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can AI write engaging news that people can trust? introducing ✨Data2Story: a data journalist agent. give it raw data, it generate a verifiable, multimodal article. 🔍verifiable: every claim is evidence-grounded, traces back to data, code, or a cited source. 🔮multimodal: the article is a generative UI — images, videos,...

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Kevin Lin

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33,083 просмотров • 2 месяцев назад •via X (Twitter)

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Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:
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Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

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11,997 просмотров • 6 месяцев назад

Demis Hassabis says the next frontier is AI self-improvement — and coding is where it starts, because the outputs are verifiable and synthetic data is limitless That's a compounding flywheel, and every major lab is betting on it "but raw capability without reliability is a liability"
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Demis Hassabis says the next frontier is AI self-improvement — and coding is where it starts, because the outputs are verifiable and synthetic data is limitless That's a compounding flywheel, and every major lab is betting on it "but raw capability without reliability is a liability"

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15,428 просмотров • 2 месяцев назад

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
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IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

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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.
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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.

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A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.
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A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

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12,083 просмотров • 6 месяцев назад

The trust layer for robot and machine data is here peaqOS Stream enables machines to sign, verify, and monetize their data across Web3 and beyond → A machine signs its data at source → Any data protocol or platform can plug in and monetize it For the first time, machine data can be trusted by third parties at scale
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The trust layer for robot and machine data is here peaqOS Stream enables machines to sign, verify, and monetize their data across Web3 and beyond → A machine signs its data at source → Any data protocol or platform can plug in and monetize it For the first time, machine data can be trusted by third parties at scale

peaq

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Gemini 3.1 Flash-Lite is the model to build always on multimodal AI Agents. I just built a memory agent with Google ADK that runs 24/7. Feed it text, images, audio, video or PDFs. It reads, connects and consolidates everything while you sleep. 100% Opensource code.
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Gemini 3.1 Flash-Lite is the model to build always on multimodal AI Agents. I just built a memory agent with Google ADK that runs 24/7. Feed it text, images, audio, video or PDFs. It reads, connects and consolidates everything while you sleep. 100% Opensource code.

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BREAKING: a new Chinese model is out. And no. This is NOT DeepSeek. Meet Qwen-2.5, from the giant Alibaba: 1. It can code, write text, search the web. 2. It can generate images, like Dall-E. 3. It can even generate videos. Here's everything you need to know:

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Full Stack Verifiability | Verifiable Cloud ☁️ Sreeram Kannan explains how EigenCloud delivers verifiability across the entire stack. It starts at the base with the EigenLayer, where computation is guaranteed by a decentralized network of hundreds of operators backed by $12B of stake. That’s the trust foundation of the cloud. On top of this: ✅ EigenDA: a data availability protocol that guarantees verifiable data publication.Publish data once, and anyone can retrieve it without trust. ✅ EigenCompute: execute arbitrary programs off-chain and bring the results back onchain verifiably. ✅ EigenAI: run AI inference today and return inference outputs directly onchain. All of them deliver strong verifiability for computation, inference, and data, all anchored by the underlying decentralized layer. And it doesn’t stop at first-party services, EigenCloud includes a growing ecosystem of third-party systems like zkTLS, proof-of-location, and decentralized prover networks, adding even deeper verification guarantees throughout the cloud. This is what a fully verifiable cloud looks like. A cloud you can check, not just trust.
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Full Stack Verifiability | Verifiable Cloud ☁️ Sreeram Kannan explains how EigenCloud delivers verifiability across the entire stack. It starts at the base with the EigenLayer, where computation is guaranteed by a decentralized network of hundreds of operators backed by $12B of stake. That’s the trust foundation of the cloud. On top of this: ✅ EigenDA: a data availability protocol that guarantees verifiable data publication.Publish data once, and anyone can retrieve it without trust. ✅ EigenCompute: execute arbitrary programs off-chain and bring the results back onchain verifiably. ✅ EigenAI: run AI inference today and return inference outputs directly onchain. All of them deliver strong verifiability for computation, inference, and data, all anchored by the underlying decentralized layer. And it doesn’t stop at first-party services, EigenCloud includes a growing ecosystem of third-party systems like zkTLS, proof-of-location, and decentralized prover networks, adding even deeper verification guarantees throughout the cloud. This is what a fully verifiable cloud looks like. A cloud you can check, not just trust.

EigenCloud

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I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.
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I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.

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In a world where fake news spreads faster than real data, having verifiable and transparent information is more crucial than ever. That’s why prediction markets are changing the game, offering accurate, community-driven insights you can trust. Built on the fully decentralized Truth Network, these markets ensure that every piece of information is transparent, verifiable, and tamper-proof. We’re not just improving predictions, we’re laying the foundation for trust in Web3.
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In a world where fake news spreads faster than real data, having verifiable and transparent information is more crucial than ever. That’s why prediction markets are changing the game, offering accurate, community-driven insights you can trust. Built on the fully decentralized Truth Network, these markets ensure that every piece of information is transparent, verifiable, and tamper-proof. We’re not just improving predictions, we’re laying the foundation for trust in Web3.

