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2nd Place - Pravah 🥈 Electrical grids are overwhelmed with data needed to manage complex operations. Pravah developed an agentic grid orchestration framework to intake multimodal data, analyze it, and coordinate response actions Mohak Mangal

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128,599 görüntüleme • 1 yıl önce •via X (Twitter)

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FASHN AI parsa Eyal Shechtman Aleph @manim_community Surya 6/ Pravah Agentic grid orchestration for electric utilities Chat with grid data Mohak Mangal
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FASHN AI parsa Eyal Shechtman Aleph @manim_community Surya 6/ Pravah Agentic grid orchestration for electric utilities Chat with grid data Mohak Mangal

Alex Reibman 🖇️

26,374 görüntüleme • 1 yıl önce

I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI
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I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI

Qiusheng Wu

63,597 görüntüleme • 5 ay önce

Every other chat I do now in cursor is to use an MCP to analyze my usage data. And it all started after I recorded this episode. This week on How I AI, Tim & Alexa at Faire show us how they analyze everything from experiment results to weird data anomalies with Cursor + various MCPs. We'll walk through - investigating a mysterious conversion rate drop across various data sources, including PRDs in Notion and code - programmatically creating Mode Analytics data visualizations via chat - defining agents to do repetitive tasks like writing eppo experiment reports After this ep, I realized I was massively underutilizing data MCPs and I hope you do, too. As always, a big thanks to our amazing sponsors! ⚡️ Zapier - the most connected AI orchestration platform (with a killer MCP I must add) 🤑 Brex - the intelligent finance platform built for founders
1:14

Every other chat I do now in cursor is to use an MCP to analyze my usage data. And it all started after I recorded this episode. This week on How I AI, Tim & Alexa at Faire show us how they analyze everything from experiment results to weird data anomalies with Cursor + various MCPs. We'll walk through - investigating a mysterious conversion rate drop across various data sources, including PRDs in Notion and code - programmatically creating Mode Analytics data visualizations via chat - defining agents to do repetitive tasks like writing eppo experiment reports After this ep, I realized I was massively underutilizing data MCPs and I hope you do, too. As always, a big thanks to our amazing sponsors! ⚡️ Zapier - the most connected AI orchestration platform (with a killer MCP I must add) 🤑 Brex - the intelligent finance platform built for founders

claire vo 🖤

22,942 görüntüleme • 11 ay önce

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning
1:11

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 görüntüleme • 1 yıl önce

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece
1:16

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

Victoria Slocum

45,497 görüntüleme • 1 yıl önce

Strategy CEO: How Do We Decide to Sell Bitcoin? Not an Impulsive Decision On June 19, 2026, Strategy CEO Phong Le Phong Le detailed the internal decision-making process behind selling 32 Bitcoins in an interview with Coinage. He stated that as a NASDAQ-listed company, their actions are never impulsive. The team maps out capital optionality quarterly, runs sophisticated financial models monthly to analyze equity and credit quality, and coordinates daily operations with Michael Saylor and the trading team. Furthermore, they use tools like Grok sentiment analysis on X to ensure every financial move is backed by rigorous data analytics.
2:21

Strategy CEO: How Do We Decide to Sell Bitcoin? Not an Impulsive Decision On June 19, 2026, Strategy CEO Phong Le Phong Le detailed the internal decision-making process behind selling 32 Bitcoins in an interview with Coinage. He stated that as a NASDAQ-listed company, their actions are never impulsive. The team maps out capital optionality quarterly, runs sophisticated financial models monthly to analyze equity and credit quality, and coordinates daily operations with Michael Saylor and the trading team. Furthermore, they use tools like Grok sentiment analysis on X to ensure every financial move is backed by rigorous data analytics.

