck, its now just dataset and objectives - Internet data is "not what you want for your transformer, it's just a nearest neighbor that gets you really far"... "what you want is the inner thought monologue of your brain.. if we had a billion trajectories [of your brain as you're doing problem solving] then AGI is here". "the Internet is like 0.001% cognition and 99.99% of information and most of it is not useful for thinking" - Synthetic data is largely about "refactoring the dataset into these inner monologue formats". Cites the Tencent 1 billion persona paper - Transformers > Humans: much better at learning/memory "if you give it a sequence and you do a single forward-backward pass in that sequence then if you give it the first few elements it will complete the rest of the sequence... it memorized that sequence!" Human brain working memory is very small, transformers "are much more efficient learners". - Most LLMs memorize useless information - a "cognitive core" LLM OS could be as smol as 1b params - just needs to think, and then use tools to look stuff up - Bullish on an AI CEO supervising a swarm (or crew?) of smaller specialist agents - on Education - LLM101n will be "an undergrad level course" coming "early next year" - "I'm always more interested in anything that empowers people... I'm on Team Human" - cites Bloom's Two Sigma problem: "I find very interesting is like how far can a person go if they have the perfect tutor for all the subjects" - people with 1:1 tutoring get 2 stdev better results - "I taught 231n at Stanford and that was the first deep learning class and was pretty successful but the question is how do you really scale these classes — like, how do you make it so that your target audience is maybe 8 billion people on Earth" - for different languages and different capability levels - languages and transfer learning (from previously known domains) are low hanging fruit - "the demo is near but the product is far" - "so the question is how do you use AI to do the scaling of a really good teacher and so the way I'm thinking about it is the teacher is doing a lot of the course creation and the curriculum" - "at current AI capability the models are not good enough to create a good course but I think they're good to become the front end to the student and interpret the course to them" - Learning is supposed to be hard.. but he will "make it easier for people to learn" and in a post AGI society, learning can be entertainment if people want - Kids today should study Math, Physics, CS - "symbol manipulation heavy tasks, not memory heavy"

the Andrej Karpathy code: - be Team Human - choose things that scale - scale them to all of humanity things = { Tesla camera vision | humanoid robots | transformers | AI education Eureka Labs} Favorite pull quotes from Andrej Karpathy this morning: - Elon Musk was right on self driving: - "Waymo looks like it's winning right now but I think when we look in 10 years and who's actually at scale and where most of the revenue is coming from I still think [Tesla is] ahead" - "Tesla has a software problem, Waymo has a hardware problem"... "a waymo car has a lot of very expensive LIDAR and other sort of sensors built into the car so it can do what it does...[but] if you can just use cameras which is the Tesla approach then you effectively get rid of enormous cost complexity and you can do it in in many different types of cars". - on Optimus: - "cars are robots" - Tesla isn't a car company, it "is a robotics at scale company" - "early versions of Optimus thought it was a car" - same computer, same cameras, it was walking but thought it was driving - first applications will be in factories where it doesnt "crush grandma" - excited for Optimus to solve the Nat Friedman challenge of the quiet leaf blower robot - on transformers: - "Transformers are this beautiful like blob of tissue you can just get just arbitrary tasks and you just need the data you need to put it in the right form" - "the scaling laws are actually to a large extent a property of the Transformer. Before the Transformer, people were playing with LSTMs and stacking them, you don't actually get clean scaling laws... the Transformer was the first thing that actually just scales. - Architecture is no longer a bottleneck, its now just dataset and objectives - Internet data is "not what you want for your transformer, it's just a nearest neighbor that gets you really far"... "what you want is the inner thought monologue of your brain.. if we had a billion trajectories [of your brain as you're doing problem solving] then AGI is here". "the Internet is like 0.001% cognition and 99.99% of information and most of it is not useful for thinking" - Synthetic data is largely about "refactoring the dataset into these inner monologue formats". Cites the Tencent 1 billion persona paper - Transformers > Humans: much better at learning/memory "if you give it a sequence and you do a single forward-backward pass in that sequence then if you give it the first few elements it will complete the rest of the sequence... it memorized that sequence!" Human brain working memory is very small, transformers "are much more efficient learners". - Most LLMs memorize useless information - a "cognitive core" LLM OS could be as smol as 1b params - just needs to think, and then use tools to look stuff up - Bullish on an AI CEO supervising a swarm (or crew?) of smaller specialist agents - on Education - LLM101n will be "an undergrad level course" coming "early next year" - "I'm always more interested in anything that empowers people... I'm on Team Human" - cites Bloom's Two Sigma problem: "I find very interesting is like how far can a person go if they have the perfect tutor for all the subjects" - people with 1:1 tutoring get 2 stdev better results - "I taught 231n at Stanford and that was the first deep learning class and was pretty successful but the question is how do you really scale these classes — like, how do you make it so that your target audience is maybe 8 billion people on Earth" - for different languages and different capability levels - languages and transfer learning (from previously known domains) are low hanging fruit - "the demo is near but the product is far" - "so the question is how do you use AI to do the scaling of a really good teacher and so the way I'm thinking about it is the teacher is doing a lot of the course creation and the curriculum" - "at current AI capability the models are not good enough to create a good course but I think they're good to become the front end to the student and interpret the course to them" - Learning is supposed to be hard.. but he will "make it easier for people to learn" and in a post AGI society, learning can be entertainment if people want - Kids today should study Math, Physics, CS - "symbol manipulation heavy tasks, not memory heavy"