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Introducing OpenLedger Datanet: Data across domains is often fragmented, unreliable, and lacks clear attribution , making it difficult to use as a trusted foundation for AI. Datanet transforms these raw contributions into structured, transparent, and attributable networks that power Specialized AI with verifiable provenance and explainability. Read more:
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Introducing OpenLedger Datanet: Data across domains is often fragmented, unreliable, and lacks clear attribution , making it difficult to use as a trusted foundation for AI. Datanet transforms these raw contributions into structured, transparent, and attributable networks that power Specialized AI with verifiable provenance and explainability. Read more:

OpenLedger

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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

50,452 просмотров • 11 месяцев назад

This is THE moment of Physical AI! We are officially announcing Cosmos 3: Omnimodal World Models for Physical AI 🚀 - Cosmos 3 is an omnimodal world model: within a unified architecture, it can understand and generate language, images, video, audio, and actions. - It is not just a VLM, not just a video generator, not just an audio-visual generative model, and not just a physics simulator / world-action model. It can understand images and videos, generate images, videos, and audio, simulate future worlds, predict actions, and generate robot policies—enabling models to truly begin to “touch the world.” - Cosmos 3 is the #1 open-weight reasoner / T2I / I2V / robot policy across many benchmarks. Huge thanks to every teammate who fought side by side on this journey—from architecture, data, training, infra, serving, and evaluation to post-training. Every part of this project carries an incredible amount of hard work. This was my first time leading a project as Tech Lead, and I feel truly fortunate. The future of Physical AI needs models that can not only “see” and “describe” the world, but also “imagine,” “simulate,” and “act”—and eventually close the loop with the real world. I hope Cosmos 3 can become an important starting point for this direction, and I’m excited to push Physical AI into its next stage together with the open-source community. Welcome to the era of Physical AI. HuggingFace: Project Website: Code:
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This is THE moment of Physical AI! We are officially announcing Cosmos 3: Omnimodal World Models for Physical AI 🚀 - Cosmos 3 is an omnimodal world model: within a unified architecture, it can understand and generate language, images, video, audio, and actions. - It is not just a VLM, not just a video generator, not just an audio-visual generative model, and not just a physics simulator / world-action model. It can understand images and videos, generate images, videos, and audio, simulate future worlds, predict actions, and generate robot policies—enabling models to truly begin to “touch the world.” - Cosmos 3 is the #1 open-weight reasoner / T2I / I2V / robot policy across many benchmarks. Huge thanks to every teammate who fought side by side on this journey—from architecture, data, training, infra, serving, and evaluation to post-training. Every part of this project carries an incredible amount of hard work. This was my first time leading a project as Tech Lead, and I feel truly fortunate. The future of Physical AI needs models that can not only “see” and “describe” the world, but also “imagine,” “simulate,” and “act”—and eventually close the loop with the real world. I hope Cosmos 3 can become an important starting point for this direction, and I’m excited to push Physical AI into its next stage together with the open-source community. Welcome to the era of Physical AI. HuggingFace: Project Website: Code:

Max Zhaoshuo Li 李赵硕

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i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below
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i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

Cody Schneider

47,302 просмотров • 1 год назад

BREAKING: you can now build a real-time, interactive, multi-asset crypto bloomberg without writing a line of code you’ve probably seen others make the claim but they're using hardcoded data that’s STILL inaccurate this is real. it uses live onchain data & links to explorers so you can verify don't believe me? watch the demo
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BREAKING: you can now build a real-time, interactive, multi-asset crypto bloomberg without writing a line of code you’ve probably seen others make the claim but they're using hardcoded data that’s STILL inaccurate this is real. it uses live onchain data & links to explorers so you can verify don't believe me? watch the demo

ryan 🌊

25,271 просмотров • 4 месяцев назад

The era of manually analyzing data will come to an end. AI can now do a lot of this automatically. It's a huge time saver. I do data analysis for a living, and I'm a huge fan of writing Jupyter notebooks to do it all, but it's now hard to justify manually writing code that you can generate in a few seconds. I still check everything manually, but breaking down datasets into tables and charts is now 10x easier than it's ever been. Here is a video where I'm using Retool. I load a dataset and generate a few charts as quickly as I can think. The speed at which we can go from one idea to a working solution is astonishing.
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The era of manually analyzing data will come to an end. AI can now do a lot of this automatically. It's a huge time saver. I do data analysis for a living, and I'm a huge fan of writing Jupyter notebooks to do it all, but it's now hard to justify manually writing code that you can generate in a few seconds. I still check everything manually, but breaking down datasets into tables and charts is now 10x easier than it's ever been. Here is a video where I'm using Retool. I load a dataset and generate a few charts as quickly as I can think. The speed at which we can go from one idea to a working solution is astonishing.

Santiago

70,812 просмотров • 10 месяцев назад

Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️
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Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️

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57,699 просмотров • 1 год назад