Wu Blockchain

19,951 görüntüleme • 3 ay önce

This week, ML Scientist @dimid_ml shares an exciting update on the Theoriq Protocol. ⚡️ As MCP gains momentum in connecting AI models with external tools and data, our team has moved fast to integrate an MCP server directly into Theoriq’s SDK. This upgrade makes it easy for agents to register, manage, and update other agents on the protocol, laying the foundation for a more flexible and adaptable agentic ecosystem. More below.
4:30

This week, ML Scientist @dimid_ml shares an exciting update on the Theoriq Protocol. ⚡️ As MCP gains momentum in connecting AI models with external tools and data, our team has moved fast to integrate an MCP server directly into Theoriq’s SDK. This upgrade makes it easy for agents to register, manage, and update other agents on the protocol, laying the foundation for a more flexible and adaptable agentic ecosystem. More below.

Theoriq

82,463 görüntüleme • 1 yıl önce

Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.
1:13

Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.

AITECH CLOUD NETWORK

171,779 görüntüleme • 1 yıl önce

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.
0:31

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,491 görüntüleme • 1 yıl önce

We are announcing our newest initiative: an interactive math engine by isaacmason. Math is core to everything interactive from geometry to collision to color, yet JavaScript lacks a high-performance kernel. Instead libraries reinvent math structures and transformations, with varying success, and now LLMs generate bespoke functions on a case by case basis. The problem is that there are no principled guarantees for correctness or performance leading to degraded apps or difficult to read code. We need math engine that can be used to power all the cool projects people want to make at scale! The goals of the initiative are ambitious: ⟡ Predictable performance. Allocation-free operations and a documented usage contract designed to preserve monomorphic, optimizable call sites, validated through reproducible benchmarks. ⟡ Portable. Efficiently interoperates with WebGL, WebGPU, Wasm, Three.js, etc., so that the handoff between math kernel and framework is simple. ⟡ Minimal. A lean, tree-shakable kernel containing only the primitives needed to build interactive algorithms. ⟡ Data-oriented. Operates on caller-owned data through data-in, data-out functions without owning the data lifecycle. ⟡ Readability. It is important the core math operations readable such that even if the code is not written by a person they can reasonably review it. isaacmason is bringing his experience building out navcat, crashcat and gpucat. We are starting with mathcat as the foundation and building from there. Stay tuned for more. 👉 Initiative: 👉 Join the convo on Discord:
0:36

We are announcing our newest initiative: an interactive math engine by isaacmason. Math is core to everything interactive from geometry to collision to color, yet JavaScript lacks a high-performance kernel. Instead libraries reinvent math structures and transformations, with varying success, and now LLMs generate bespoke functions on a case by case basis. The problem is that there are no principled guarantees for correctness or performance leading to degraded apps or difficult to read code. We need math engine that can be used to power all the cool projects people want to make at scale! The goals of the initiative are ambitious: ⟡ Predictable performance. Allocation-free operations and a documented usage contract designed to preserve monomorphic, optimizable call sites, validated through reproducible benchmarks. ⟡ Portable. Efficiently interoperates with WebGL, WebGPU, Wasm, Three.js, etc., so that the handoff between math kernel and framework is simple. ⟡ Minimal. A lean, tree-shakable kernel containing only the primitives needed to build interactive algorithms. ⟡ Data-oriented. Operates on caller-owned data through data-in, data-out functions without owning the data lifecycle. ⟡ Readability. It is important the core math operations readable such that even if the code is not written by a person they can reasonably review it. isaacmason is bringing his experience building out navcat, crashcat and gpucat. We are starting with mathcat as the foundation and building from there. Stay tuned for more. 👉 Initiative: 👉 Join the convo on Discord:

Poimandres

56,745 görüntüleme • 2 ay önce

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.
0:30

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.

sachin

38,485 görüntüleme • 5 ay önce

Crypto markets are stronger in numbers. This week, 717ai Terminal upgrade will include: - Profile creation, social links, and UX journey👤 - Leaderboards & community database📊 - Engagement metrics and rewards framework🏆 - Live Social Feed📺 - Prompt Interactions🔁 - Search Capabilities🔍 - Community Alpha Notifications📳 ALL of these upgrades make up a unique SocialFi framework. Enabling other AI Agents & builders to benefit from an easy plug-and-play model. - User data aggregation📊 - Mempool database and support; - Rewards framework for other communities; - Potential ad/partnership revenue (banner notifications); - Response Feedback; ALL of these framework benefits were designed to improve 717ai’s language model and refine social capabilities as Alpha subagents are developed. All while providing a fun and engaging way of rewarding $WIRE holders! Stay tuned👀
0:57

