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Life AI is validating our Biohub infrastructure through real-world deployments that test whether preventive healthcare can operate sustainably at scale. 1. National-scale operation The nationwide deployment of the PREVANA subnet across Thailand is Life AI’s first national-scale operating environment. This deployment allows Biohub to be evaluated across real geographic,...

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LIFE AI

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12,862 просмотров • 5 месяцев назад •via X (Twitter)

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🔥 Launching the Fireworks AI Experimentation Platform (GA) and Build SDK (Beta)🔥 Top AI teams don't win by chance - they win by running 10X more experiments than competitors. Launching early, collecting feedback, tuning and selecting models, running offline evals, and validating impact with product metrics in a fast, continuous cycle is critical to build successful AI products. But, the need to manage complex infrastructure, wait for GPUs, reconcile differences between training and serving, and maintain reliability, slow AI teams down. Now, with the Firework’s experimentation platform, experimenting with open models just got a whole lot easier. With the help of our brand-new Python SDK (in beta), you can: → Stop waiting for GPUs, fine-tune and deploy 1000s of models on a single GPU deployment using Multi-LoRA → Programmatically run large-scale fine-tuning experiments → Scale evals with fully managed and autoscaling GPU deployments Check out our blog post to learn more and see a demo of running 125 experiments in 3 minutes on a single deployment:
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🔥 Launching the Fireworks AI Experimentation Platform (GA) and Build SDK (Beta)🔥 Top AI teams don't win by chance - they win by running 10X more experiments than competitors. Launching early, collecting feedback, tuning and selecting models, running offline evals, and validating impact with product metrics in a fast, continuous cycle is critical to build successful AI products. But, the need to manage complex infrastructure, wait for GPUs, reconcile differences between training and serving, and maintain reliability, slow AI teams down. Now, with the Firework’s experimentation platform, experimenting with open models just got a whole lot easier. With the help of our brand-new Python SDK (in beta), you can: → Stop waiting for GPUs, fine-tune and deploy 1000s of models on a single GPU deployment using Multi-LoRA → Programmatically run large-scale fine-tuning experiments → Scale evals with fully managed and autoscaling GPU deployments Check out our blog post to learn more and see a demo of running 125 experiments in 3 minutes on a single deployment:

Fireworks AI

17,619 просмотров • 1 год назад

BREAKING: our AI Agents can now deep-research 10,000 accounts faster (and cheaper) than an Uber from JFK. Last month our growth team pushed this hard: 40,000 accounts deep-researched for product launches → personalized outreach → 15+ meetings and a closed-won in 30 days. Most GTM teams don’t lack ideas. They struggle with the cost and complexity of running them at scale. Kunal , who leads our agent infrastructure, saw this firsthand. Adoption climbed past 1M runs per month, but cost was still the ceiling on pushing always-on workflows. After months of evaluating models against real customer workflows, Kunal & team shipped a major upgrade: Optimized agents with peak performance at a 10x cost reduction. Always-on intelligence in outbound becomes practical, not theoretical. What this unlocks: If you’re a cybersecurity company, Unify’s optimized agents can: (1) Monitor your entire TAM for breaches (2) Auto-qualify accounts if they’re B2B + using Auth0 (3) Use real breach context to write outbound that’s timely, relevant, human No manual research. No painful copywriting. No cost tradeoffs. The future of GTM is deploying 100,000 deep-research agents daily to understand every shift in your market. That future is closer than people think.
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BREAKING: our AI Agents can now deep-research 10,000 accounts faster (and cheaper) than an Uber from JFK. Last month our growth team pushed this hard: 40,000 accounts deep-researched for product launches → personalized outreach → 15+ meetings and a closed-won in 30 days. Most GTM teams don’t lack ideas. They struggle with the cost and complexity of running them at scale. Kunal , who leads our agent infrastructure, saw this firsthand. Adoption climbed past 1M runs per month, but cost was still the ceiling on pushing always-on workflows. After months of evaluating models against real customer workflows, Kunal & team shipped a major upgrade: Optimized agents with peak performance at a 10x cost reduction. Always-on intelligence in outbound becomes practical, not theoretical. What this unlocks: If you’re a cybersecurity company, Unify’s optimized agents can: (1) Monitor your entire TAM for breaches (2) Auto-qualify accounts if they’re B2B + using Auth0 (3) Use real breach context to write outbound that’s timely, relevant, human No manual research. No painful copywriting. No cost tradeoffs. The future of GTM is deploying 100,000 deep-research agents daily to understand every shift in your market. That future is closer than people think.

