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400G. 800G. 1.6T. The AI networking stack isn't slowing down. AOI's latest breaks down the optical technologies reshaping data center infrastructure: CPO, XPO, LPO/LRO, OCI fabrics, and 6.4T On-Board Optics. $AAOI

AOI

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46,740 views • 3 months ago •via X (Twitter)

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We believe the future of enterprise computing will bring AI, HPC, and quantum together. That’s why, today, Quantinuum and Oracle have announced a multi-year strategic partnership to bring quantum computing to Oracle Cloud Infrastructure customers and accelerate the commercial adoption of hybrid quantum-AI computing. As part of the collaboration, our Helios quantum computer will be deployed in a U.S.-based Oracle AI data center, alongside OCI’s HPC and GPU infrastructure, and made available through OCI’s quantum service to give OCI customers a practical and secure way to explore how high-fidelity quantum computing can complement existing AI and HPC workloads—using the governance and access controls they already rely on. Read the joint announcement:

We believe the future of enterprise computing will bring AI, HPC, and quantum together. That’s why, today, Quantinuum and Oracle have announced a multi-year strategic partnership to bring quantum computing to Oracle Cloud Infrastructure customers and accelerate the commercial adoption of hybrid quantum-AI computing. As part of the collaboration, our Helios quantum computer will be deployed in a U.S.-based Oracle AI data center, alongside OCI’s HPC and GPU infrastructure, and made available through OCI’s quantum service to give OCI customers a practical and secure way to explore how high-fidelity quantum computing can complement existing AI and HPC workloads—using the governance and access controls they already rely on. Read the joint announcement:

Quantinuum

22,305 views • 1 month ago

Behind the pioneering aircraft you know is an invisible digital ecosystem you need to see. As we count down to VivaTech, discover how next-generation technologies like smart computer vision for safer landings, AI models to counter fake media, and global satellite connectivity work together to protect physical and digital spaces. Get the latest updates on Airbus at #VivaTech here ➡️

Behind the pioneering aircraft you know is an invisible digital ecosystem you need to see. As we count down to VivaTech, discover how next-generation technologies like smart computer vision for safer landings, AI models to counter fake media, and global satellite connectivity work together to protect physical and digital spaces. Get the latest updates on Airbus at #VivaTech here ➡️

Airbus

10,727 views • 3 months ago

🚨ALL IN CHALLENGE - DAY 3🚨 Still holding $NOK strong 💪 Slightly down pre-market but still green on the overall port. 𝗧𝗵𝗲 𝘁𝗵𝗲𝘀𝗶𝘀 𝗶𝘀 𝗽𝗹𝗮𝘆𝗶𝗻𝗴 𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗮𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱: Nokia is accelerating into AI infrastructure with Optical and IP Networks guiding 18-20% growth in 2026 on surging demand. Q1 AI & Cloud sales surged +49%, Network Infrastructure outlook lifted to 12-14%, and the path to €2-2.5B operating profit is clear. 6G leadership, enterprise private wireless wins, and rising analyst targets continue to position $NOK for a significant re-rating. High-conviction names don’t rip every single day. This is the exact type of healthy consolidation that sets up the next leg higher. Patience is part of the edge here. The setup remains intact. Who’s still in $NOK with me?

🚨ALL IN CHALLENGE - DAY 3🚨 Still holding $NOK strong 💪 Slightly down pre-market but still green on the overall port. 𝗧𝗵𝗲 𝘁𝗵𝗲𝘀𝗶𝘀 𝗶𝘀 𝗽𝗹𝗮𝘆𝗶𝗻𝗴 𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗮𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱: Nokia is accelerating into AI infrastructure with Optical and IP Networks guiding 18-20% growth in 2026 on surging demand. Q1 AI & Cloud sales surged +49%, Network Infrastructure outlook lifted to 12-14%, and the path to €2-2.5B operating profit is clear. 6G leadership, enterprise private wireless wins, and rising analyst targets continue to position $NOK for a significant re-rating. High-conviction names don’t rip every single day. This is the exact type of healthy consolidation that sets up the next leg higher. Patience is part of the edge here. The setup remains intact. Who’s still in $NOK with me?