Crypto markets are stronger in numbers. This week, 717ai Terminal upgrade will include: - Profile creation, social links, and UX journey👤 - Leaderboards & community database📊 - Engagement metrics and rewards framework🏆 - Live Social Feed📺 - Prompt Interactions🔁 - Search Capabilities🔍 - Community Alpha Notifications📳 ALL of these upgrades make up a unique SocialFi framework. Enabling other AI Agents & builders to benefit from an easy plug-and-play model. - User data aggregation📊 - Mempool database and support; - Rewards framework for other communities; - Potential ad/partnership revenue (banner notifications); - Response Feedback; ALL of these framework benefits were designed to improve 717ai’s language model and refine social capabilities as Alpha subagents are developed. All while providing a fun and engaging way of rewarding $WIRE holders! Stay tuned👀

717ai

20,709 görüntüleme • 1 yıl önce

For years, green groups have hailed electrification as a core climate solution. And now that we’re suddenly in the middle of historic load growth, they’re largely unprepared to meet the moment with big ideas. “We should all be embarrassed,” says Jane Flegal on this week's Open Circuit episode. For a long time, the climate community talked about aggressive electrification and the need to expand and modernize the grid. But now that future is arriving, the response has been fragmented or defensive. Some groups have leaned into affordability. Others are debating who should pay. And there are increasing calls for data center moratoriums. But there’s been very little thinking about how to plan and build at the scale this moment actually requires. "There's kind of like a rude awakening happening now, where everyone's like, ‘what was our plan to actually build?' she says. Jane has an idea. She proposes a new kind of grid infrastructure fund to bring hyperscalers, utilities, and policymakers into tighter coordination and to use demand as leverage to invest in the grid in a way that actually serves the public. Jigar Shah and I dug into the idea with her. We are mired in debates about grid-connected vs off-grid data centers, gas vs renewables, and who should pay for it all. But those debates are downstream of a bigger issue: we don’t have a system that can plan, coordinate, and build the grid for this era.
2:01

For years, green groups have hailed electrification as a core climate solution. And now that we’re suddenly in the middle of historic load growth, they’re largely unprepared to meet the moment with big ideas. “We should all be embarrassed,” says Jane Flegal on this week's Open Circuit episode. For a long time, the climate community talked about aggressive electrification and the need to expand and modernize the grid. But now that future is arriving, the response has been fragmented or defensive. Some groups have leaned into affordability. Others are debating who should pay. And there are increasing calls for data center moratoriums. But there’s been very little thinking about how to plan and build at the scale this moment actually requires. "There's kind of like a rude awakening happening now, where everyone's like, ‘what was our plan to actually build?' she says. Jane has an idea. She proposes a new kind of grid infrastructure fund to bring hyperscalers, utilities, and policymakers into tighter coordination and to use demand as leverage to invest in the grid in a way that actually serves the public. Jigar Shah and I dug into the idea with her. We are mired in debates about grid-connected vs off-grid data centers, gas vs renewables, and who should pay for it all. But those debates are downstream of a bigger issue: we don’t have a system that can plan, coordinate, and build the grid for this era.

Stephen Lacey

90,569 görüntüleme • 6 ay önce

The DoJ is using your tax dollars to sue any community that resists data centers. Clayton Morris on the AI takeover of America. 0:00 Clayton Morris’ Perspective on AI and Data Centers 4:26 Are Your Zoom Calls Being Recorded and Where Does the Data Go? 8:08 The AI Task Force Designed to Pummel Communities 14:29 How Much Water Does It Take to Cool a Data Center? 21:40 Why Build Data Centers in Areas Lacking Necessary Resources? 29:53 The Powerlessness of Communities Consumed by Data Centers 33:30 AI-China War, National Security, and Amazon Data Centers 41:27 Morris Debunks the AI Bubble Theory 47:34 The Upsides vs. Downsides of AI 54:26 Do We Serve the Machine or Does the Machine Serve Us? 58:20 How Anything With an Electrical Impulse Isn’t Under Your Control
1:01:04