Austin Hughes

41,139 просмотров • 7 месяцев назад

Tokenization as Infrastructure, Not a Shortcut In this clip, André Casterman explains why tokenization is best understood as an upgrade to the internet’s financial infrastructure, not a guarantee of liquidity or success. Tokenization does not create demand on its own. Liquidity still matters. What it does change is the efficiency of how value moves. Cheaper settlement. Faster transfer. Native verifiability. Stablecoins are a clear example. Instead of routing payments through correspondent banking and local clearing entities, value can move directly by transferring tokens. The asset does not need to be physically moved or reconciled across jurisdictions. The result is lower cost and less friction. Verifiability is the real unlock. Unlike physical assets, which require costly and invasive processes to prove authenticity, tokenized assets can be verified on-chain, publicly, and without altering the asset itself. This is the shift from moving information to moving value. Tokenization is not a product. It is infrastructure. And infrastructure is what enables real-world use at scale. — André Casterman 🇦🇪
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Tokenization as Infrastructure, Not a Shortcut In this clip, André Casterman explains why tokenization is best understood as an upgrade to the internet’s financial infrastructure, not a guarantee of liquidity or success. Tokenization does not create demand on its own. Liquidity still matters. What it does change is the efficiency of how value moves. Cheaper settlement. Faster transfer. Native verifiability. Stablecoins are a clear example. Instead of routing payments through correspondent banking and local clearing entities, value can move directly by transferring tokens. The asset does not need to be physically moved or reconciled across jurisdictions. The result is lower cost and less friction. Verifiability is the real unlock. Unlike physical assets, which require costly and invasive processes to prove authenticity, tokenized assets can be verified on-chain, publicly, and without altering the asset itself. This is the shift from moving information to moving value. Tokenization is not a product. It is infrastructure. And infrastructure is what enables real-world use at scale. — André Casterman 🇦🇪

XDC Foundation

14,607 просмотров • 6 месяцев назад

Efficiency is the real bottleneck in AI. As AI adoption accelerates, data centers are becoming some of the most energy-hungry assets in the world. $NBIS' approach is clear: sustainability isn’t an afterthought, it’s a design principle. The company designs its servers, racks, and data centers in-house, allowing: - Higher thermal & power efficiency - Faster cluster deployment - Lower failure rates and easier maintenance - Longer hardware lifespan Essentially, more compute per unit of energy. In Finland, $NBIS operates hybrid cooling systems that combine outside air with direct liquid-to-chip cooling, using a closed-loop system with zero water withdrawal. Even more interesting: waste heat isn’t wasted. In 2024 alone, Nebius’ Finnish data center: - Helped heat ~2,500 households - Avoided ~3,200 tons of CO₂ emissions This is circular efficiency in practice. On the software side, $NBIS' full-stack cloud platform avoids overprovisioning by sharing buffers across hundreds of customers, while offering deep observability, down to tracking every kilowatt used to train large models across compute and storage. All in all, $NBIS is building AI infrastructure optimized for peak performance, cost efficiency, resilience, and long-term sustainability.
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Efficiency is the real bottleneck in AI. As AI adoption accelerates, data centers are becoming some of the most energy-hungry assets in the world. $NBIS' approach is clear: sustainability isn’t an afterthought, it’s a design principle. The company designs its servers, racks, and data centers in-house, allowing: - Higher thermal & power efficiency - Faster cluster deployment - Lower failure rates and easier maintenance - Longer hardware lifespan Essentially, more compute per unit of energy. In Finland, $NBIS operates hybrid cooling systems that combine outside air with direct liquid-to-chip cooling, using a closed-loop system with zero water withdrawal. Even more interesting: waste heat isn’t wasted. In 2024 alone, Nebius’ Finnish data center: - Helped heat ~2,500 households - Avoided ~3,200 tons of CO₂ emissions This is circular efficiency in practice. On the software side, $NBIS' full-stack cloud platform avoids overprovisioning by sharing buffers across hundreds of customers, while offering deep observability, down to tracking every kilowatt used to train large models across compute and storage. All in all, $NBIS is building AI infrastructure optimized for peak performance, cost efficiency, resilience, and long-term sustainability.