swyx

53,917 görüntüleme • 1 yıl önce

TOPIC #106: What Is a “Free Market”? Clarifying the Misconceptions in the Pi Ecosystem I’ve noticed a narrative spreading within parts of the Pi Network community: the idea that Pi’s value in Dapps or ecosystem should fluctuate freely with the exchange market, and that this is what defines a “free market.” They use this "free market" to deny GCV. Let me be clear: this misconception is not only misleading, but it threatens the foundation of the Pi ecosystem we’ve worked so hard to build. It’s time to clarify the truth, not only for our pioneers today but for the economic legacy we’re building for generations to come. What Is a Free Market Really? According to Britannica, a free market is an economic system characterized by minimal government intervention, where prices are determined by the interplay of supply and demand. But even Britannica admits: > “The free market represents a benchmark that does not actually exist… Modern societies only approach this ideal along a spectrum.” — value in relation to In short, a 100% free market is a myth. Every successful economy has rules and frameworks to maintain stability. Without these, markets descend into chaos, not freedom. In Pi Network, “free market” cannot mean price anarchy. And “decentralization” does not mean “do whatever you want.” Let’s break this down: What Pi Network Decentralization Actually Means Pi Network’s decentralization is built on the Stellar Consensus Protocol (SCP) and reflects a healthy distribution of power and particip,ation — not a lack of structure. Key principles of Pi's decentralization: No Single Point of Control No central entity dominates the network. User Participation Pioneers validate transactions and contribute to governance. Resilience The network can survive attacks or failures due to its distributed nature. Censorship Resistance It’s harder for one party to silence or manipulate the system. None of this means that Pi's value can operate in a free market. Any currency must have a fixed value; this is a fundamental concept in economics. Have you ever seen the values of currencies like the USD, CAD, or RMB fluctuate freely based on individual opinions? On the contrary, a fixed value emphasizes the need to protect the economy we are building together. The community-driven GCV illustrates that the value of Pi should derive from its pioneers and merchants, demonstrating the spirit of decentralization. It should not depend on PCT, any government, large corporations, or investors. Furthermore, this structure ensures that no entity can shut down the Pi Network once it becomes fully decentralized, which I believe will occur when it is fully operational and mature. The Danger of Currency Risk: Why Price or Value Chaos Is Destructive In global finance, currency risk refers to the potential loss of value resulting from unstable exchange rates. As the Corporate Finance Institute explains: > “Currency risk refers to the exposure faced by investors or companies operating across different countries due to changes in the value of one currency versus another.” Let’s apply this to Pi. Imagine a Pi Network Dapp marketplace mall merchant collecting a large amount of 10,000 Pi after the Open Mainnet (OM). Customers pay with Pi, but at a value $1. The merchants must know the Pi value because they need to calculate the FIAT cost. Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant total Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant has a total of 10,000 Pi, which is only $1,000, but the cost of investing in products is $9,000 (Sales $10,000 with $1,000 as profit). That’s a massive loss for the merchant $8,000. If you were the merchant, would you feel it was unfair? Will you still support "free market"? Now, imagine the exchange market drops Pi to $0.40. You will lose $5,000. Would you still want to run your business in Pi? Likely not. And neither would other developers or merchants. Unstable value leads to fear. Fear leads to exit. Exit leads to collapse. This is why we must support Global Consensus Value (GCV) — to ensure a unified, trusted economy. Why GCV Exists — and Why $314,159 Matters GCV is not a fantasy. It’s an economic strategy. It functions much like the gold standard once did: England pioneered it. The U.S. adopted it under the Bretton Woods system, fixing the dollar to gold at $35/oz. This standard enabled global trade and trust until 1971. If the free market can work, why did the US adopt the Bretton Woods system at that time to fix the USD's rate with gold? Because if they didn't promise a fixed rate, no country would give its gold to the US. The gold is trust! Here in Pi Network, GCV is a trust! Pi’s GCV of $314,159 per Pi is not random. It’s based on utility, scarcity, and long-term vision. It reflects Pi’s potential as a foundational currency for a real digital economy. Misusing “Free Market” Is Cheating to Ignorant Pioneers Let’s be blunt. Some individuals abuse the term “free market” to justify undervaluing Pi for personal short-term gain, hoarding more Pi, and undermining long-term stability. However, a true economy isn’t built on confusion. Consider the Cayman Islands — a country with no income tax — yet it only accepts USD for settlement. Why? Because multiple currencies lead to confusion, which undermines investor trust. If Pi has no unified value, we will lose merchants, DApps, developers, and the entire vision, except that they just come to hoard Pi, not for the long-term economy, or they really don't understand the economy. The Way Forward: Unity, Strategy, and Patience Here’s how we build the future together for the following strategies before fully OM Strategy #1: Offline Partial GCV Adoption -Fix Pi Value at GCV in Ecosystem for OM GCV Ambassadors around the world are guiding merchants to accept partial GCV, benefiting both sides: Pioneers buy low-cost goods. Merchants enjoy more sales and earn a small profit in FIAT. The ecosystem produces GCV transaction data, creating the real basis for Pi’s future fixed value at OM. Strategy # 2: Online DApps with Utility — at Any Value to Increase Exchange Pi price for OM We support ALL DApps — regardless of the Pi value they use ($1, $100, or floating): As long as the pioneers and merchants are satisfied. As long as real usage is created. As long as the utility grows. As long as more good-quality Dapps are created It will protect and attract more merchants and developers, driving up Pi demand while reducing supply and organically pushing Pi’s market price toward GCV. Strategy #3: Build up GCV Infrastructure The Head of GCV Ambassador builds up your countrywide GCV infrastructure in all provinces, cities, counties, and villages. Strategy #4: Education and Protection of Pi Network Mission and GCV GCV Education Ambassadors: Educate pioneers to HOLD Pi and support GCV usage. GCV Army: Defend GCV and Pi Network on social media, building public trust and global participation. Online Non-GCV pioneers and merchants, or DApp owners, can still enjoy DApps, even if they use low Pi values. They are reducing selling pressure and strengthening the Pi economy. It is said that a person's wealth is closely linked to their knowledge, cognitive abilities, and moral character. We respect and appreciate all DApp owners, merchants, service providers, and pioneers, regardless of whether they share our beliefs in GCV. We are currently in a chaotic period. Before fully transitioning to OM, pioneers, merchants, and DApps will undergo a screening process based on their own judgment and understanding. Those who strongly believe in GCV will become champions and accumulate substantial wealth. Conversely, those who do not believe in GCV may risk losing their wealth by abandoning Pi. This is because if you have a strong belief, you are more likely to hold onto your Pi. If you oppose GCV, it is often due to a lack of long-term confidence in Pi or a current need to accumulate more Pi. It's important to recognize that once you have accumulated enough Pi, you will want to support GCV because no one wishes to hold onto a worthless coin. This approach is fair to everyone. GCV is akin to Noah's Ark, carrying those who have a strong belief in GCV to safety on the mountains of Ararat. A fixed GCV: Attracts real investors Encourages developers and merchants Reduces currency risk Builds global trust and reputation Let’s stop spreading confusion. Let’s stop begging the old system. We are builders. We are visionaries. We are the future. Final Words Together, we build — not beg. Together, we lead, not mislead. Together, we protect Pi for a future that lasts not for years, but centuries. Doris Yin 🪷🪷🪷 July 20th, 2025