The DoJ is using your tax dollars to sue any community that resists data centers. Clayton Morris on the AI takeover of America. 0:00 Clayton Morris’ Perspective on AI and Data Centers 4:26 Are Your Zoom Calls Being Recorded and Where Does the Data Go? 8:08 The AI Task Force Designed to Pummel Communities 14:29 How Much Water Does It Take to Cool a Data Center? 21:40 Why Build Data Centers in Areas Lacking Necessary Resources? 29:53 The Powerlessness of Communities Consumed by Data Centers 33:30 AI-China War, National Security, and Amazon Data Centers 41:27 Morris Debunks the AI Bubble Theory 47:34 The Upsides vs. Downsides of AI 54:26 Do We Serve the Machine or Does the Machine Serve Us? 58:20 How Anything With an Electrical Impulse Isn’t Under Your Control

Tucker Carlson

781,223 görüntüleme • 1 ay önce

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.
1:14

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

The Humanoid Hub

11,575 görüntüleme • 7 ay önce

The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.
1:03

The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.

Department of War CTO

14,523 görüntüleme • 6 ay önce

How to setup a multi agent system? Bookmark it 📂 "The Trading Floor" Multi-Agent Market Analysis Council to analyze a stock ticker Z.ai GLM-4.7 🤝 OpenCode Agent framework: CrewAI How it works? 1. User enters a stock ticker to analyze 2. 5 AI agents wake up, each with distinct expertise: - Quant Analyst — technical indicators & price patterns - Sentiment Scout — market mood & crowd psychology - Macro Strategist — sector dynamics & economic context - Risk Manager — volatility, drawdowns & position sizing - Portfolio Chief — synthesizes all perspectives 3. Agents analyze independently using real market data 4. They debate, challenge assumptions, and identify disagreements 5. Portfolio Chief resolves conflicts and delivers a consensus recommendation 6. Final output: buy/hold/sell rating with confidence level, position size, and key risks How to built The Trading Floor? 1. Chose CrewAI as the agent framework — handles multi-agent orchestration out of the box 2. Defined 5 agents with distinct roles, goals, and backstories in Python 3. Built custom tools wrapping yfinance for real market data (prices, indicators, volatility) 4. Configured sequential workflow — specialists analyze first, Portfolio Chief synthesizes last 5. Set up FastAPI backend with SSE to stream agent thoughts in real-time 6. Built Next.js frontend to visualize the "board of directors" deliberating live 7. One environment variable (MODEL=openai/gpt-5.2) powers all agents 8. Generated unique agent icons with AI image tools Total cost: $0 for the framework, pay only for LLM API calls Tech stack: - GLM-4.7 with opencode to build the app - CrewAI (open source) for agent orchestration - GPT-5.2 powering each agent - FastAPI + SSE for real-time streaming - Next.js frontend showing live agent deliberations
0:40

How to setup a multi agent system? Bookmark it 📂 "The Trading Floor" Multi-Agent Market Analysis Council to analyze a stock ticker Z.ai GLM-4.7 🤝 OpenCode Agent framework: CrewAI How it works? 1. User enters a stock ticker to analyze 2. 5 AI agents wake up, each with distinct expertise: - Quant Analyst — technical indicators & price patterns - Sentiment Scout — market mood & crowd psychology - Macro Strategist — sector dynamics & economic context - Risk Manager — volatility, drawdowns & position sizing - Portfolio Chief — synthesizes all perspectives 3. Agents analyze independently using real market data 4. They debate, challenge assumptions, and identify disagreements 5. Portfolio Chief resolves conflicts and delivers a consensus recommendation 6. Final output: buy/hold/sell rating with confidence level, position size, and key risks How to built The Trading Floor? 1. Chose CrewAI as the agent framework — handles multi-agent orchestration out of the box 2. Defined 5 agents with distinct roles, goals, and backstories in Python 3. Built custom tools wrapping yfinance for real market data (prices, indicators, volatility) 4. Configured sequential workflow — specialists analyze first, Portfolio Chief synthesizes last 5. Set up FastAPI backend with SSE to stream agent thoughts in real-time 6. Built Next.js frontend to visualize the "board of directors" deliberating live 7. One environment variable (MODEL=openai/gpt-5.2) powers all agents 8. Generated unique agent icons with AI image tools Total cost: $0 for the framework, pay only for LLM API calls Tech stack: - GLM-4.7 with opencode to build the app - CrewAI (open source) for agent orchestration - GPT-5.2 powering each agent - FastAPI + SSE for real-time streaming - Next.js frontend showing live agent deliberations