M. V. Cunha

44,039 просмотров • 7 месяцев назад

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.
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Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

King

155,637 просмотров • 7 месяцев назад

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV
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On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

Sean Henry

1,275,331 просмотров • 2 месяцев назад

The hardest problems in AI aren't research problems anymore. They're deployment problems. It’s how we actually deliver real value, today, to build the future people want. That’s why, after 20 years in AI, my next step was inevitable: make robots do useful work for and alongside people, right now. Today, I am delighted to announce the launch of Walden Robotics to tackle just that. We started this year and are coming out of stealth today with a $300M seed round backed by some of the most serious companies and investors in the world. They have seen firsthand our general-purpose robots being useful in production on day one, and getting better every day after. You can see a glimpse of what we've been building in the video below. Physical AI has gone through a rapid phase transition, in part thanks to pioneering research from my friends and co-founders Russ Tedrake , Ben Burchfiel , Siyuan Feng, Rareș Ambruș , and many others at Walden. But from our long experience working together with co-founders Kerri Fetzer-Borelli and Dave Johnson, we learned how hard it is to deploy cutting-edge AI in a real, live, incredibly sophisticated production environment with an intricate ballet of automation and human ingenuity. That’s why we deliberately created Walden Robotics as a full-stack, human-centric, customer-focused robotics company from the start: we seeded the company with a world-class team across hardware, software, AI, deployment, operations, product, and business talent, so we could continuously optimize our whole system end-to-end, deeply and purposefully, from real-world experience with real customers. The efficacy of this strategy speaks for itself: since February, our general-purpose robots have been doing useful work in production at a Toyota plant in North America, moving from first pilot to real work in under two months. Not a lab. Not a demo. Not a future promise. Real work on a real line, today, at one of the best large-scale manufacturers in the world, with general-purpose robots that get better every day. And this is just the beginning. Two ways to find us: If you run a manufacturing or logistics business and want robots that are widely useful now, not someday, let's talk. We own “ for a reason! And if you want to build them: we're hiring across the company, from software, to hardware, AI, ops, product, business, and more. In particular, as the Chief Strategy Officer at Walden, I am recruiting for three incredibly impactful founding roles to fuel our agent-native go-to-market engine. Check out Let’s build together!
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The hardest problems in AI aren't research problems anymore. They're deployment problems. It’s how we actually deliver real value, today, to build the future people want. That’s why, after 20 years in AI, my next step was inevitable: make robots do useful work for and alongside people, right now. Today, I am delighted to announce the launch of Walden Robotics to tackle just that. We started this year and are coming out of stealth today with a $300M seed round backed by some of the most serious companies and investors in the world. They have seen firsthand our general-purpose robots being useful in production on day one, and getting better every day after. You can see a glimpse of what we've been building in the video below. Physical AI has gone through a rapid phase transition, in part thanks to pioneering research from my friends and co-founders Russ Tedrake , Ben Burchfiel , Siyuan Feng, Rareș Ambruș , and many others at Walden. But from our long experience working together with co-founders Kerri Fetzer-Borelli and Dave Johnson, we learned how hard it is to deploy cutting-edge AI in a real, live, incredibly sophisticated production environment with an intricate ballet of automation and human ingenuity. That’s why we deliberately created Walden Robotics as a full-stack, human-centric, customer-focused robotics company from the start: we seeded the company with a world-class team across hardware, software, AI, deployment, operations, product, and business talent, so we could continuously optimize our whole system end-to-end, deeply and purposefully, from real-world experience with real customers. The efficacy of this strategy speaks for itself: since February, our general-purpose robots have been doing useful work in production at a Toyota plant in North America, moving from first pilot to real work in under two months. Not a lab. Not a demo. Not a future promise. Real work on a real line, today, at one of the best large-scale manufacturers in the world, with general-purpose robots that get better every day. And this is just the beginning. Two ways to find us: If you run a manufacturing or logistics business and want robots that are widely useful now, not someday, let's talk. We own “ for a reason! And if you want to build them: we're hiring across the company, from software, to hardware, AI, ops, product, business, and more. In particular, as the Chief Strategy Officer at Walden, I am recruiting for three incredibly impactful founding roles to fuel our agent-native go-to-market engine. Check out Let’s build together!