TOPIC #106: What Is a “Free Market”? Clarifying the Misconceptions in the Pi Ecosystem I’ve noticed a narrative spreading within parts of the Pi Network community: the idea that Pi’s value in Dapps or ecosystem should fluctuate freely with the exchange market, and that this is what defines a “free market.” They use this "free market" to deny GCV. Let me be clear: this misconception is not only misleading, but it threatens the foundation of the Pi ecosystem we’ve worked so hard to build. It’s time to clarify the truth, not only for our pioneers today but for the economic legacy we’re building for generations to come. What Is a Free Market Really? According to Britannica, a free market is an economic system characterized by minimal government intervention, where prices are determined by the interplay of supply and demand. But even Britannica admits: > “The free market represents a benchmark that does not actually exist… Modern societies only approach this ideal along a spectrum.” — value in relation to In short, a 100% free market is a myth. Every successful economy has rules and frameworks to maintain stability. Without these, markets descend into chaos, not freedom. In Pi Network, “free market” cannot mean price anarchy. And “decentralization” does not mean “do whatever you want.” Let’s break this down: What Pi Network Decentralization Actually Means Pi Network’s decentralization is built on the Stellar Consensus Protocol (SCP) and reflects a healthy distribution of power and particip,ation — not a lack of structure. Key principles of Pi's decentralization: No Single Point of Control No central entity dominates the network. User Participation Pioneers validate transactions and contribute to governance. Resilience The network can survive attacks or failures due to its distributed nature. Censorship Resistance It’s harder for one party to silence or manipulate the system. None of this means that Pi's value can operate in a free market. Any currency must have a fixed value; this is a fundamental concept in economics. Have you ever seen the values of currencies like the USD, CAD, or RMB fluctuate freely based on individual opinions? On the contrary, a fixed value emphasizes the need to protect the economy we are building together. The community-driven GCV illustrates that the value of Pi should derive from its pioneers and merchants, demonstrating the spirit of decentralization. It should not depend on PCT, any government, large corporations, or investors. Furthermore, this structure ensures that no entity can shut down the Pi Network once it becomes fully decentralized, which I believe will occur when it is fully operational and mature. The Danger of Currency Risk: Why Price or Value Chaos Is Destructive In global finance, currency risk refers to the potential loss of value resulting from unstable exchange rates. As the Corporate Finance Institute explains: > “Currency risk refers to the exposure faced by investors or companies operating across different countries due to changes in the value of one currency versus another.” Let’s apply this to Pi. Imagine a Pi Network Dapp marketplace mall merchant collecting a large amount of 10,000 Pi after the Open Mainnet (OM). Customers pay with Pi, but at a value $1. The merchants must know the Pi value because they need to calculate the FIAT cost. Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant total Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant has a total of 10,000 Pi, which is only $1,000, but the cost of investing in products is $9,000 (Sales $10,000 with $1,000 as profit). That’s a massive loss for the merchant $8,000. If you were the merchant, would you feel it was unfair? Will you still support "free market"? Now, imagine the exchange market drops Pi to $0.40. You will lose $5,000. Would you still want to run your business in Pi? Likely not. And neither would other developers or merchants. Unstable value leads to fear. Fear leads to exit. Exit leads to collapse. This is why we must support Global Consensus Value (GCV) — to ensure a unified, trusted economy. Why GCV Exists — and Why $314,159 Matters GCV is not a fantasy. It’s an economic strategy. It functions much like the gold standard once did: England pioneered it. The U.S. adopted it under the Bretton Woods system, fixing the dollar to gold at $35/oz. This standard enabled global trade and trust until 1971. If the free market can work, why did the US adopt the Bretton Woods system at that time to fix the USD's rate with gold? Because if they didn't promise a fixed rate, no country would give its gold to the US. The gold is trust! Here in Pi Network, GCV is a trust! Pi’s GCV of $314,159 per Pi is not random. It’s based on utility, scarcity, and long-term vision. It reflects Pi’s potential as a foundational currency for a real digital economy. Misusing “Free Market” Is Cheating to Ignorant Pioneers Let’s be blunt. Some individuals abuse the term “free market” to justify undervaluing Pi for personal short-term gain, hoarding more Pi, and undermining long-term stability. However, a true economy isn’t built on confusion. Consider the Cayman Islands — a country with no income tax — yet it only accepts USD for settlement. Why? Because multiple currencies lead to confusion, which undermines investor trust. If Pi has no unified value, we will lose merchants, DApps, developers, and the entire vision, except that they just come to hoard Pi, not for the long-term economy, or they really don't understand the economy. The Way Forward: Unity, Strategy, and Patience Here’s how we build the future together for the following strategies before fully OM Strategy #1: Offline Partial GCV Adoption -Fix Pi Value at GCV in Ecosystem for OM GCV Ambassadors around the world are guiding merchants to accept partial GCV, benefiting both sides: Pioneers buy low-cost goods. Merchants enjoy more sales and earn a small profit in FIAT. The ecosystem produces GCV transaction data, creating the real basis for Pi’s future fixed value at OM. Strategy # 2: Online DApps with Utility — at Any Value to Increase Exchange Pi price for OM We support ALL DApps — regardless of the Pi value they use ($1, $100, or floating): As long as the pioneers and merchants are satisfied. As long as real usage is created. As long as the utility grows. As long as more good-quality Dapps are created It will protect and attract more merchants and developers, driving up Pi demand while reducing supply and organically pushing Pi’s market price toward GCV. Strategy #3: Build up GCV Infrastructure The Head of GCV Ambassador builds up your countrywide GCV infrastructure in all provinces, cities, counties, and villages. Strategy #4: Education and Protection of Pi Network Mission and GCV GCV Education Ambassadors: Educate pioneers to HOLD Pi and support GCV usage. GCV Army: Defend GCV and Pi Network on social media, building public trust and global participation. Online Non-GCV pioneers and merchants, or DApp owners, can still enjoy DApps, even if they use low Pi values. They are reducing selling pressure and strengthening the Pi economy. It is said that a person's wealth is closely linked to their knowledge, cognitive abilities, and moral character. We respect and appreciate all DApp owners, merchants, service providers, and pioneers, regardless of whether they share our beliefs in GCV. We are currently in a chaotic period. Before fully transitioning to OM, pioneers, merchants, and DApps will undergo a screening process based on their own judgment and understanding. Those who strongly believe in GCV will become champions and accumulate substantial wealth. Conversely, those who do not believe in GCV may risk losing their wealth by abandoning Pi. This is because if you have a strong belief, you are more likely to hold onto your Pi. If you oppose GCV, it is often due to a lack of long-term confidence in Pi or a current need to accumulate more Pi. It's important to recognize that once you have accumulated enough Pi, you will want to support GCV because no one wishes to hold onto a worthless coin. This approach is fair to everyone. GCV is akin to Noah's Ark, carrying those who have a strong belief in GCV to safety on the mountains of Ararat. A fixed GCV: Attracts real investors Encourages developers and merchants Reduces currency risk Builds global trust and reputation Let’s stop spreading confusion. Let’s stop begging the old system. We are builders. We are visionaries. We are the future. Final Words Together, we build — not beg. Together, we lead, not mislead. Together, we protect Pi for a future that lasts not for years, but centuries. Doris Yin 🪷🪷🪷 July 20th, 2025