LEAPTRADER

14,841 views • 2 months ago

🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

16,062 views • 3 months ago

Get ready for the “071 Labs’ AI, DePIN, RWA & Infrastructure Summit,” happening on April 16! Date : April 16 Time : 12:00 PM – 4:00 PM (KST) Join us : This unmissable event unites the cutting-edge realms of AI, DePIN, RWA, and Blockchain Infrastructure into one power-packed summit. Explore how these evolving technologies are transforming global markets and reshaping the future of decentralized applications. Industry experts will discuss: - Integrating AI into emerging crypto solutions - The latest DePIN innovations driving decentralization - Unlocking real-world opportunities through RWA - Building robust Blockchain Infrastructure for tomorrow’s economy Stay ahead of the curve, connect with top minds in the field, and be part of the next wave of innovation. Don’t miss out on the highlight of the season, where vision meets opportunity! Featuring: Host 071labs @theprgenius 토큰포스트 - TokenPost Korea Blockstreet BLOCKMEDIA(블록미디어) 코인니스 Co-Host Aya Gold Sponsor DuckChain Plume Jito Labs Gaia 🌱 Exabits Mawari Speaker 071labs Aya Berachain Foundation 🐻⛓ XPIN Network🛰️ Exabits Gaia 🌱 Plume Mawari Jito Labs Sentient Theoriq Monad Infrared gensyn B-Harvest DePHY Flow Traders Mu Digital LayerZero nonce Classic Mind Network Aethir Bless Spacebar Ventures Imperator.co Alterim

Get ready for the “071 Labs’ AI, DePIN, RWA & Infrastructure Summit,” happening on April 16! Date : April 16 Time : 12:00 PM – 4:00 PM (KST) Join us : This unmissable event unites the cutting-edge realms of AI, DePIN, RWA, and Blockchain Infrastructure into one power-packed summit. Explore how these evolving technologies are transforming global markets and reshaping the future of decentralized applications. Industry experts will discuss: - Integrating AI into emerging crypto solutions - The latest DePIN innovations driving decentralization - Unlocking real-world opportunities through RWA - Building robust Blockchain Infrastructure for tomorrow’s economy Stay ahead of the curve, connect with top minds in the field, and be part of the next wave of innovation. Don’t miss out on the highlight of the season, where vision meets opportunity! Featuring: Host 071labs @theprgenius 토큰포스트 - TokenPost Korea Blockstreet BLOCKMEDIA(블록미디어) 코인니스 Co-Host Aya Gold Sponsor DuckChain Plume Jito Labs Gaia 🌱 Exabits Mawari Speaker 071labs Aya Berachain Foundation 🐻⛓ XPIN Network🛰️ Exabits Gaia 🌱 Plume Mawari Jito Labs Sentient Theoriq Monad Infrared gensyn B-Harvest DePHY Flow Traders Mu Digital LayerZero nonce Classic Mind Network Aethir Bless Spacebar Ventures Imperator.co Alterim

071labs

115,226 views • 1 year ago

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:

OptimAI Network

76,499 views • 1 year ago

Chainlink surfaces Swift's case against tearing finance down Nick Kerigan (Nick Kerigan), Managing Director and Head of Innovation at Swift (Swift), pushed back on the idea that building the next financial system means starting over, saying of that view, "I don't believe that at all." His version keeps the parts of the existing stack that already work, specifically the ones supplying trust and data, then builds new technology around them rather than over them. That new layer covers new forms of money and blockchains, with agentic AI named as a key part feeding into the same stack rather than running alongside it. Coming from the network sitting between more than 11,500 institutions, it's a fair marker of how the incumbents intend to arrive.