Adrien Gaidon

63,597 просмотров • 20 дней назад

$IonQ Tennessee just allocated $20M to accelerate quantum computing - 🧵 Governor Bill Lee’s FY27 budget includes funding specifically designed to attract federal and private investment in quantum, targeting advanced manufacturing, life sciences, and logistics sectors. Why this matters: State-level quantum funding signals a shift from pure research to economic deployment. Tennessee isn’t funding university labs - they’re building infrastructure to attract quantum companies and create high-wage jobs. Tennessee already has Oak Ridge National Laboratory, one of the world’s premier quantum research facilities. Adding $20M in state support creates a complete ecosystem: research capability, government backing, workforce development, and commercial deployment pathways. The timing is perfect: Quantum companies with deployable systems can now tap into state partnerships, regional contracts, and workforce programs. This creates real commercialization opportunities beyond federal research grants. For context, IonQ already works with Oak Ridge and has the commercial systems ready to deploy. But this funding isn’t just about one company - it’s about Tennessee positioning itself as THE quantum hub in the Southeast, competing with Colorado, Maryland, and California. What to watch: When states start competing for quantum companies with actual budget allocations, it validates that quantum computing is becoming an economic sector, not just a science project. Expect more states to follow Tennessee’s lead in 2026. The quantum industry is transitioning from “interesting research” to “strategic economic investment” at the state government level. That’s a meaningful milestone. 🎯 #QuantumComputing #Tennessee #Innovation #IONQ #EconomicPolicy #TechIndustry
0:48

$IonQ Tennessee just allocated $20M to accelerate quantum computing - 🧵 Governor Bill Lee’s FY27 budget includes funding specifically designed to attract federal and private investment in quantum, targeting advanced manufacturing, life sciences, and logistics sectors. Why this matters: State-level quantum funding signals a shift from pure research to economic deployment. Tennessee isn’t funding university labs - they’re building infrastructure to attract quantum companies and create high-wage jobs. Tennessee already has Oak Ridge National Laboratory, one of the world’s premier quantum research facilities. Adding $20M in state support creates a complete ecosystem: research capability, government backing, workforce development, and commercial deployment pathways. The timing is perfect: Quantum companies with deployable systems can now tap into state partnerships, regional contracts, and workforce programs. This creates real commercialization opportunities beyond federal research grants. For context, IonQ already works with Oak Ridge and has the commercial systems ready to deploy. But this funding isn’t just about one company - it’s about Tennessee positioning itself as THE quantum hub in the Southeast, competing with Colorado, Maryland, and California. What to watch: When states start competing for quantum companies with actual budget allocations, it validates that quantum computing is becoming an economic sector, not just a science project. Expect more states to follow Tennessee’s lead in 2026. The quantum industry is transitioning from “interesting research” to “strategic economic investment” at the state government level. That’s a meaningful milestone. 🎯 #QuantumComputing #Tennessee #Innovation #IONQ #EconomicPolicy #TechIndustry

TechInnovation

15,775 просмотров • 6 месяцев назад

Orbit AI Satellite Successfully Achieve World’s First Orbital AI Deployment and Launching Digital AI Sovereignty Decentralized Orbital AI Network Orbit AI Orbit AI🛰️ today announced that the first satellite, “OAI Genesis-1,” has successfully launched and entered Low Earth Orbit (LEO). Amidst fierce competition from tech giants (e.g., Starlink Starlink Elon Musk , Google AI Project Suncatcher) in space AI computing, this launch signifies Orbit AI’s position as the first to achieve real-world AI deployment, formally inaugurating its "Orbit AI Cloud Platform." Genesis-1 is equipped with NVIDIA NVIDIA AI Compute Cores, running a 2.6B parameter AI model for real-time analysis of infrared remote sensing data in space. By processing data on orbit, Genesis-1 drastically reduces critical information retrieval time (e.g., disaster alerts, maritime monitoring) from hours to mere seconds, while cutting transmission bandwidth costs by over 90%. Furthermore, Orbit AI has partnered with from energy company Powerbank (NASDAQ: SUUN) ( utilizing infinite solar power to achieve carbon-neutral computing and projecting a reduction in overall energy operational costs by 60%. Following its triumph at the BNB Chain Hackathon ( Orbit AI protocol is committed to creating an ultimate censorship-resistant deployment environment: Developers can deploy AI models, privacy applications, financial algorithms, and even blockchain nodes on the satellite network. This ensures that code and data operate in a physically isolated, neutral environment beyond the jurisdiction of major nations, guaranteeing extreme digital sovereignty and service resilience. Orbit AI will also leverage the RWA (Real World Assets) mechanism to allow community users to purchase satellite NFT shares, becoming co-owners of this space infrastructure and sharing in its compute revenues, thus building a community-owned orbital AI economy.
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Orbit AI Satellite Successfully Achieve World’s First Orbital AI Deployment and Launching Digital AI Sovereignty Decentralized Orbital AI Network Orbit AI Orbit AI🛰️ today announced that the first satellite, “OAI Genesis-1,” has successfully launched and entered Low Earth Orbit (LEO). Amidst fierce competition from tech giants (e.g., Starlink Starlink Elon Musk , Google AI Project Suncatcher) in space AI computing, this launch signifies Orbit AI’s position as the first to achieve real-world AI deployment, formally inaugurating its "Orbit AI Cloud Platform." Genesis-1 is equipped with NVIDIA NVIDIA AI Compute Cores, running a 2.6B parameter AI model for real-time analysis of infrared remote sensing data in space. By processing data on orbit, Genesis-1 drastically reduces critical information retrieval time (e.g., disaster alerts, maritime monitoring) from hours to mere seconds, while cutting transmission bandwidth costs by over 90%. Furthermore, Orbit AI has partnered with from energy company Powerbank (NASDAQ: SUUN) ( utilizing infinite solar power to achieve carbon-neutral computing and projecting a reduction in overall energy operational costs by 60%. Following its triumph at the BNB Chain Hackathon ( Orbit AI protocol is committed to creating an ultimate censorship-resistant deployment environment: Developers can deploy AI models, privacy applications, financial algorithms, and even blockchain nodes on the satellite network. This ensures that code and data operate in a physically isolated, neutral environment beyond the jurisdiction of major nations, guaranteeing extreme digital sovereignty and service resilience. Orbit AI will also leverage the RWA (Real World Assets) mechanism to allow community users to purchase satellite NFT shares, becoming co-owners of this space infrastructure and sharing in its compute revenues, thus building a community-owned orbital AI economy.