Doris Yin 东方紫莲🪷

30,299 görüntüleme • 1 yıl önce

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on mainstream cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

266,920 görüntüleme • 2 ay önce

🚨 BREAKING: Italian radar scientist detected what appears to be a massive grid of eight cylindrical structures, each 20 meters in diameter, descending over a kilometer beneath the Giza pyramids using Synthetic Aperture Radar Doppler Tomography. The cylindrical columns have coils wrapping around them resulting in a megastructure that looks like an ancient energy grid 🚨 So I brought in Geoffrey Drumm, one of the most technically rigorous pyramid researchers alive, to stress test every claim in real time. What followed was a four hour technical interrogation that revealed both stunning validations and unresolved questions about what may be the most significant archaeological discovery of the century. Biondi holds a PhD in radar science, 30 years in the field, and invented a proprietary method called the Biondi Protocol that reads surface micro-vibrations detected by Italian COSMO-SkyMed satellites to reconstruct what lies inside and beneath solid structures. His first peer-reviewed paper scanned the Great Pyramid in 2020. His second project scanned the Khafre Pyramid and the wider Giza Plateau, producing the 3D model that broke the internet: eight tubular columns with coils wrapping around them, sitting on a foundation of enormous cube-shaped structures, extending beneath all three pyramids and the Sphinx. Drumm is the author of The Land of Chem YouTube channel, lives in Egypt, and has developed a comprehensive hypothesis that the pyramids functioned as industrial-scale chemical reactors powered by lightning during the Saharan Humid Period. He knows the Giza Plateau like the back of his hand and has previously stress tested and poked holes in Biondi’s findings. This conversation is an unfiltered exchange between two heavyweights: 1. Biondi's Best Scan Is Jaw-Dropping As validation, Biondi presented a proof-of-concept scan of Italy's Gran Sasso National Laboratory, buried 1.4 kilometers inside a mountain. The image is stunning. You can see the tunnel cutting through the mountain, the interior of the facility, and even the interferometer inside it using the same technique Biondi used to scan beneath the pyramids. Drumm called it the single most convincing piece of evidence that this technology works. The Gotthard Tunnel in Switzerland produced a similarly clear image at two kilometers depth through solid rock. These are not theoretical demonstrations. They are working scans of known structures at extreme depth, and they validate that the Biondi Protocol can see through kilometers of stone. 2. He Found a Hidden Corridor Before Anyone Else In his 2020 paper, Biondi identified a feature on the northern face of the Great Pyramid labeled Tag 17. A dead-end corridor behind the chevron stones that nobody knew existed. Years later, the ScanPyramids muon team confirmed it and drilled in with a microscopic camera. Biondi's measurements of the corridor's length and the positions of its floor and ceiling matched what was found. This is a confirmed prediction from satellite radar, made years before physical verification. 3. He Detected a Sealed Shaft Beneath the Queen's Chamber One of the most compelling findings from the 2020 paper is a shaft and chamber system descending from the bottom of the Queen's Chamber. This structure was actually reported in 19th century excavation documents. Explorers found a pit in the Queen's Chamber floor, excavated down, and discovered a tunnel system below it. The Egyptian authorities then permanently sealed it with modern blocks. Biondi's scans picked it up independently, with no prior knowledge of those historical records. Drumm, who had already proposed this exact extraction shaft in his own chemical reactor model, called this the most promising result in the entire dataset. 4. The Substructures Are Enormous The tubular columns beneath the Khafre Pyramid measure approximately 20 meters in diameter each, spaced about 5 meters apart. That is 65 feet across per column. Eight of them. For context, the Queen's Chamber sometimes fails to register in certain scan slices because it is too small relative to the tomographic line. Biondi's argument is that megastructures at this scale are exactly what the technology is built to detect. Small chambers can be missed depending on the angle of the satellite pass. Repeating cylindrical structures 20 meters wide, appearing consistently across multiple scan geometries and multiple satellite sensors, are a different category of detection entirely. 5. Drumm's Challenge: The Processing Gap Here is where the debate gets sharp. The Gran Sasso and Gotthard scans used an advanced processing technique that averages noise across adjacent tomographic slices, requiring months of computation on borrowed hardware. The pyramid scans used a faster but noisier method on Biondi's own limited computers. Drumm pointed out that the quality difference is massive. The proof-of-concept images are transparent like a crystal. The pyramid images require expert interpretation to read. Biondi's response: he needs an array of GPUs he cannot afford. With that hardware, he says he could produce Gran Sasso-quality scans of the Giza substructures in near real-time. Estimated cost: millions. This is the bottleneck standing between a controversial claim and a potentially world-changing confirmation. 