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NVIDIA cuPhoton is now available on GitHub 🔭 Working with data from telescopes, X-ray sources and laser experiments often means spending months or years just getting the data ready to use. cuPhoton moves that pipeline onto the GPU to speed up that process, from ingest and cleaning to analysis,...

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The worst part of late-night experiments? The data analysis that comes after. OpenBio now automates your analysis - FTIR, spectroscopy data, and more. From data to publication-ready plots and insights in minutes. See it in action:
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The worst part of late-night experiments? The data analysis that comes after. OpenBio now automates your analysis - FTIR, spectroscopy data, and more. From data to publication-ready plots and insights in minutes. See it in action:

OpenBio

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🌌 20 Terabytes of data. That's what the Rubin Observatory captures every night to map dark matter — but traditional processing takes months. By leveraging CPU/GPU-accelerated pipelines on multi-GPU, multi-node systems, NVIDIA cuPhoton accelerates image loading by 15,000x and processing by 8,400x, turning months of waiting into minutes. #ISC26
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🌌 20 Terabytes of data. That's what the Rubin Observatory captures every night to map dark matter — but traditional processing takes months. By leveraging CPU/GPU-accelerated pipelines on multi-GPU, multi-node systems, NVIDIA cuPhoton accelerates image loading by 15,000x and processing by 8,400x, turning months of waiting into minutes. #ISC26

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Pentagon unveils AI system that helps plan strikes within minutes Palantir presented a platform that combines intelligence data from multiple sources into a single interface and speeds up target analysis. According to the developers, during the operation against Iran the system reduced the time from target detection to strike from hours to just minutes. It analyzes data from dozens of sources and suggests possible courses of action, while the final decision to launch a strike is still made by a human.
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Pentagon unveils AI system that helps plan strikes within minutes Palantir presented a platform that combines intelligence data from multiple sources into a single interface and speeds up target analysis. According to the developers, during the operation against Iran the system reduced the time from target detection to strike from hours to just minutes. It analyzes data from dozens of sources and suggests possible courses of action, while the final decision to launch a strike is still made by a human.

NEXTA

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Is your JSON data getting hung up with trailing commas or incorrect data types? Just ask GitHub Copilot Chat what’s wrong and how to fix it 🛠️ Learn more in the Copilot Chat Cookbook.
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Is your JSON data getting hung up with trailing commas or incorrect data types? Just ask GitHub Copilot Chat what’s wrong and how to fix it 🛠️ Learn more in the Copilot Chat Cookbook.

GitHub

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Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.
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Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

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Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!
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Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

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NVIDIA just introduced Cosmos, a platform for world foundation models designed for robotics. ⦿ It features advanced tokenizers, an AI-accelerated data pipeline, and integration with NVIDIA Omniverse. Humanoid makers 1X, Figure, and Agility are among the first to adopt Cosmos. ⦿ Cosmos generates synthetic, physics-based data, accelerating model training and customization. ⦿ It also features a CUDA-accelerated data processing pipeline that enables developers to process, curate, and label 20 million hours of videos in 14 days using the NVIDIA Blackwell platform.
8:02

NVIDIA just introduced Cosmos, a platform for world foundation models designed for robotics. ⦿ It features advanced tokenizers, an AI-accelerated data pipeline, and integration with NVIDIA Omniverse. Humanoid makers 1X, Figure, and Agility are among the first to adopt Cosmos. ⦿ Cosmos generates synthetic, physics-based data, accelerating model training and customization. ⦿ It also features a CUDA-accelerated data processing pipeline that enables developers to process, curate, and label 20 million hours of videos in 14 days using the NVIDIA Blackwell platform.