Orbit AI🛰️

24,839 просмотров • 7 месяцев назад

In continuous cable manufacturing, compressed air performance must keep pace with constantly changing production demands. In a leading cable manufacturing facility, compressed air plays a critical role across extrusion controls, air-wipe drying, cleaning, and material handling systems, making pressure stability and compressor reliability essential for consistent product quality and high uptime. At the plant, a 75 kW fixed-speed compressor reliably met base air requirements. However, variations in production loads, line speeds, and process cycles led to frequent demand fluctuations, resulting in repeated compressor cycling, higher energy consumption, and pressure instability across the system. With the deployment of ELGi’s DEMAND=MATCH System, performance was evaluated under actual operating conditions by comparing operations with and without DEMAND=MATCH. The results were clearly validated. • 11.1% reduction in average power consumption (from 69.9 kWh to 62.1 kWh per hour) • Approximately 1,489 units of energy saved over just eight days of actual running • Reduced load cycling, leading to improved pressure stability and lower mechanical stress on the compressor This deployment demonstrates how intelligent airflow control can deliver measurable gains in energy efficiency, system stability, and operational reliability, all critical in a continuous manufacturing environment. A strong example of how aligning compressed air delivery with real-time demand can drive sustainable performance improvements on the shopfloor. Learn more about how DEMAND=MATCH optimises compressed air systems:
2:19

In continuous cable manufacturing, compressed air performance must keep pace with constantly changing production demands. In a leading cable manufacturing facility, compressed air plays a critical role across extrusion controls, air-wipe drying, cleaning, and material handling systems, making pressure stability and compressor reliability essential for consistent product quality and high uptime. At the plant, a 75 kW fixed-speed compressor reliably met base air requirements. However, variations in production loads, line speeds, and process cycles led to frequent demand fluctuations, resulting in repeated compressor cycling, higher energy consumption, and pressure instability across the system. With the deployment of ELGi’s DEMAND=MATCH System, performance was evaluated under actual operating conditions by comparing operations with and without DEMAND=MATCH. The results were clearly validated. • 11.1% reduction in average power consumption (from 69.9 kWh to 62.1 kWh per hour) • Approximately 1,489 units of energy saved over just eight days of actual running • Reduced load cycling, leading to improved pressure stability and lower mechanical stress on the compressor This deployment demonstrates how intelligent airflow control can deliver measurable gains in energy efficiency, system stability, and operational reliability, all critical in a continuous manufacturing environment. A strong example of how aligning compressed air delivery with real-time demand can drive sustainable performance improvements on the shopfloor. Learn more about how DEMAND=MATCH optimises compressed air systems:

Elgi Equipments Limited

85,677 просмотров • 6 месяцев назад

SpaceX has officially acquired xAI and this is a HUGE deal, probably bigger than most people realize. SpaceX + xAI together are about to build a single, vertically integrated engine that connects AI, rockets, space based internet, and real time information all into one unified system. At the core of this announcement is the fact that AI can’t scale forever on Earth. Think about it. Data centers are already running into hard limits with power, cooling, land, and environmental cost. On top of this, global AI electricity demand is exploding, and doing all of this on the ground just doesn’t scale long term. Therefore Space changes this. In orbit, you can get things like near-constant solar power, you don’t have cooling problems, there’s minimal land/environmental impact, and you can build systems that can run continuously with minimal maintenance. This is why space-based AI is actually very practical and there’s only one company that can do it, which is SpaceX! The scale that SpaceX is aiming for is pretty wild. Elon is talking about 1/ 1 million satellites acting as orbital data centers, and 2/ Launching ~1 million tons of satellites per year Which means 3/ At 100 kW per ton, that’s 100 gigawatts of AI compute every year 4/ And that can scale to 1 terawatt per year. FYI, adding 100 GW of new compute capacity annually in space would be like building out roughly 20-25% of the USA’s total average electricity demand every single year. And scaling to 1 TW/year would exceed the nation’s entire power usage, ALL without burdening Earth’s grids! Bro… this is next level… I repeat, as of today, SpaceX is the ONLY company positioned to do this, especially with Starship - the largest flying vehicle ever. In the future, Elon’s saying the team is going to have Starship launch 200 tons at a time, on an HOURLY basis… WTF?! That’s is absolutely mind boggling. In my opinion, whichever company builds the lowest cost AI compute is going to be the first company that defines the next era of AI, which will all be in space… and my $ is that SpaceX is going to be the first one to do it. Then, when you zoom out even further, this will unlock things most people aren’t even thinking about yet today bc it’s too sci-fi. Like, • Powering lunar bases • In-space propellant transfer • Manufacturing in orbit and on the Moon • Deep-space AI infrastructure using massive solar collectors • Self-growing civilizations on the Moon, Mars, and beyond By tapping into space’s unlimited solar energy, this is how we get to a Kardashev-level civilization! So for SpaceX, the faster Starship progresses, there will be massive new revenue streams and will lead in building out space-based AI infrastructures. For xAI, AI scales without ruining Earth, deeply integrated with everything SpaceX is building, further understanding the universe. For us humans, AI will grow without destroying the planet, and there’s a real path to becoming multi-planetary. I believe SpaceX acquiring xAI is officially the blueprint for where AI, energy, and humanity are headed. Congratulations to everyone, this is such an exciting time to be alive!
1:14

SpaceX has officially acquired xAI and this is a HUGE deal, probably bigger than most people realize. SpaceX + xAI together are about to build a single, vertically integrated engine that connects AI, rockets, space based internet, and real time information all into one unified system. At the core of this announcement is the fact that AI can’t scale forever on Earth. Think about it. Data centers are already running into hard limits with power, cooling, land, and environmental cost. On top of this, global AI electricity demand is exploding, and doing all of this on the ground just doesn’t scale long term. Therefore Space changes this. In orbit, you can get things like near-constant solar power, you don’t have cooling problems, there’s minimal land/environmental impact, and you can build systems that can run continuously with minimal maintenance. This is why space-based AI is actually very practical and there’s only one company that can do it, which is SpaceX! The scale that SpaceX is aiming for is pretty wild. Elon is talking about 1/ 1 million satellites acting as orbital data centers, and 2/ Launching ~1 million tons of satellites per year Which means 3/ At 100 kW per ton, that’s 100 gigawatts of AI compute every year 4/ And that can scale to 1 terawatt per year. FYI, adding 100 GW of new compute capacity annually in space would be like building out roughly 20-25% of the USA’s total average electricity demand every single year. And scaling to 1 TW/year would exceed the nation’s entire power usage, ALL without burdening Earth’s grids! Bro… this is next level… I repeat, as of today, SpaceX is the ONLY company positioned to do this, especially with Starship - the largest flying vehicle ever. In the future, Elon’s saying the team is going to have Starship launch 200 tons at a time, on an HOURLY basis… WTF?! That’s is absolutely mind boggling. In my opinion, whichever company builds the lowest cost AI compute is going to be the first company that defines the next era of AI, which will all be in space… and my $ is that SpaceX is going to be the first one to do it. Then, when you zoom out even further, this will unlock things most people aren’t even thinking about yet today bc it’s too sci-fi. Like, • Powering lunar bases • In-space propellant transfer • Manufacturing in orbit and on the Moon • Deep-space AI infrastructure using massive solar collectors • Self-growing civilizations on the Moon, Mars, and beyond By tapping into space’s unlimited solar energy, this is how we get to a Kardashev-level civilization! So for SpaceX, the faster Starship progresses, there will be massive new revenue streams and will lead in building out space-based AI infrastructures. For xAI, AI scales without ruining Earth, deeply integrated with everything SpaceX is building, further understanding the universe. For us humans, AI will grow without destroying the planet, and there’s a real path to becoming multi-planetary. I believe SpaceX acquiring xAI is officially the blueprint for where AI, energy, and humanity are headed. Congratulations to everyone, this is such an exciting time to be alive!