6. Other issues: Known Chambers Sometimes Do Not Appear Drumm walked through the 2020 dataset scan by scan. The Queen's Chamber shows a strong, consistent signature and serves as a reliable benchmark. But in several tomographic slices, the King's Chamber does not appear. The Grand Gallery does not appear. The subterranean chamber does not appear. Biondi attributes this to single-slice geometry. Each scan captures one vertical curtain through the structure in 15 seconds. If that curtain does not intersect a chamber precisely, it will not register. He says the real-time GPU system would allow him to sweep through hundreds of adjacent slices and reconstruct a full 3D volume. That system does not yet exist. 7. Biondi Challenged the Muon Team's Interpretation The ScanPyramids muon team claims the Big Void inside the Great Pyramid runs north to south, parallel to and above the Grand Gallery. Biondi's scans show it running east to west, connected to structures wrapping around the King's Chamber. Looking at the muon data during the conversation, Biondi argued they may have confused the floor and roof of the Grand Gallery for two separate features. The Egyptian Ministry of Antiquities is using the muon team's interpretation to justify drilling into the Great Pyramid in 2026. If Biondi is right about the orientation, that excavation could validate SAR Doppler tomography over the established method in one stroke. 8. The Signal Fades at 600 Meters and Nobody Knows Why The model shows structures extending over a kilometer deep. But in the raw data, the signal tapers around 600 meters. Drumm pressed Biondi on this. The initial explanation was the water table, but both agreed the actual water table sits only about 50 meters below the plateau. When pushed further, Biondi said he cannot yet explain the change but hinted at something he is not authorized to disclose. The structures do continue in the model below that line, detected across multiple satellite sensors showing the same cutoff pattern. What changes at 600 meters remains an open question. 9. Drumm's Model Says the Substructures Could Make Functional Sense Drumm's hypothesis is that each pyramid produced a specific chemical in sequence, from methane extraction at the Step Pyramid to ammonia synthesis in the Red Pyramid to sulfuric acid production in the Great Pyramid. He places the operational period during the Saharan Humid Period, roughly 8500 to 5300 BC, when massive thunderstorms provided the electrical input. The Big Void sits exactly where a heat exchanger would need to be to manage exothermic reactions in the Grand Gallery. The sealed shaft beneath the Queen's Chamber aligns with his proposed product extraction system. He confirmed that he has already integrated Biondi's substructure findings into a working functional model. If the deep structures are real, they connect to known hydrothermal mineral deposits, iron ore veins, and rare earth elements embedded in the Giza bedrock. Drumm and Biondi both agree: whoever built these structures chose the Giza Plateau for a very specific reason tied to what lies beneath it. 10. Validation & What Comes Next Biondi wants to establish a foundation in Malta with a dedicated data center and GPU array to reprocess the Giza data using his superior technique. Drumm wants to go to the Giza Plateau with Biondi's team to physically investigate anomalies he has already identified near the Osiris Shaft and along the Khafre causeway. Both say the SAR method and the muon method should be combined rather than treated as competitors. Both state that the conventional dating and tomb explanation for the pyramids is wrong. And both Drumm and Biondi agree that what lies beneath the Giza Plateau is more important than what sits on top of it. They also agree on the need for further validation and stress-testing. Why This Matters A satellite technique that can see through 1.4 kilometers of mountain and accurately image the Gran Sasso Laboratory. A confirmed prediction of a hidden corridor inside the Great Pyramid years before physical verification. A detection of a sealed shaft that matches 19th century excavation records. And now, scans showing a repeating grid of massive cylindrical structures beneath the entire Giza Plateau that no conventional archaeological framework can account for. The technology has demonstrated real capability. The substructure claims remain extraordinary. The 2026 Big Void excavation and GPU-powered rescans could settle this within months. If even a fraction of what Biondi is detecting turns out to be real, we are looking at the largest undiscovered structure on Earth, hidden in plain sight beneath the most studied archaeological site in human history. Full conversation covers all of this and much more. One of the most important technical examinations of the pyramid mystery ever recorded. Live now👇