Chainlink surfaces Swift's case against tearing finance down Nick Kerigan (Nick Kerigan), Managing Director and Head of Innovation at Swift (Swift), pushed back on the idea that building the next financial system means starting over, saying of that view, "I don't believe that at all." His version keeps the parts of the existing stack that already work, specifically the ones supplying trust and data, then builds new technology around them rather than over them. That new layer covers new forms of money and blockchains, with agentic AI named as a key part feeding into the same stack rather than running alongside it. Coming from the network sitting between more than 11,500 institutions, it's a fair marker of how the incumbents intend to arrive.

BSCN

35,130 views • 1 month ago

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Melvin

180,578 views • 2 months ago

What if crypto research was as easy as chatting with ChatGPT, but powered by real market data👑 Introducing CMC AI, a powerful new tool from CoinMarketCap that combines the speed of AI with the depth of live crypto data. It delivers fast, data-backed answers to your questions: ✅ Want to know why Bitcoin's price is rising? ✅ Curious about the latest news on your favorite cryptocurrency? ✅ Need sentiment analysis? It pulls real-time data and explains it in seconds. But it goes far beyond basic Q&A. In the future, you’ll be able to ask anything! For example, you could ask it to: – Discover undervalued tokens based on volume, MC, and sentiment. – Compare Layer 1s or L2s by adoption, speed, and dev activity. – Detect rug-pull risk via wallet distribution and tokenomics red flags. – Break down your portfolio by risk, correlation, and potential return. – Explore new use cases in DeFi, AI, RWA and DePIN And much more! 🔗Try it here: CMC AI changes how you learn, think, and act in Web3🧠

What if crypto research was as easy as chatting with ChatGPT, but powered by real market data👑 Introducing CMC AI, a powerful new tool from CoinMarketCap that combines the speed of AI with the depth of live crypto data. It delivers fast, data-backed answers to your questions: ✅ Want to know why Bitcoin's price is rising? ✅ Curious about the latest news on your favorite cryptocurrency? ✅ Need sentiment analysis? It pulls real-time data and explains it in seconds. But it goes far beyond basic Q&A. In the future, you’ll be able to ask anything! For example, you could ask it to: – Discover undervalued tokens based on volume, MC, and sentiment. – Compare Layer 1s or L2s by adoption, speed, and dev activity. – Detect rug-pull risk via wallet distribution and tokenomics red flags. – Break down your portfolio by risk, correlation, and potential return. – Explore new use cases in DeFi, AI, RWA and DePIN And much more! 🔗Try it here: CMC AI changes how you learn, think, and act in Web3🧠

Alaoui Capital

34,908 views • 1 year ago

🖥️🇦🇲Armenia to Launch High-Tech AI Data Center in Gagarin Village Investor: Eleveight AI ($60 million in private investment). Hardware: Equipped with 512 of the latest #NVIDIA B300 Blackwell GPUs. #Armenia is among the first countries globally to deploy this technology. Power: 1.2 MW capacity (scalable to 2.0 MW) powered by renewable energy sources. Timeline: Full operational launch expected by March 2026. Key Impact: Armenia is transitioning from an "exporter of talent" to a regional #AI infrastructure hub. This facility allows local startups, businesses, and scientific groups to train complex AI models domestically using world-class resources, eliminating the need for foreign relocation or reliance on external cloud providers. #Gagarin #SouthCaucasus #MiddleEast #Technology #IT

🖥️🇦🇲Armenia to Launch High-Tech AI Data Center in Gagarin Village Investor: Eleveight AI ($60 million in private investment). Hardware: Equipped with 512 of the latest #NVIDIA B300 Blackwell GPUs. #Armenia is among the first countries globally to deploy this technology. Power: 1.2 MW capacity (scalable to 2.0 MW) powered by renewable energy sources. Timeline: Full operational launch expected by March 2026. Key Impact: Armenia is transitioning from an "exporter of talent" to a regional #AI infrastructure hub. This facility allows local startups, businesses, and scientific groups to train complex AI models domestically using world-class resources, eliminating the need for foreign relocation or reliance on external cloud providers. #Gagarin #SouthCaucasus #MiddleEast #Technology #IT