Teslaconomics

148,575 просмотров • 6 месяцев назад

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks
26:35

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,826 просмотров • 1 месяц назад

Breaking the Barriers of Global Settlement The first cup of coffee paid for with $USDA on Cardano has been served in Kenya, settled in real-time, directly to this merchant’s M-Pesa wallet in Nairobi. This pilot confirms that the Cardano network can handle the speed and integration required for everyday retail. And that's not all. This demo is just one piece of a larger infrastructure. This wallet pilot already supports: > Instant Fiat-to-Fiat: Seamless transfers across Africa. > USDA-to-Fiat: Direct conversion for real-world spending at almost every retailer > Multi-Currency Support: Instant payments to 7 different fiat currencies. Using a credit card internationally would have cost over 14% in fees, plus 5 days in settlement for the Merchant. With USDA, fees were under 1% and settled instantly. Our goal is to bring this liquidity to emerging markets worldwide, removing the friction of traditional cross-border payments. The wallet is entering final stages of testing and will launch in the coming weeks.
1:42

Breaking the Barriers of Global Settlement The first cup of coffee paid for with $USDA on Cardano has been served in Kenya, settled in real-time, directly to this merchant’s M-Pesa wallet in Nairobi. This pilot confirms that the Cardano network can handle the speed and integration required for everyday retail. And that's not all. This demo is just one piece of a larger infrastructure. This wallet pilot already supports: > Instant Fiat-to-Fiat: Seamless transfers across Africa. > USDA-to-Fiat: Direct conversion for real-world spending at almost every retailer > Multi-Currency Support: Instant payments to 7 different fiat currencies. Using a credit card internationally would have cost over 14% in fees, plus 5 days in settlement for the Merchant. With USDA, fees were under 1% and settled instantly. Our goal is to bring this liquidity to emerging markets worldwide, removing the friction of traditional cross-border payments. The wallet is entering final stages of testing and will launch in the coming weeks.

Anzens Official

18,252 просмотров • 5 месяцев назад

I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the data used by large language models and search engines. By utilizing specialized techniques like PolarQuant and Quantized Johnson-Lindenstrauss, the system transforms complex information into a compact "shorthand" that requires significantly less memory. This innovation specifically addresses the key-value cache bottleneck, allowing AI models to process massive amounts of text faster without losing accuracy. Testing demonstrates that these methods can shrink data size by six times while actually increasing operational speed on high-end hardware. Ultimately, these theoretical advancements enable more efficient semantic search and high-performance AI applications at a global scale.
5:07

I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the data used by large language models and search engines. By utilizing specialized techniques like PolarQuant and Quantized Johnson-Lindenstrauss, the system transforms complex information into a compact "shorthand" that requires significantly less memory. This innovation specifically addresses the key-value cache bottleneck, allowing AI models to process massive amounts of text faster without losing accuracy. Testing demonstrates that these methods can shrink data size by six times while actually increasing operational speed on high-end hardware. Ultimately, these theoretical advancements enable more efficient semantic search and high-performance AI applications at a global scale.

Emily

14,648 просмотров • 4 месяцев назад

Even my kids know that in a world full of Tesla Robotaxis, Waymo is dunzo. It’s really not that complicated. Waymo needs expensive LiDAR, radar, cameras, specialized hardware, detailed maps, and a massive amount of infrastructure just to operate in certain areas. Tesla’s approach is completely different. Cameras are cheap. Tesla can mass-produce vehicles at a scale Waymo can’t come close to, use the same AI system across millions of cars, collect massive amounts of real-world driving data, and improve the entire fleet through software updates. That’s where the economics become almost impossible to compete with. The cheaper you can build the car, the cheaper you can operate it, and the more vehicles you can put on the road, the cheaper you can eventually make every ride. Waymo might prove Robotaxis can work. Tesla’s goal is to make them cheap enough and build enough of them to be everywhere. That’s the difference people still don’t understand. This isn’t just about who has the better Robotaxi today. It’s about who can build millions of them, drive down the cost per mile, and scale across the world. Even my kids understand the end game. Tesla Robotaxis are going to be everywhere. In the world where Tesla Robotaxis are flooded all around the world, it will very difficult for Waymo to survive.
0:28