The Humanoid Hub

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YOUR AI AGENT CAN NOW COLLECT DATA FROM ALMOST ANY WEBSITE X, YouTube, Reddit, random websites, basically whatever you need for research, analytics or monitoring. Here are 3 GitHub tools that let your agent pull the data for you. 1. Agent-Reach Bundles tools for working with different platforms in one place. X, YouTube, Reddit, GitHub and more. 2. Patchright Enhanced Works through the browser with Playwright. You can listen to the requests a website makes and pull the data you need through a script. So if a website doesn’t have a proper API, you still have a way to get the data. 3. Scrapling A general purpose tool for pulling data from web pages. And the best part is that you can give all of this to your AI agent. For example: “Collect posts from 100 X accounts from the last month, find the most popular topics and analyze what’s working.” Or: “Collect reviews about this product from Reddit and YouTube and summarize the main complaints.” You tell the agent what data you need → it writes the code → collects the data → gives you the result. Basically, you’re giving your agent the ability to pull data from almost anywhere on the web. That opens up a whole different level of stuff you can automate.
0:41

YOUR AI AGENT CAN NOW COLLECT DATA FROM ALMOST ANY WEBSITE X, YouTube, Reddit, random websites, basically whatever you need for research, analytics or monitoring. Here are 3 GitHub tools that let your agent pull the data for you. 1. Agent-Reach Bundles tools for working with different platforms in one place. X, YouTube, Reddit, GitHub and more. 2. Patchright Enhanced Works through the browser with Playwright. You can listen to the requests a website makes and pull the data you need through a script. So if a website doesn’t have a proper API, you still have a way to get the data. 3. Scrapling A general purpose tool for pulling data from web pages. And the best part is that you can give all of this to your AI agent. For example: “Collect posts from 100 X accounts from the last month, find the most popular topics and analyze what’s working.” Or: “Collect reviews about this product from Reddit and YouTube and summarize the main complaints.” You tell the agent what data you need → it writes the code → collects the data → gives you the result. Basically, you’re giving your agent the ability to pull data from almost anywhere on the web. That opens up a whole different level of stuff you can automate.

TeddyCEO

199,519 görüntüleme • 13 gün önce

Our integration with Walrus is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇
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Our integration with Walrus is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇

Baselight

30,422 görüntüleme • 10 ay önce

How to automate data cleaning with AI. Today, I'll show you how to automate data cleaning using my free AI co-pilot available on GitHub. We'll create an AI co-pilot to automate data-cleaning tasks, focusing on cleaning a customer churn dataset. I'll guide you through setting up the co-pilot and using it effectively. This AI agent is a big help. Table of Contents: 00:00 Introduction 02:52 Setting Up Libraries 06:31 Setting Up LLMs 08:31 Creating Data Cleaner Code 11:02 Data Cleaning Process 13:02 Examining Data Cleaner Function 15:02 Final Thoughts Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter:
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How to automate data cleaning with AI. Today, I'll show you how to automate data cleaning using my free AI co-pilot available on GitHub. We'll create an AI co-pilot to automate data-cleaning tasks, focusing on cleaning a customer churn dataset. I'll guide you through setting up the co-pilot and using it effectively. This AI agent is a big help. Table of Contents: 00:00 Introduction 02:52 Setting Up Libraries 06:31 Setting Up LLMs 08:31 Creating Data Cleaner Code 11:02 Data Cleaning Process 13:02 Examining Data Cleaner Function 15:02 Final Thoughts Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter:

Matt Dancho (Business Science)

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Joseph Gordon Levitt: “The US government isn’t allowed to point it’s own cameras at the American people, but it is allowed to buy data from private companies who just happen to be collecting data on us and use that data to spy on us.”
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Joseph Gordon Levitt: “The US government isn’t allowed to point it’s own cameras at the American people, but it is allowed to buy data from private companies who just happen to be collecting data on us and use that data to spy on us.”