🚨 BREAKING: Italian radar scientist detected what appears to be a massive grid of eight cylindrical structures, each 20 meters in diameter, descending over a kilometer beneath the Giza pyramids using Synthetic Aperture Radar Doppler Tomography. The cylindrical columns have coils wrapping around them resulting in a megastructure that looks like an ancient energy grid 🚨 So I brought in Geoffrey Drumm, one of the most technically rigorous pyramid researchers alive, to stress test every claim in real time. What followed was a four hour technical interrogation that revealed both stunning validations and unresolved questions about what may be the most significant archaeological discovery of the century. Biondi holds a PhD in radar science, 30 years in the field, and invented a proprietary method called the Biondi Protocol that reads surface micro-vibrations detected by Italian COSMO-SkyMed satellites to reconstruct what lies inside and beneath solid structures. His first peer-reviewed paper scanned the Great Pyramid in 2020. His second project scanned the Khafre Pyramid and the wider Giza Plateau, producing the 3D model that broke the internet: eight tubular columns with coils wrapping around them, sitting on a foundation of enormous cube-shaped structures, extending beneath all three pyramids and the Sphinx. Drumm is the author of The Land of Chem YouTube channel, lives in Egypt, and has developed a comprehensive hypothesis that the pyramids functioned as industrial-scale chemical reactors powered by lightning during the Saharan Humid Period. He knows the Giza Plateau like the back of his hand and has previously stress tested and poked holes in Biondi’s findings. This conversation is an unfiltered exchange between two heavyweights: 1. Biondi's Best Scan Is Jaw-Dropping As validation, Biondi presented a proof-of-concept scan of Italy's Gran Sasso National Laboratory, buried 1.4 kilometers inside a mountain. The image is stunning. You can see the tunnel cutting through the mountain, the interior of the facility, and even the interferometer inside it using the same technique Biondi used to scan beneath the pyramids. Drumm called it the single most convincing piece of evidence that this technology works. The Gotthard Tunnel in Switzerland produced a similarly clear image at two kilometers depth through solid rock. These are not theoretical demonstrations. They are working scans of known structures at extreme depth, and they validate that the Biondi Protocol can see through kilometers of stone. 2. He Found a Hidden Corridor Before Anyone Else In his 2020 paper, Biondi identified a feature on the northern face of the Great Pyramid labeled Tag 17. A dead-end corridor behind the chevron stones that nobody knew existed. Years later, the ScanPyramids muon team confirmed it and drilled in with a microscopic camera. Biondi's measurements of the corridor's length and the positions of its floor and ceiling matched what was found. This is a confirmed prediction from satellite radar, made years before physical verification. 3. He Detected a Sealed Shaft Beneath the Queen's Chamber One of the most compelling findings from the 2020 paper is a shaft and chamber system descending from the bottom of the Queen's Chamber. This structure was actually reported in 19th century excavation documents. Explorers found a pit in the Queen's Chamber floor, excavated down, and discovered a tunnel system below it. The Egyptian authorities then permanently sealed it with modern blocks. Biondi's scans picked it up independently, with no prior knowledge of those historical records. Drumm, who had already proposed this exact extraction shaft in his own chemical reactor model, called this the most promising result in the entire dataset. 4. The Substructures Are Enormous The tubular columns beneath the Khafre Pyramid measure approximately 20 meters in diameter each, spaced about 5 meters apart. That is 65 feet across per column. Eight of them. For context, the Queen's Chamber sometimes fails to register in certain scan slices because it is too small relative to the tomographic line. Biondi's argument is that megastructures at this scale are exactly what the technology is built to detect. Small chambers can be missed depending on the angle of the satellite pass. Repeating cylindrical structures 20 meters wide, appearing consistently across multiple scan geometries and multiple satellite sensors, are a different category of detection entirely. 5. Drumm's Challenge: The Processing Gap Here is where the debate gets sharp. The Gran Sasso and Gotthard scans used an advanced processing technique that averages noise across adjacent tomographic slices, requiring months of computation on borrowed hardware. The pyramid scans used a faster but noisier method on Biondi's own limited computers. Drumm pointed out that the quality difference is massive. The proof-of-concept images are transparent like a crystal. The pyramid images require expert interpretation to read. Biondi's response: he needs an array of GPUs he cannot afford. With that hardware, he says he could produce Gran Sasso-quality scans of the Giza substructures in near real-time. Estimated cost: millions. This is the bottleneck standing between a controversial claim and a potentially world-changing confirmation. 6. Other issues: Known Chambers Sometimes Do Not Appear Drumm walked through the 2020 dataset scan by scan. The Queen's Chamber shows a strong, consistent signature and serves as a reliable benchmark. But in several tomographic slices, the King's Chamber does not appear. The Grand Gallery does not appear. The subterranean chamber does not appear. Biondi attributes this to single-slice geometry. Each scan captures one vertical curtain through the structure in 15 seconds. If that curtain does not intersect a chamber precisely, it will not register. He says the real-time GPU system would allow him to sweep through hundreds of adjacent slices and reconstruct a full 3D volume. That system does not yet exist. 7. Biondi Challenged the Muon Team's Interpretation The ScanPyramids muon team claims the Big Void inside the Great Pyramid runs north to south, parallel to and above the Grand Gallery. Biondi's scans show it running east to west, connected to structures wrapping around the King's Chamber. Looking at the muon data during the conversation, Biondi argued they may have confused the floor and roof of the Grand Gallery for two separate features. The Egyptian Ministry of Antiquities is using the muon team's interpretation to justify drilling into the Great Pyramid in 2026. If Biondi is right about the orientation, that excavation could validate SAR Doppler tomography over the established method in one stroke. 8. The Signal Fades at 600 Meters and Nobody Knows Why The model shows structures extending over a kilometer deep. But in the raw data, the signal tapers around 600 meters. Drumm pressed Biondi on this. The initial explanation was the water table, but both agreed the actual water table sits only about 50 meters below the plateau. When pushed further, Biondi said he cannot yet explain the change but hinted at something he is not authorized to disclose. The structures do continue in the model below that line, detected across multiple satellite sensors showing the same cutoff pattern. What changes at 600 meters remains an open question. 9. Drumm's Model Says the Substructures Could Make Functional Sense Drumm's hypothesis is that each pyramid produced a specific chemical in sequence, from methane extraction at the Step Pyramid to ammonia synthesis in the Red Pyramid to sulfuric acid production in the Great Pyramid. He places the operational period during the Saharan Humid Period, roughly 8500 to 5300 BC, when massive thunderstorms provided the electrical input. The Big Void sits exactly where a heat exchanger would need to be to manage exothermic reactions in the Grand Gallery. The sealed shaft beneath the Queen's Chamber aligns with his proposed product extraction system. He confirmed that he has already integrated Biondi's substructure findings into a working functional model. If the deep structures are real, they connect to known hydrothermal mineral deposits, iron ore veins, and rare earth elements embedded in the Giza bedrock. Drumm and Biondi both agree: whoever built these structures chose the Giza Plateau for a very specific reason tied to what lies beneath it. 10. Validation & What Comes Next Biondi wants to establish a foundation in Malta with a dedicated data center and GPU array to reprocess the Giza data using his superior technique. Drumm wants to go to the Giza Plateau with Biondi's team to physically investigate anomalies he has already identified near the Osiris Shaft and along the Khafre causeway. Both say the SAR method and the muon method should be combined rather than treated as competitors. Both state that the conventional dating and tomb explanation for the pyramids is wrong. And both Drumm and Biondi agree that what lies beneath the Giza Plateau is more important than what sits on top of it. They also agree on the need for further validation and stress-testing. Why This Matters A satellite technique that can see through 1.4 kilometers of mountain and accurately image the Gran Sasso Laboratory. A confirmed prediction of a hidden corridor inside the Great Pyramid years before physical verification. A detection of a sealed shaft that matches 19th century excavation records. And now, scans showing a repeating grid of massive cylindrical structures beneath the entire Giza Plateau that no conventional archaeological framework can account for. The technology has demonstrated real capability. The substructure claims remain extraordinary. The 2026 Big Void excavation and GPU-powered rescans could settle this within months. If even a fraction of what Biondi is detecting turns out to be real, we are looking at the largest undiscovered structure on Earth, hidden in plain sight beneath the most studied archaeological site in human history. Full conversation covers all of this and much more. One of the most important technical examinations of the pyramid mystery ever recorded. Live now👇

