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Today, we’re open sourcing HASTE on GitHub, giving governments, humanitarian organizations, researchers, and emergency responders a powerful new tool to assess damage and accelerate disaster response when every minute matters. Built on Microsoft's trusted cloud and AI platform, including Azure Maps, Azure Batch, Azure Machine Learning, and Azure infrastructure,...

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Brad Smith

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10,203 просмотров • 1 месяц назад •via X (Twitter)

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We’re delighted to collaborate with Microsoft to launch an ontology‑driven #AgenticAI platform that accelerates #telecom and enterprise data modernization. Built on Microsoft Fabric and Azure AI Foundry, the platform enables explainable, auditable, and real‑time AI‑powered decision‑making—helping organizations move from #AI experimentation to scalable, production‑ready intelligence. Here's what Amol Phadke has to say about this strategic #partnership: #ScaleAtSpeed #EnterpriseAI #DataModernization #Microsoft #TechMahindra #MWCBarcelona
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We’re delighted to collaborate with Microsoft to launch an ontology‑driven #AgenticAI platform that accelerates #telecom and enterprise data modernization. Built on Microsoft Fabric and Azure AI Foundry, the platform enables explainable, auditable, and real‑time AI‑powered decision‑making—helping organizations move from #AI experimentation to scalable, production‑ready intelligence. Here's what Amol Phadke has to say about this strategic #partnership: #ScaleAtSpeed #EnterpriseAI #DataModernization #Microsoft #TechMahindra #MWCBarcelona

Tech Mahindra

44,045 просмотров • 6 месяцев назад

With Planet’s AI app, users can combine the power of AI with near-daily satellite data to explore what’s happening on Earth, uncover insights, and better understand change at scale. This is a glimpse into the future of Planetary Intelligence: transforming the world’s most comprehensive Earth data into actionable intelligence. Sign up today, in open beta now:
1:00

With Planet’s AI app, users can combine the power of AI with near-daily satellite data to explore what’s happening on Earth, uncover insights, and better understand change at scale. This is a glimpse into the future of Planetary Intelligence: transforming the world’s most comprehensive Earth data into actionable intelligence. Sign up today, in open beta now:

Planet

10,248 просмотров • 21 дней назад

✨ Introducing 3 new ways to build with real-world imagery and AI → From the fine details of a city street to a bird's eye view of the planet, at Cloud Next we revealed new ways that businesses can use our Street View, aerial and satellite imagery, and tap into our comprehensive view of the physical world including: 🏙️ Use Maps Imagery Grounding in Gemini Enterprise Agent Platform to generate location-specific assets at scale, anchored in real-world Street View imagery 🌍 Unlock a new perspective with Aerial and Satellite Insights, part of Earth AI, in BigQuery and Agent Platform to quickly extract detailed info about our changing planet 🛰️ Build custom apps with Aerial and Satellite models in Model Garden from Earth AI that easily identify specific objects or scenes Read the blog at the link above and sign up for early access.
0:20

✨ Introducing 3 new ways to build with real-world imagery and AI → From the fine details of a city street to a bird's eye view of the planet, at Cloud Next we revealed new ways that businesses can use our Street View, aerial and satellite imagery, and tap into our comprehensive view of the physical world including: 🏙️ Use Maps Imagery Grounding in Gemini Enterprise Agent Platform to generate location-specific assets at scale, anchored in real-world Street View imagery 🌍 Unlock a new perspective with Aerial and Satellite Insights, part of Earth AI, in BigQuery and Agent Platform to quickly extract detailed info about our changing planet 🛰️ Build custom apps with Aerial and Satellite models in Model Garden from Earth AI that easily identify specific objects or scenes Read the blog at the link above and sign up for early access.

Google Maps Platform

12,355 просмотров • 4 месяцев назад

Today, Mistral AI and Microsoft are expanding our partnership to bring Mistral’s frontier models across Azure, Microsoft Foundry, Copilot Studio, and Azure Local. Europe should have access to the world’s most capable AI without compromising control over their data, operations, or digital future. By bringing Mistral’s frontier European models into our environment, we are providing customers with a trusted foundation for AI, one that offers both leading-edge innovation and the freedom to operate on their own terms. Learn more:
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Today, Mistral AI and Microsoft are expanding our partnership to bring Mistral’s frontier models across Azure, Microsoft Foundry, Copilot Studio, and Azure Local. Europe should have access to the world’s most capable AI without compromising control over their data, operations, or digital future. By bringing Mistral’s frontier European models into our environment, we are providing customers with a trusted foundation for AI, one that offers both leading-edge innovation and the freedom to operate on their own terms. Learn more:

Brad Smith

316,921 просмотров • 1 месяц назад

Get AI-powered insights, straight from your Node. Your nodes are being upgraded with built-in AI—helping you decode news, crypto & market trends in seconds. Powered by Nodepay’s real-time data retrieval infrastructure, this tool transforms live web content into clear, actionable insights. But here’s the real alpha… It’s just the beginning of a new intelligence layer—where bandwidth, data, and insights converge 🤫
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Get AI-powered insights, straight from your Node. Your nodes are being upgraded with built-in AI—helping you decode news, crypto & market trends in seconds. Powered by Nodepay’s real-time data retrieval infrastructure, this tool transforms live web content into clear, actionable insights. But here’s the real alpha… It’s just the beginning of a new intelligence layer—where bandwidth, data, and insights converge 🤫

Nodepay

91,406 просмотров • 1 год назад

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp
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Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp

Scale Network

24,488 просмотров • 14 дней назад

Every autonomous system depends on one thing above anything else – data. The challenge is that most organizations struggle with data that is static, siloed and too slow to shape real decisions. That gap is where the hardest problems live. At Vantor, you work at the center of a software ecosystem built to fix those problems. Tensorglobe and our broader software suite sit on top of a living flow of satellite tasking decisions made every day – where to collect, when to collect, and how to turn that stream into smarter, connected and actionable spatial intelligence. Peter Wilczynski, our Chief Product Officer, explains why this matters. When you build on top of an internal platform that already moves at the speed of mission needs, you get a true sandbox for modern machine learning and autonomous system development. You test, iterate and deploy with an organization that uses its own technology at scale, then deliver those capabilities to customers who rely on fast, trusted decisions in defense, enterprise and humanitarian contexts. If you want to work on problems that were previously unsolvable, this is your place. We’re hiring. Join us. 🔗
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Every autonomous system depends on one thing above anything else – data. The challenge is that most organizations struggle with data that is static, siloed and too slow to shape real decisions. That gap is where the hardest problems live. At Vantor, you work at the center of a software ecosystem built to fix those problems. Tensorglobe and our broader software suite sit on top of a living flow of satellite tasking decisions made every day – where to collect, when to collect, and how to turn that stream into smarter, connected and actionable spatial intelligence. Peter Wilczynski, our Chief Product Officer, explains why this matters. When you build on top of an internal platform that already moves at the speed of mission needs, you get a true sandbox for modern machine learning and autonomous system development. You test, iterate and deploy with an organization that uses its own technology at scale, then deliver those capabilities to customers who rely on fast, trusted decisions in defense, enterprise and humanitarian contexts. If you want to work on problems that were previously unsolvable, this is your place. We’re hiring. Join us. 🔗

Vantor

10,955 просмотров • 9 месяцев назад

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.
0:24

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Akshay 🚀

280,679 просмотров • 1 год назад

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️
0:51

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

NVIDIA AI Infrastructure

23,393 просмотров • 14 дней назад

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre
1:45

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,075 просмотров • 2 месяцев назад