Thomas Sowell Quotes

22,145 görüntüleme • 5 ay önce

Actor Joseph Gordon-Levitt breaks down how the US government spies on US citizens through the Data Broker Loophole: “The US government isn’t allowed to point it’s own cameras at the American people, but it is allowed to buy data from private companies who just happen to be collecting data on us and use that data to spy on us”
1:30

Actor Joseph Gordon-Levitt breaks down how the US government spies on US citizens through the Data Broker Loophole: “The US government isn’t allowed to point it’s own cameras at the American people, but it is allowed to buy data from private companies who just happen to be collecting data on us and use that data to spy on us”

Marco Foster

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New Feature: Match Importer📥⚡️ Now you can bring lineups onto the board instantly using data from external providers! You can set the jersey colors for the GK and field players before starting, and if height or preferred foot data is available, it’ll be reflected too. *Match data usually becomes available 40 to 20 minutes before kickoff.
0:24

New Feature: Match Importer📥⚡️ Now you can bring lineups onto the board instantly using data from external providers! You can set the jersey colors for the GK and field players before starting, and if height or preferred foot data is available, it’ll be reflected too. *Match data usually becomes available 40 to 20 minutes before kickoff.

TACTICALista

90,099 görüntüleme • 3 ay önce

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.
1:36

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

Ricardo

641,147 görüntüleme • 6 ay önce

NASA’s science data is freely available for anyone, anywhere in the world to use. That means scientists and policy-makers, but it also means artists! Jewel (Jewel), a singer-songwriter and visual artist, used data from a @nasa ocean model in her latest artwork.
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NASA’s science data is freely available for anyone, anywhere in the world to use. That means scientists and policy-makers, but it also means artists! Jewel (Jewel), a singer-songwriter and visual artist, used data from a @nasa ocean model in her latest artwork.

NASA Earth

41,937 görüntüleme • 9 ay önce

Streamline early-stage workflows with new terrain analysis data layers. ⛰️ Use our new digital elevation model to visualize slope and elevation constraints directly on our 3D canvas. Whether you are trying to speed up site assessment or ensure site feasibility, you can now analyze terrain and validate reality remotely. This is just one of countless powerful data layers now available to help you make better decisions, faster.
0:29

Streamline early-stage workflows with new terrain analysis data layers. ⛰️ Use our new digital elevation model to visualize slope and elevation constraints directly on our 3D canvas. Whether you are trying to speed up site assessment or ensure site feasibility, you can now analyze terrain and validate reality remotely. This is just one of countless powerful data layers now available to help you make better decisions, faster.

Google Earth

14,002 görüntüleme • 8 ay önce

THIS IS F**KING DANGEROUS THESE OSINT TOOLS CAN DIG UP WAY MORE THAN YOU’D EXPECT I FOUND 3 TOOLS THAT SHOW JUST HOW MUCH INFORMATION CAN BE CONNECTED FROM PUBLIC DATA. MAIGRET searches 3,000+ sites to uncover accounts connected to a username or email. TRAPE can help analyze visitor and browser data during authorized security testing. Then BLACKBIRD uses AI-powered analysis to connect username data and generate deeper insights from the information it finds. The interesting part isn’t just finding the data. It’s what AI can do once all that data is connected. OSINT + AI IS A CRAZY COMBINATION. BOOKMARK this before someone take it down REPO’S BELOW
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THIS IS F**KING DANGEROUS THESE OSINT TOOLS CAN DIG UP WAY MORE THAN YOU’D EXPECT I FOUND 3 TOOLS THAT SHOW JUST HOW MUCH INFORMATION CAN BE CONNECTED FROM PUBLIC DATA. MAIGRET searches 3,000+ sites to uncover accounts connected to a username or email. TRAPE can help analyze visitor and browser data during authorized security testing. Then BLACKBIRD uses AI-powered analysis to connect username data and generate deeper insights from the information it finds. The interesting part isn’t just finding the data. It’s what AI can do once all that data is connected. OSINT + AI IS A CRAZY COMBINATION. BOOKMARK this before someone take it down REPO’S BELOW

MAX

16,728 görüntüleme • 2 gün önce