Jesse Michels

1,071,119 görüntüleme • 4 ay önce

🚨BREAKING🚨: Gary McKinnon, the man behind the most sensitive and largest military hack in U.S. history, sat down for the first time in years and described seeing a cigar-shaped UFO hovering above Earth in a NASA database attached to Johnson Space Center. This corroborates the testimony of former NASA employee Donna Hare who claimed she saw a photograph of a UFO in the exact building McKinnon scanned. McKinnon also discovered a spreadsheet titled "Non-Terrestrial Officers" listing roughly 40 names and ship-to-ship transfers of exotic materials. He was promptly persecuted by American authorities, threatened with extradition along with 70 years in prison and remains on the Interpol Red List to this day. For the first time in decades, Gary reveals what he really thinks NASA’s “secret space fleet” was: a supply chain for highly useful, thinly-layered metamaterials that require a low-gravity space environment for fabrication. He also opens up about being implanted with a “tracking” chip in the middle of the night a few years after the hack. Gary McKinnon hacked into 97 U.S. military and government sites in early 2000 from his girlfriend's aunt's flat in London. NSA at Fort Meade. DISA. Army, Navy, Air Force networks. NASA. All accessed with a Perl script scanning for blank passwords on a 56K dial-up connection while smoking weed in a dressing gown at 4 AM. He was not a professional hacker. He was a guy from Falkirk, Scotland, who grew up near Bonnybridge, one of the UK's most active UFO hotspots, who had read the Disclosure Project book and wanted to know for himself. What he found inside those systems, and what the U.S. government did to him for finding it, is one of the most consequential stories in modern UFO history. 1. Cigar-Shaped UFO Hovering Above Earth Inside Building 8 at Johnson Space Center, McKinnon found a machine with two folders on a bare desktop: "Raw" and "Processed." On his 56K connection the file loaded line by line. First blackness. Then a hemisphere. Blue and white. Earth. Then a straight silvery line. A smooth, cylindrical, cigar-shaped object with no seams, no rivets, no sensors. Far beyond low Earth orbit. Then the mouse moved on its own. Someone at the other end right-clicked the network icon and disconnected him. He never saw the full image. 2. "Non-Terrestrial Officers" Spreadsheet On what he believes was a Navy system, McKinnon found a spreadsheet titled "Non-Terrestrial Officers." One tab listed roughly 30-40 names. Another listed ship names that matched no known U.S. Navy vessel. A third recorded fleet-to-fleet transfers of materials: molybdenum, barium, strontium. He downloaded it. When he was arrested, all his data was seized by the Office of Naval Intelligence. He has tried for years to get his hard drives back. ONI says the investigation is ongoing. 3. Space Supply Chain, Not “Alien Officers” McKinnon never found the secret space program known as Solar Warden. That term came from an anonymous forum post after his case went public. What the spreadsheet describes is logistics. Ship names. Personnel. Material transfers between fleets. Non-terrestrial means not Earth-based. Not necessarily non-human. The simplest read is a classified space manufacturing operation: humans creating exotic metamaterials in zero-gravity that are physically impossible to fabricate on Earth. This interpretation emerged live during the interview. McKinnon said he had never connected it that way before. 4. Materials in Space Made for Anti-Gravity Barium and strontium are high-K dielectrics that store and discharge electric fields efficiently. These are the exact materials in Thomas Townsend Brown's mid-century anti-gravity experiments. Molybdenum is used in advanced alloy strengthening. Commercial efforts to build in space haven’t succeeded historically due to cost of launch – but for materials like these with extreme national security implications – it makes sense for such a program to exist. McKinnon has been researching the Biefield-Brown effect since 2007 and is building his own experiment in a garden shed. The overlap between the spreadsheet and electrogravitics research is not something he recognized at the time. He made the connection for the first time in our interview. 5. They Wanted Him in Guantanamo The UK's crime unit initially said six months, maybe community service. Then those officers visited the Office of Naval Intelligence. When they came back, the tone changed completely. Ed Gibson, U.S. attaché in London, told McKinnon's lawyer: "We want to see him fry." The DOJ said he would be tried under Military Order Number One. Guantanamo status. No media. No family visits. Seven counts, ten years each. Seventy years. 6. He Bought Lethal Injection Chemicals and Considered Taking His Own Life By 2008, after losing multiple court cases, McKinnon gave up. He purchased potassium chloride, one of the three chemicals in lethal injection, and calculated dosage per kilogram of body weight. In 2012, UK Home Secretary Theresa May blocked the extradition, citing unacceptable risk he would end his life. Gordon Brown, David Cameron, and Barack Obama all engaged with the case. McKinnon remains on the Interpol Red List and cannot enter the United States. 7. Donna Hare Said Building 8 Held UFO Photos. McKinnon Found Building 8. Donna Hare, a NASA photographic specialist with secret clearance, testified at the Disclosure Project that a colleague in Building 8 of Johnson Space Center showed her satellite imagery of a large disc that cast a shadow. His job was to airbrush these objects out. McKinnon was already inside JSC's network when he read her testimony. He used Windows auditing commands to isolate Building 8 machines. About a dozen came up. Half had blank passwords. The first one had two folders on a bare desktop: "Raw" and "Processed." The same building. The same kind of imagery. Decades apart. 8. He Got In With Blank Passwords McKinnon wrote a Perl script that scanned hundreds of thousands of military IP addresses in minutes. Five percent responded. Of those, a further five percent had passwords that were blank, "password," or "admin." He used a tool called LanSearch to search every file and folder across up to 5,000 networked PCs at once. The Pentagon's most sensitive networks were protected by nothing. 9. He Reveals He Was “Microchipped” (likely with a tracking device) Gary opens up for the first time about his sleep being interrupted due to a chip implant. He shows us the implant on camera. Two small bumps - incisions - on his foot. He’s done some vigilante investigating and thinks the company that likely made the chip is called Verisign – they built “grain of rice” sized microchips often meant for human implantation. McKinnon’s was likely an RFID tracker to track his whereabouts. Dystopian to say the least! Why This Matters Matthew Bevan hacked into the Department of Energy and atomic labs in the 1990s using more sophisticated techniques. Slap on the wrist. McKinnon used blank passwords and found UFO imagery and a logistics spreadsheet. He faced decades in prison. The severity of the response tells you something about what he found; the existence of a secret space supply chain. The materials on that spreadsheet, barium, strontium, molybdenum, are the same materials in Townsend Brown's anti-gravity research – that is not a coincidence you dismiss easily. McKinnon never intended any harm – he was merely a curious UFO fanatic. He used off-the-shelf available technology. He should be pardoned by Trump (who has explicitly expressed interest in UFO transparency) immediately. #FREEMCKINNON Full conversation covers all of this and much more. Maybe the most mind-blowing and dot-connecting interview we've ever done 👇