CloudAI-X

58,189 görüntüleme • 9 ay önce

Dr. Eric Schmidt says AI needs “energy in all forms: renewable, non-renewable, whatever.” “It needs to be there, and it needs to be quickly.” “People are planning 10 gigawatt data centers.” “An average nuclear power plant in the United States is one gigawatt.” “Data centers will require an additional 29 gigawatts of power by 2027 and 67 more gigawatts by 2030.” “These things are industrial at a scale I have never seen in my life.” This is exactly why I believe the market is still underestimating the AI power and infrastructure opportunity in companies like $TE, $BE, $PUMP, $PSIX, $SMR, $FCEL, $MARA, $KEEL, and $OKLO. The biggest constraint to scaling AI remains electricity. The companies building power generation, grid infrastructure, electrical equipment, cooling systems, and AI data centers could be among the biggest beneficiaries over the next decade. Not financial advice. Do your own research.
0:51

Dr. Eric Schmidt says AI needs “energy in all forms: renewable, non-renewable, whatever.” “It needs to be there, and it needs to be quickly.” “People are planning 10 gigawatt data centers.” “An average nuclear power plant in the United States is one gigawatt.” “Data centers will require an additional 29 gigawatts of power by 2027 and 67 more gigawatts by 2030.” “These things are industrial at a scale I have never seen in my life.” This is exactly why I believe the market is still underestimating the AI power and infrastructure opportunity in companies like $TE, $BE, $PUMP, $PSIX, $SMR, $FCEL, $MARA, $KEEL, and $OKLO. The biggest constraint to scaling AI remains electricity. The companies building power generation, grid infrastructure, electrical equipment, cooling systems, and AI data centers could be among the biggest beneficiaries over the next decade. Not financial advice. Do your own research.

Leopold Stock Tracker

20,643 görüntüleme • 3 ay önce

Zack Polanski, "Would you be prepared to say no to a data centre if it didn't secure clean water and energy supply?" Sadiq Khan, "I'm a bit concerned that he's right, he's raised the issue of water when it comes to cooling data centres.. I'm working with industry and the government to make progress here" My view: Zack Polanski raises an essential question: do we actually have the water and energy capacity for the AI data-centre boom being promised? Sadiq Khan acknowledges the pressure these centres can place on both. But Australia shows what real leadership can look like: making sufficient energy and water capacity a condition of large new AI data centres. Developers must secure additional clean power, fund their grid connections and upgrades, and use water efficiently. Victoria proposes going further still, requiring renewable power and storage, alongside recycled water for cooling. National legal standards are due in 2027.
1:08

Zack Polanski, "Would you be prepared to say no to a data centre if it didn't secure clean water and energy supply?" Sadiq Khan, "I'm a bit concerned that he's right, he's raised the issue of water when it comes to cooling data centres.. I'm working with industry and the government to make progress here" My view: Zack Polanski raises an essential question: do we actually have the water and energy capacity for the AI data-centre boom being promised? Sadiq Khan acknowledges the pressure these centres can place on both. But Australia shows what real leadership can look like: making sufficient energy and water capacity a condition of large new AI data centres. Developers must secure additional clean power, fund their grid connections and upgrades, and use water efficiently. Victoria proposes going further still, requiring renewable power and storage, alongside recycled water for cooling. National legal standards are due in 2027.

Farrukh

11,591 görüntüleme • 5 gün önce