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Analyzing large vector datasets on Source Cooperative with #DuckDB 🦆Using the 75 GB National Wetlands Inventory as an example. It used to take me hours to get summary statistics of the dataset, now it takes seconds with DuckDB 🚀 Dataset: Colab: #geospatial #dataviz #opendata

Qiusheng Wu

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13,309 просмотров • 2 лет назад •via X (Twitter)

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Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.

Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.

Y Combinator

20,944 просмотров • 8 месяцев назад

📢📢𝐍𝐞𝐑𝐒𝐞𝐦𝐛𝐥𝐞 𝐯𝟐 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞📢📢 Head captures of 7.1MP from 16 cameras at 73fps: * More recordings (425 people) * Better color calibration * Convenient download scripts The new version of our dataset adds 156 participants for a total of 425 different people. In its entirety, the dataset provides now 65 million images from over 15 hours of diverse human facial expression performances. We improved the color consistency of the recorded images with a better color calibration procedure. As a result, 3D reconstructions with images from the NeRSemble dataset should become better and look more realistic. Finally, we made it much easier to download the recordings with our new download repository. It now just takes a single command to download all frontal hair shake videos of all participants or to download all recordings of a single participant. Check it out: Awesome work by Tobias Kirschstein Simon Giebenhain !!!

📢📢𝐍𝐞𝐑𝐒𝐞𝐦𝐛𝐥𝐞 𝐯𝟐 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞📢📢 Head captures of 7.1MP from 16 cameras at 73fps: * More recordings (425 people) * Better color calibration * Convenient download scripts The new version of our dataset adds 156 participants for a total of 425 different people. In its entirety, the dataset provides now 65 million images from over 15 hours of diverse human facial expression performances. We improved the color consistency of the recorded images with a better color calibration procedure. As a result, 3D reconstructions with images from the NeRSemble dataset should become better and look more realistic. Finally, we made it much easier to download the recordings with our new download repository. It now just takes a single command to download all frontal hair shake videos of all participants or to download all recordings of a single participant. Check it out: Awesome work by Tobias Kirschstein Simon Giebenhain !!!

Matthias Niessner

12,089 просмотров • 1 год назад

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

SandboxAQ

129,416 просмотров • 1 год назад

🇶🇦 Qatar Airways flight QR339 used to go Doha to Moscow in about 5 hours, cutting straight through Iraq and Turkey. Now with airspace closing, it takes around 7.5 hours and swings way east over the Caspian Sea to get there. That extra 2.5 hours burns about 20,000 kg more fuel, adding roughly $20,000 to $25,000 per flight for the airline. Open the flight map and it’s basically a ghost town now. Another example of how this war is quietly messing with the global economy. Source: Flightradar24

🇶🇦 Qatar Airways flight QR339 used to go Doha to Moscow in about 5 hours, cutting straight through Iraq and Turkey. Now with airspace closing, it takes around 7.5 hours and swings way east over the Caspian Sea to get there. That extra 2.5 hours burns about 20,000 kg more fuel, adding roughly $20,000 to $25,000 per flight for the airline. Open the flight map and it’s basically a ghost town now. Another example of how this war is quietly messing with the global economy. Source: Flightradar24

Mario Nawfal

272,997 просмотров • 4 месяцев назад

🚀 The #GeoAI Python package now supports feature segmentation from high-resolution satellite and aerial imagery using text prompts, such as trees, buildings, etc. It efficiently processes large datasets with automatic tiling and can save results as a single image. Stay tuned for more features coming soon! 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning

🚀 The #GeoAI Python package now supports feature segmentation from high-resolution satellite and aerial imagery using text prompts, such as trees, buildings, etc. It efficiently processes large datasets with automatic tiling and can save results as a single image. Stay tuned for more features coming soon! 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

13,923 просмотров • 1 год назад

A Portuguese man sits on a street in Portugal holding his national flag when a group of migrants approaches him. One of them grabs the flag and tries to take it, but someone with him quickly takes it back as tensions rise.

A Portuguese man sits on a street in Portugal holding his national flag when a group of migrants approaches him. One of them grabs the flag and tries to take it, but someone with him quickly takes it back as tensions rise.

Clown World ™ 🤡

415,875 просмотров • 4 месяцев назад

someone recently asked me what it would take to create an FPS with this level of skill expression today. we don't have the technology to do that anymore, we used to, but we destroyed it.

someone recently asked me what it would take to create an FPS with this level of skill expression today. we don't have the technology to do that anymore, we used to, but we destroyed it.

minesweeper

79,211 просмотров • 5 месяцев назад

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

32,085 просмотров • 1 год назад

PackUV: Packed Gaussian UV Maps for 4D Volumetric Video - PackUV — A new volumetric video representation that packs 3D Gaussian attributes into a sequence of UV atlases for efficient streaming and storage, making it readily compatible with existing video coding infrastructure. - PackUV-GS — An efficient method to fit PackUV directly from multiview videos using optical-flow-based keyframing and Gaussian labeling to handle large motions, disocclusions, and temporal consistency. - PackUV-2B — The largest multi-view 4D dataset with 2B frames, large motions, and disocclusions. It provides 360° coverage from 50+ synchronized cameras.