Arthur Maghakian

59,896 views • 8 months ago

I studied 𝟭,𝟰𝟲𝟬 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗳𝗹𝗼𝘄𝘀 across 𝟵𝟴𝟲 apps & websites. the average app has 25 onboarding screens. and some of the longest ones are the most successful. so it's probably not about making onboarding shorter. then what actually makes a good onboarding flow? Mobbin's data: → 22% of apps personalize during onboarding (AI apps: only 7%) → 22% show a paywall during onboarding → 6.3% do both This 10-minute video breaks down how top apps design onboarding, from Duolingo, Headspace , Bump by amo, Airbnb, and more 🔎 'mobbin onboarding' on youtube

I studied 𝟭,𝟰𝟲𝟬 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗳𝗹𝗼𝘄𝘀 across 𝟵𝟴𝟲 apps & websites. the average app has 25 onboarding screens. and some of the longest ones are the most successful. so it's probably not about making onboarding shorter. then what actually makes a good onboarding flow? Mobbin's data: → 22% of apps personalize during onboarding (AI apps: only 7%) → 22% show a paywall during onboarding → 6.3% do both This 10-minute video breaks down how top apps design onboarding, from Duolingo, Headspace , Bump by amo, Airbnb, and more 🔎 'mobbin onboarding' on youtube

Rachel How

26,010 views • 5 months ago

The video smooth zoom on the Samsung Galaxy S26 Ultra is still the closest thing to a professional camcorder experience in the smartphone industry today. In fact, it’s even easier to control than the iPhone. On many other phones, video zooming requires constant finger movement and very precise control. The zoom speed can easily become inconsistent, suddenly speeding up or slowing down. Samsung works differently. You simply hold your finger at a certain position, and the phone continues zooming at a constant speed. The entire process feels extremely stable and linear. It genuinely resembles the powered zoom control of a professional video camera. This logic is fundamentally related to Samsung’s AI slow motion technology. They share the same core foundation: real time control over motion trajectories, speed transitions, and frame interpolation. What you’re seeing here was shot in very windy conditions using Samsung’s Pro Video mode, continuously zooming from 5x to 25x. Aside from some slight stutter during optical lens switching points, the continuous zoom transition within digital zoom ranges is arguably the closest thing to a professional camera currently available on a smartphone. So if the future Samsung Galaxy S27 Ultra really removes the 3x telephoto camera, it could actually improve the video zoom experience further. Fewer optical switching points would theoretically reduce transition jumps and stutters, making the entire zoom range feel even more natural and continuous.

The video smooth zoom on the Samsung Galaxy S26 Ultra is still the closest thing to a professional camcorder experience in the smartphone industry today. In fact, it’s even easier to control than the iPhone. On many other phones, video zooming requires constant finger movement and very precise control. The zoom speed can easily become inconsistent, suddenly speeding up or slowing down. Samsung works differently. You simply hold your finger at a certain position, and the phone continues zooming at a constant speed. The entire process feels extremely stable and linear. It genuinely resembles the powered zoom control of a professional video camera. This logic is fundamentally related to Samsung’s AI slow motion technology. They share the same core foundation: real time control over motion trajectories, speed transitions, and frame interpolation. What you’re seeing here was shot in very windy conditions using Samsung’s Pro Video mode, continuously zooming from 5x to 25x. Aside from some slight stutter during optical lens switching points, the continuous zoom transition within digital zoom ranges is arguably the closest thing to a professional camera currently available on a smartphone. So if the future Samsung Galaxy S27 Ultra really removes the 3x telephoto camera, it could actually improve the video zoom experience further. Fewer optical switching points would theoretically reduce transition jumps and stutters, making the entire zoom range feel even more natural and continuous.