Even my kids know that in a world full of Tesla Robotaxis, Waymo is dunzo. It’s really not that complicated. Waymo needs expensive LiDAR, radar, cameras, specialized hardware, detailed maps, and a massive amount of infrastructure just to operate in certain areas. Tesla’s approach is completely different. Cameras are cheap. Tesla can mass-produce vehicles at a scale Waymo can’t come close to, use the same AI system across millions of cars, collect massive amounts of real-world driving data, and improve the entire fleet through software updates. That’s where the economics become almost impossible to compete with. The cheaper you can build the car, the cheaper you can operate it, and the more vehicles you can put on the road, the cheaper you can eventually make every ride. Waymo might prove Robotaxis can work. Tesla’s goal is to make them cheap enough and build enough of them to be everywhere. That’s the difference people still don’t understand. This isn’t just about who has the better Robotaxi today. It’s about who can build millions of them, drive down the cost per mile, and scale across the world. Even my kids understand the end game. Tesla Robotaxis are going to be everywhere. In the world where Tesla Robotaxis are flooded all around the world, it will very difficult for Waymo to survive.

Teslaconomics

34,323 просмотров • 25 дней назад

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast
2:26

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

79,066 просмотров • 1 месяц назад

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.
0:21

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,022,693 просмотров • 10 месяцев назад

Honoured to participate in the CNBC-TV18 Global AI Lens Fireside Conversation at the #IndiaAIImpactSummit2026, following insightful remarks by H.E. Ebba Busch, Deputy Prime Minister of Sweden, where I reflected on how the India–Sweden partnership combines Sweden’s world-class innovation with India’s unparalleled scale as a real-world test bed for AI deployment. I emphasised that while the Union Government provides national frameworks and digital public infrastructure, the real momentum of AI adoption will be driven by states. Therefore, competitive and collaborative federalism must become the engine of implementation, and closer Centre–State coordination is essential to translate policy ambition into measurable outcomes for citizens. Tamil Nadu is leading this charge as an enabler by strengthening structured data systems, offering calibrated incentives, and deploying practical AI solutions in high-impact sectors such as health, agriculture, and governance. Inclusion is not optional; accordingly, we are expanding 100 Mbps fiber connectivity to every village and providing AI-enabled laptops to college students to ensure our youth are prepared for the future. To ensure true social equity, we must move beyond English and text-based interfaces toward voice-based and local language models that serve everyone, regardless of literacy. By balancing innovation with responsibility and equity, we are positioning India as a trusted and inclusive partner in the global AI ecosystem. Watch the video here : [English W\ Tamil CC]
11:34

Honoured to participate in the CNBC-TV18 Global AI Lens Fireside Conversation at the #IndiaAIImpactSummit2026, following insightful remarks by H.E. Ebba Busch, Deputy Prime Minister of Sweden, where I reflected on how the India–Sweden partnership combines Sweden’s world-class innovation with India’s unparalleled scale as a real-world test bed for AI deployment. I emphasised that while the Union Government provides national frameworks and digital public infrastructure, the real momentum of AI adoption will be driven by states. Therefore, competitive and collaborative federalism must become the engine of implementation, and closer Centre–State coordination is essential to translate policy ambition into measurable outcomes for citizens. Tamil Nadu is leading this charge as an enabler by strengthening structured data systems, offering calibrated incentives, and deploying practical AI solutions in high-impact sectors such as health, agriculture, and governance. Inclusion is not optional; accordingly, we are expanding 100 Mbps fiber connectivity to every village and providing AI-enabled laptops to college students to ensure our youth are prepared for the future. To ensure true social equity, we must move beyond English and text-based interfaces toward voice-based and local language models that serve everyone, regardless of literacy. By balancing innovation with responsibility and equity, we are positioning India as a trusted and inclusive partner in the global AI ecosystem. Watch the video here : [English W\ Tamil CC]

Dr P Thiaga Rajan (PTR)

22,196 просмотров • 5 месяцев назад