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly
0:30

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

Adaptyv Bio

14,864 просмотров • 2 месяцев назад

Google just acquired an Israeli Trojan horse to STEAL clients from AWS and Microsoft. $32 billion. All cash. For a cybersecurity startup called Wiz. It’s the largest acquisition in Google’s history. But nobody’s talking about what they ACTUALLY bought... Here’s why this is way bigger than you think: Wiz protects over half the Fortune 100. Their clients run on AWS, Azure, Google Cloud, and Oracle. The platform scans every workload, every vulnerability, every misconfiguration across ALL of those clouds. Meaning Wiz has a god-level view of how the world's biggest companies use their competitors' infrastructure. And Google just bought that view for $32 billion. Now think about what Google Cloud's biggest problem has been for YEARS... They're stuck in third place. 13% market share. AWS has 30%. Azure has 20%. Google has been hemorrhaging money trying to close that gap and nothing has worked. Wiz changes that equation overnight. Because Wiz doesn't just protect cloud environments. It MAPS them. It knows which companies are running what workloads, where their vulnerabilities are, and where they're overpaying. Google now has a real-time blueprint of its competitors' biggest customers. And it gets crazier: The 4 founders of Wiz previously built Adallom, a cloud security startup that Microsoft acquired for $320 million in 2015. After that acquisition, those same founders ran Microsoft's entire Azure Cloud Security Group. They literally built the security infrastructure that Azure runs on today. Then they left. Started Wiz. Built a product that works across every cloud. Got 45% of the Fortune 100 as customers. Most of those customers are on AWS and Azure. And now they just handed ALL of that to Google. Google promised Wiz will remain "multi-cloud" and continue working with AWS, Azure, and Oracle. That's the public story. But here's the game theory every enterprise CTO is thinking about right now: If you're running sensitive workloads on AWS or Azure and your security layer is now owned by your competitor, how comfortable are you? Google doesn't need to do anything shady. The PERCEPTION alone is enough to start shifting enterprise decisions. And that's worth way more than $32 billion. But there's another layer... Wiz went from $0 to $100 million in revenue in 18 months. Fastest software company in history to hit that mark. By 2025, they were at $750 million. The founders said no to Google's first offer of $23 billion in 2024 because they wanted to IPO. 9 months later, they said yes to $32 billion. What changed? The IPO market collapsed. Tech IPOs dried up. Valuations got slashed. Wiz's leadership looked at the math and realized $32 billion in guaranteed cash beats an uncertain public offering in a hostile market. Google paid a 39% premium over the rejected offer. A multiple of 45-65x revenue. For a company founded 5 years ago by 4 guys who met in Israeli military intelligence. This is the BIGGEST tech acquisition of an Israeli-founded company ever. Bigger than Intel buying Mobileye for $15.3 billion. Bigger than anything in Israeli tech history. And Google is betting it will be worth every penny. Because the cloud war isn't about compute anymore. It's about TRUST. And the company that controls the security layer controls where enterprises put their most sensitive data. Google just bought the keys to every major cloud customer on the planet. The question is whether AWS and Microsoft let them keep using those keys. What do you think?
1:06

Google just acquired an Israeli Trojan horse to STEAL clients from AWS and Microsoft. $32 billion. All cash. For a cybersecurity startup called Wiz. It’s the largest acquisition in Google’s history. But nobody’s talking about what they ACTUALLY bought... Here’s why this is way bigger than you think: Wiz protects over half the Fortune 100. Their clients run on AWS, Azure, Google Cloud, and Oracle. The platform scans every workload, every vulnerability, every misconfiguration across ALL of those clouds. Meaning Wiz has a god-level view of how the world's biggest companies use their competitors' infrastructure. And Google just bought that view for $32 billion. Now think about what Google Cloud's biggest problem has been for YEARS... They're stuck in third place. 13% market share. AWS has 30%. Azure has 20%. Google has been hemorrhaging money trying to close that gap and nothing has worked. Wiz changes that equation overnight. Because Wiz doesn't just protect cloud environments. It MAPS them. It knows which companies are running what workloads, where their vulnerabilities are, and where they're overpaying. Google now has a real-time blueprint of its competitors' biggest customers. And it gets crazier: The 4 founders of Wiz previously built Adallom, a cloud security startup that Microsoft acquired for $320 million in 2015. After that acquisition, those same founders ran Microsoft's entire Azure Cloud Security Group. They literally built the security infrastructure that Azure runs on today. Then they left. Started Wiz. Built a product that works across every cloud. Got 45% of the Fortune 100 as customers. Most of those customers are on AWS and Azure. And now they just handed ALL of that to Google. Google promised Wiz will remain "multi-cloud" and continue working with AWS, Azure, and Oracle. That's the public story. But here's the game theory every enterprise CTO is thinking about right now: If you're running sensitive workloads on AWS or Azure and your security layer is now owned by your competitor, how comfortable are you? Google doesn't need to do anything shady. The PERCEPTION alone is enough to start shifting enterprise decisions. And that's worth way more than $32 billion. But there's another layer... Wiz went from $0 to $100 million in revenue in 18 months. Fastest software company in history to hit that mark. By 2025, they were at $750 million. The founders said no to Google's first offer of $23 billion in 2024 because they wanted to IPO. 9 months later, they said yes to $32 billion. What changed? The IPO market collapsed. Tech IPOs dried up. Valuations got slashed. Wiz's leadership looked at the math and realized $32 billion in guaranteed cash beats an uncertain public offering in a hostile market. Google paid a 39% premium over the rejected offer. A multiple of 45-65x revenue. For a company founded 5 years ago by 4 guys who met in Israeli military intelligence. This is the BIGGEST tech acquisition of an Israeli-founded company ever. Bigger than Intel buying Mobileye for $15.3 billion. Bigger than anything in Israeli tech history. And Google is betting it will be worth every penny. Because the cloud war isn't about compute anymore. It's about TRUST. And the company that controls the security layer controls where enterprises put their most sensitive data. Google just bought the keys to every major cloud customer on the planet. The question is whether AWS and Microsoft let them keep using those keys. What do you think?