Ice Universe

27,250 views • 4 months ago

SOMEONE FROM TOKYO IS MAPPING BIRD LANGUAGE INTO REAL DATA PATTERNS AND THE VISUALIZATION LOOKS LIKE A NEURAL NETWORK DREAMING Every bird sound - frequency, duration, amplitude and modulation - gets converted into 3D space coordinates and rendered as a cluster of colored points in real time through Deepen AI. A microphone captures the acoustic signal, FFT breaks it down into frequency components from 1 kHz to 8 kHz where most birds communicate, an ML model classifies the pattern and the mapped data hits a graph where each call type gets its own color and position in space. Birds have a vocal repertoire of 5 to 200+ unique signals depending on the species - and every signal carries different information about predators, food, territory and mates that humans simply can't hear. The same technology that decodes bird language detects anomalies in industrial machinery, cardiac rhythms and structural vibrations in buildings - he just trained it on nature first.

SOMEONE FROM TOKYO IS MAPPING BIRD LANGUAGE INTO REAL DATA PATTERNS AND THE VISUALIZATION LOOKS LIKE A NEURAL NETWORK DREAMING Every bird sound - frequency, duration, amplitude and modulation - gets converted into 3D space coordinates and rendered as a cluster of colored points in real time through Deepen AI. A microphone captures the acoustic signal, FFT breaks it down into frequency components from 1 kHz to 8 kHz where most birds communicate, an ML model classifies the pattern and the mapped data hits a graph where each call type gets its own color and position in space. Birds have a vocal repertoire of 5 to 200+ unique signals depending on the species - and every signal carries different information about predators, food, territory and mates that humans simply can't hear. The same technology that decodes bird language detects anomalies in industrial machinery, cardiac rhythms and structural vibrations in buildings - he just trained it on nature first.

Cortex

11,128 views • 4 months ago

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS

Rittenhouse Research

13,919 views • 2 months ago

🦔Turn your sound on for this one. This is what it sounds like to live next to an AI data center, 24 hours a day. In Dowagiac, Michigan, residents sued after a facility run by Hyperscale Data ran around the clock at 78 decibels outside their homes. The company offered to buy their houses rather than fix the noise. One family has lived on that street for nearly a century. My Take This isn't one town with one bad facility. Microsoft faces a class-action in Wisconsin over data center noise residents can hear a mile and a half from the property line. In Virginia, one-third of data centers sit within 200 feet of homes. Noise from thousands of servers can hit 96 decibels, and diesel backup generators reach 105, which is a jet overhead. Residents report headaches, nausea, and sleep problems. Property values near this kind of infrastructure drop up to 20%. The company response in Michigan was to offer to buy the homes rather than quiet the facility, homes that families spent decades in, priced against a corporate buyout. Water in Oregon, wells in Georgia, ratepayer costs in Louisiana, river water in California, and now noise in Michigan and Wisconsin. None of this was in the brochure when these projects got approved. Closed-loop cooling and proper sound barriers exist and would solve most of this, but they cost more, so the companies skip them and the neighbors lose sleep and equity instead. Hedgie🤗

🦔Turn your sound on for this one. This is what it sounds like to live next to an AI data center, 24 hours a day. In Dowagiac, Michigan, residents sued after a facility run by Hyperscale Data ran around the clock at 78 decibels outside their homes. The company offered to buy their houses rather than fix the noise. One family has lived on that street for nearly a century. My Take This isn't one town with one bad facility. Microsoft faces a class-action in Wisconsin over data center noise residents can hear a mile and a half from the property line. In Virginia, one-third of data centers sit within 200 feet of homes. Noise from thousands of servers can hit 96 decibels, and diesel backup generators reach 105, which is a jet overhead. Residents report headaches, nausea, and sleep problems. Property values near this kind of infrastructure drop up to 20%. The company response in Michigan was to offer to buy the homes rather than quiet the facility, homes that families spent decades in, priced against a corporate buyout. Water in Oregon, wells in Georgia, ratepayer costs in Louisiana, river water in California, and now noise in Michigan and Wisconsin. None of this was in the brochure when these projects got approved. Closed-loop cooling and proper sound barriers exist and would solve most of this, but they cost more, so the companies skip them and the neighbors lose sleep and equity instead. Hedgie🤗