PackUV: Packed Gaussian UV Maps for 4D Volumetric Video - PackUV — A new volumetric video representation that packs 3D Gaussian attributes into a sequence of UV atlases for efficient streaming and storage, making it readily compatible with existing video coding infrastructure. - PackUV-GS — An efficient method to fit PackUV directly from multiview videos using optical-flow-based keyframing and Gaussian labeling to handle large motions, disocclusions, and temporal consistency. - PackUV-2B — The largest multi-view 4D dataset with 2B frames, large motions, and disocclusions. It provides 360° coverage from 50+ synchronized cameras.

MrNeRF

17,035 просмотров • 5 месяцев назад

50% Deposit Match is officially LIVE on SmackTok! Put $100 in? Get $150 to play with Put $200 in? Get $300 to play with You get the point. Now go take advantage of it 🔥

50% Deposit Match is officially LIVE on SmackTok! Put $100 in? Get $150 to play with Put $200 in? Get $300 to play with You get the point. Now go take advantage of it 🔥

SmackTok

89,433 просмотров • 1 год назад

A lot of hitters confuse being late with letting the ball get deep. They’re not the same thing. In this video I show it with a simple example. I toss the ball and reach way out in front of me to catch it. That’s what late looks like. The ball already beat you… now you’re chasing it with your hands. Then I toss another one and catch it slightly behind me. That’s being on time and letting the ball get deep. When you’re late, your body and barrel are reacting after the ball passes your window. You’re forced to reach, cut across it, or roll it over. When you’re on time, you can let the ball travel and still control it. Now the barrel can stay through the zone longer, and you can drive it with authority. Deep ≠ late. Good hitters aren’t reaching for the ball… they’re on time enough to let it come to them. ⚾️ Train with an expert. Not an influencer.

A lot of hitters confuse being late with letting the ball get deep. They’re not the same thing. In this video I show it with a simple example. I toss the ball and reach way out in front of me to catch it. That’s what late looks like. The ball already beat you… now you’re chasing it with your hands. Then I toss another one and catch it slightly behind me. That’s being on time and letting the ball get deep. When you’re late, your body and barrel are reacting after the ball passes your window. You’re forced to reach, cut across it, or roll it over. When you’re on time, you can let the ball travel and still control it. Now the barrel can stay through the zone longer, and you can drive it with authority. Deep ≠ late. Good hitters aren’t reaching for the ball… they’re on time enough to let it come to them. ⚾️ Train with an expert. Not an influencer.

Stance Doctor

25,605 просмотров • 4 месяцев назад

Since when did "Economy Class" become code for "I’ve forgotten how to be a human"? ✈️🚮 Personally, I make it a point to hand my trash to the crew during the actual collection or take it with me. It takes 2 seconds and shows a shred of respect for the cleaners who are working on a literal countdown clock.

Since when did "Economy Class" become code for "I’ve forgotten how to be a human"? ✈️🚮 Personally, I make it a point to hand my trash to the crew during the actual collection or take it with me. It takes 2 seconds and shows a shred of respect for the cleaners who are working on a literal countdown clock.

Fahad Naim

19,731 просмотров • 6 месяцев назад

🚀 Get FREE Access to Kling 2.1 on Dzine! We're celebrating 1st anniversary of Kling AI , with free access on Dzine. 🎉 Now's the perfect time to try Kling 2.1 with no cost. Experience it now 👉

🚀 Get FREE Access to Kling 2.1 on Dzine! We're celebrating 1st anniversary of Kling AI , with free access on Dzine. 🎉 Now's the perfect time to try Kling 2.1 with no cost. Experience it now 👉

Dzine

61,359 просмотров • 1 год назад

Facial Sculpting. Beards. Makeup. Saving Characters and More! Get ready to express yourself with an infinite amount of possibilities using one of the most robust character customizers in the industry, coming to Star Citizen in Alpha 3.23. Watch it Now:

Facial Sculpting. Beards. Makeup. Saving Characters and More! Get ready to express yourself with an infinite amount of possibilities using one of the most robust character customizers in the industry, coming to Star Citizen in Alpha 3.23. Watch it Now:

Star Citizen

101,926 просмотров • 2 лет назад

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

Brandon White

25,117 просмотров • 1 год назад

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.

Yohan

45,065 просмотров • 4 месяцев назад

EJ:but about the scene passed (they're kissed?? Tell me if im wrong) Nong Freen you should take us as an example F:should I? EJ:for in case you want to do it with... F:*coughing*😌 #EnjoyJune1stfanmeet #srchafreen

EJ:but about the scene passed (they're kissed?? Tell me if im wrong) Nong Freen you should take us as an example F:should I? EJ:for in case you want to do it with... F:coughing😌 #EnjoyJune1stfanmeet #srchafreen

Love Dino Ji 공룡 사랑~

72,766 просмотров • 7 месяцев назад

Gen-3 Alpha Image to Video now supports using an image as either the first or last frame of your video generation. This feature can be used on its own or combined with a text prompt for additional guidance. All examples below demonstrate using an image as the last frame. (1/5)

Gen-3 Alpha Image to Video now supports using an image as either the first or last frame of your video generation. This feature can be used on its own or combined with a text prompt for additional guidance. All examples below demonstrate using an image as the last frame. (1/5)

Runway

143,494 просмотров • 2 лет назад