Ricardo

188,960 просмотров • 5 месяцев назад

Fireworks AI is now available first-party in Microsoft Foundry, as part of a multi-year partnership, starting today in private preview. Enterprise AI has a speed problem. Most infrastructure can't keep up with how fast open models are moving. New architectures, fine-tuned weights, models that aren't on anyone's approved list yet. The usual answer was "manage your own serving infrastructure." That's a real cost. Fireworks runs the inference. Azure owns the governance. Your weights stay yours. Beyond custom weights, teams can also access the latest open models on day zero. No waiting for the marketplace to catch up. Serverless inference for prototyping, and a path to production that doesn't require re-architecting anything. Fireworks handles the serving layer. Azure handles the rest. Your boss handles the congratulations. Want in →
0:23

Fireworks AI is now available first-party in Microsoft Foundry, as part of a multi-year partnership, starting today in private preview. Enterprise AI has a speed problem. Most infrastructure can't keep up with how fast open models are moving. New architectures, fine-tuned weights, models that aren't on anyone's approved list yet. The usual answer was "manage your own serving infrastructure." That's a real cost. Fireworks runs the inference. Azure owns the governance. Your weights stay yours. Beyond custom weights, teams can also access the latest open models on day zero. No waiting for the marketplace to catch up. Serverless inference for prototyping, and a path to production that doesn't require re-architecting anything. Fireworks handles the serving layer. Azure handles the rest. Your boss handles the congratulations. Want in →

Fireworks AI

51,657 просмотров • 5 месяцев назад

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇
0:52

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

FX1

23,913 просмотров • 1 год назад

📢We’re excited to share that we’ve raised $100M in seed funding to support LMArena and continue our research on reliable AI. Led by a16z and UC Investments (University of California), we're proud to have the support of those that believe in both the science and the mission. We’re focused on building a neutral, open, community-driven platform that helps the world understand and improve the performance of AI models on real queries from real users. Also, big news is coming next week!👀 We're relaunching LMArena with a whole new look built directly with community feedback from the ground up 🧱 Link in thread.
1:11

📢We’re excited to share that we’ve raised $100M in seed funding to support LMArena and continue our research on reliable AI. Led by a16z and UC Investments (University of California), we're proud to have the support of those that believe in both the science and the mission. We’re focused on building a neutral, open, community-driven platform that helps the world understand and improve the performance of AI models on real queries from real users. Also, big news is coming next week!👀 We're relaunching LMArena with a whole new look built directly with community feedback from the ground up 🧱 Link in thread.

Arena.ai

436,189 просмотров • 1 год назад

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.
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MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.

NO1ennn

68,276 просмотров • 15 дней назад

Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:
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Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:

Vantor

17,050 просмотров • 6 месяцев назад

🎯 AI Agents for Microsoft Planetary Computer: Explore Satellite Data with Zero Code Unlock satellite data without writing a single line of code! 🌍 In this step-by-step tutorial, you’ll discover how to harness the power of AI agents to search, filter, and visualize geospatial datasets from the Microsoft Planetary Computer — all using natural language. Perfect for researchers, developers, and environmental professionals who want fast, intuitive, code-free access to massive Earth observation datasets like Sentinel-2, Landsat, NAIP, and DEM. Learn how to: ✅ Use zero-code AI agents in Jupyter Notebooks ✅ Search satellite imagery by date, location, or cloud cover ✅ Visualize data instantly using Leafmap ✅ Integrate STAC data catalogs into your GeoAI workflow Full video tutorial available on YouTube: #geospatial #GeoAI #opensource #PlanetaryComputer
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🎯 AI Agents for Microsoft Planetary Computer: Explore Satellite Data with Zero Code Unlock satellite data without writing a single line of code! 🌍 In this step-by-step tutorial, you’ll discover how to harness the power of AI agents to search, filter, and visualize geospatial datasets from the Microsoft Planetary Computer — all using natural language. Perfect for researchers, developers, and environmental professionals who want fast, intuitive, code-free access to massive Earth observation datasets like Sentinel-2, Landsat, NAIP, and DEM. Learn how to: ✅ Use zero-code AI agents in Jupyter Notebooks ✅ Search satellite imagery by date, location, or cloud cover ✅ Visualize data instantly using Leafmap ✅ Integrate STAC data catalogs into your GeoAI workflow Full video tutorial available on YouTube: #geospatial #GeoAI #opensource #PlanetaryComputer

Qiusheng Wu

10,346 просмотров • 10 месяцев назад