Hedgie

717,539 views • 1 month ago

$4.4 trillion in PE assets under management Monitored by analysts who spend 500+ hours a year copying numbers from PDFs into Excel. Not analyzing. Copying and pasting. A fund has 10 portcos. One on NetSuite, one on QuickBooks, three on Excel, one CFO sending a paragraph in an email every quarter. Company A calls it "Revenue." Company B calls it "Net Sales." Company C doesn't even use the same EBITDA calculation. Before AI can do anything useful, you need four layers: 1. Ingestion: get the data out of PDFs, spreadsheets, emails, whatever format it shows up in 2. Normalization: make sure the same metric means the same thing across every company 3. Storage: structured database that can actually be queried, not a folder of files 4. Query layer: natural language interface so a non-technical partner can ask "which portco is trending down on margins?" and get a real answer Most companies jump straight to layer 4 and wonder why nothing works. The unlock is building layers 1-3 first. Once those exist, the AI part is MUCH easier. The firms that get their data infrastructure right first will be untouchable.

$4.4 trillion in PE assets under management Monitored by analysts who spend 500+ hours a year copying numbers from PDFs into Excel. Not analyzing. Copying and pasting. A fund has 10 portcos. One on NetSuite, one on QuickBooks, three on Excel, one CFO sending a paragraph in an email every quarter. Company A calls it "Revenue." Company B calls it "Net Sales." Company C doesn't even use the same EBITDA calculation. Before AI can do anything useful, you need four layers: 1. Ingestion: get the data out of PDFs, spreadsheets, emails, whatever format it shows up in 2. Normalization: make sure the same metric means the same thing across every company 3. Storage: structured database that can actually be queried, not a folder of files 4. Query layer: natural language interface so a non-technical partner can ask "which portco is trending down on margins?" and get a real answer Most companies jump straight to layer 4 and wonder why nothing works. The unlock is building layers 1-3 first. Once those exist, the AI part is MUCH easier. The firms that get their data infrastructure right first will be untouchable.

James Camp 🛠,🛠

19,249 views • 6 months ago

1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.

1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.

AiMind

82,647 views • 28 days ago

🚨 BREAKING — one of the strongest OpenClaw setups on Polymarket just went public. A trader reportedly started with ~$100–200 and scaled it to ~$3.7M. No insider access. No political connections. Just a developer running his own automation built with OpenClaw. Profile → Copytrade → I went through the framework myself. What surprised me: There’s no huge infrastructure. No complex quant stack. No giant data pipelines. Just clean logic and disciplined automation. After about 8 hours analyzing it, the strategy breaks down into three parts. 1) “Free money” via NO positions The bot targets outcomes with near-zero probability. Instead of chasing big wins, it accumulates a massive number of small high-probability NO trades. Not speculation — systematic probability harvesting. 2) Logical arbitrage Sometimes Outcome A logically implies Outcome B, but markets don’t adjust instantly. The bot detects these inconsistencies and enters before repricing happens. By the time the headline reaches traders, the window is already closed. 3) Retail-driven markets Sports and political markets are dominated by retail flow and emotional reactions. Prices overshoot, spreads widen, and inefficiencies appear constantly. The bot sits in those gaps and clips small edges repeatedly. Scale is the edge. 4,192 trades executed. Individually small. Together they compounded into roughly ~$3.7M profit. Largest single win: $1,464,152. The equity curve is almost vertical. It’s not about predicting events. It’s about exploiting structural inefficiencies faster than the crowd.

Discover

186,697 views • 6 months ago