🚨BREAKING🚨: Gary McKinnon, the man behind the most sensitive and largest military hack in U.S. history, sat down for the first time in years and described seeing a cigar-shaped UFO hovering above Earth in a NASA database attached to Johnson Space Center. This corroborates the testimony of former NASA employee Donna Hare who claimed she saw a photograph of a UFO in the exact building McKinnon scanned. McKinnon also discovered a spreadsheet titled "Non-Terrestrial Officers" listing roughly 40 names and ship-to-ship transfers of exotic materials. He was promptly persecuted by American authorities, threatened with extradition along with 70 years in prison and remains on the Interpol Red List to this day. For the first time in decades, Gary reveals what he really thinks NASA’s “secret space fleet” was: a supply chain for highly useful, thinly-layered metamaterials that require a low-gravity space environment for fabrication. He also opens up about being implanted with a “tracking” chip in the middle of the night a few years after the hack. Gary McKinnon hacked into 97 U.S. military and government sites in early 2000 from his girlfriend's aunt's flat in London. NSA at Fort Meade. DISA. Army, Navy, Air Force networks. NASA. All accessed with a Perl script scanning for blank passwords on a 56K dial-up connection while smoking weed in a dressing gown at 4 AM. He was not a professional hacker. He was a guy from Falkirk, Scotland, who grew up near Bonnybridge, one of the UK's most active UFO hotspots, who had read the Disclosure Project book and wanted to know for himself. What he found inside those systems, and what the U.S. government did to him for finding it, is one of the most consequential stories in modern UFO history. 1. Cigar-Shaped UFO Hovering Above Earth Inside Building 8 at Johnson Space Center, McKinnon found a machine with two folders on a bare desktop: "Raw" and "Processed." On his 56K connection the file loaded line by line. First blackness. Then a hemisphere. Blue and white. Earth. Then a straight silvery line. A smooth, cylindrical, cigar-shaped object with no seams, no rivets, no sensors. Far beyond low Earth orbit. Then the mouse moved on its own. Someone at the other end right-clicked the network icon and disconnected him. He never saw the full image. 2. "Non-Terrestrial Officers" Spreadsheet On what he believes was a Navy system, McKinnon found a spreadsheet titled "Non-Terrestrial Officers." One tab listed roughly 30-40 names. Another listed ship names that matched no known U.S. Navy vessel. A third recorded fleet-to-fleet transfers of materials: molybdenum, barium, strontium. He downloaded it. When he was arrested, all his data was seized by the Office of Naval Intelligence. He has tried for years to get his hard drives back. ONI says the investigation is ongoing. 3. Space Supply Chain, Not “Alien Officers” McKinnon never found the secret space program known as Solar Warden. That term came from an anonymous forum post after his case went public. What the spreadsheet describes is logistics. Ship names. Personnel. Material transfers between fleets. Non-terrestrial means not Earth-based. Not necessarily non-human. The simplest read is a classified space manufacturing operation: humans creating exotic metamaterials in zero-gravity that are physically impossible to fabricate on Earth. This interpretation emerged live during the interview. McKinnon said he had never connected it that way before. 4. Materials in Space Made for Anti-Gravity Barium and strontium are high-K dielectrics that store and discharge electric fields efficiently. These are the exact materials in Thomas Townsend Brown's mid-century anti-gravity experiments. Molybdenum is used in advanced alloy strengthening. Commercial efforts to build in space haven’t succeeded historically due to cost of launch – but for materials like these with extreme national security implications – it makes sense for such a program to exist. McKinnon has been researching the Biefield-Brown effect since 2007 and is building his own experiment in a garden shed. The overlap between the spreadsheet and electrogravitics research is not something he recognized at the time. He made the connection for the first time in our interview. 5. They Wanted Him in Guantanamo The UK's crime unit initially said six months, maybe community service. Then those officers visited the Office of Naval Intelligence. When they came back, the tone changed completely. Ed Gibson, U.S. attaché in London, told McKinnon's lawyer: "We want to see him fry." The DOJ said he would be tried under Military Order Number One. Guantanamo status. No media. No family visits. Seven counts, ten years each. Seventy years. 6. He Bought Lethal Injection Chemicals and Considered Taking His Own Life By 2008, after losing multiple court cases, McKinnon gave up. He purchased potassium chloride, one of the three chemicals in lethal injection, and calculated dosage per kilogram of body weight. In 2012, UK Home Secretary Theresa May blocked the extradition, citing unacceptable risk he would end his life. Gordon Brown, David Cameron, and Barack Obama all engaged with the case. McKinnon remains on the Interpol Red List and cannot enter the United States. 7. Donna Hare Said Building 8 Held UFO Photos. McKinnon Found Building 8. Donna Hare, a NASA photographic specialist with secret clearance, testified at the Disclosure Project that a colleague in Building 8 of Johnson Space Center showed her satellite imagery of a large disc that cast a shadow. His job was to airbrush these objects out. McKinnon was already inside JSC's network when he read her testimony. He used Windows auditing commands to isolate Building 8 machines. About a dozen came up. Half had blank passwords. The first one had two folders on a bare desktop: "Raw" and "Processed." The same building. The same kind of imagery. Decades apart. 8. He Got In With Blank Passwords McKinnon wrote a Perl script that scanned hundreds of thousands of military IP addresses in minutes. Five percent responded. Of those, a further five percent had passwords that were blank, "password," or "admin." He used a tool called LanSearch to search every file and folder across up to 5,000 networked PCs at once. The Pentagon's most sensitive networks were protected by nothing. 9. He Reveals He Was “Microchipped” (likely with a tracking device) Gary opens up for the first time about his sleep being interrupted due to a chip implant. He shows us the implant on camera. Two small bumps - incisions - on his foot. He’s done some vigilante investigating and thinks the company that likely made the chip is called Verisign – they built “grain of rice” sized microchips often meant for human implantation. McKinnon’s was likely an RFID tracker to track his whereabouts. Dystopian to say the least! Why This Matters Matthew Bevan hacked into the Department of Energy and atomic labs in the 1990s using more sophisticated techniques. Slap on the wrist. McKinnon used blank passwords and found UFO imagery and a logistics spreadsheet. He faced decades in prison. The severity of the response tells you something about what he found; the existence of a secret space supply chain. The materials on that spreadsheet, barium, strontium, molybdenum, are the same materials in Townsend Brown's anti-gravity research – that is not a coincidence you dismiss easily. McKinnon never intended any harm – he was merely a curious UFO fanatic. He used off-the-shelf available technology. He should be pardoned by Trump (who has explicitly expressed interest in UFO transparency) immediately. #FREEMCKINNON Full conversation covers all of this and much more. Maybe the most mind-blowing and dot-connecting interview we've ever done 👇

Jesse Michels

41,491 görüntüleme • 4 ay önce