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Training world models needs egocentric video and dense action signals, synchronized. That data is genuinely hard to find. We built it from Counter-Strike 2 demos. CS2-10k: 600K+ player-round videos, 10K+ hours, per-frame annotations — keyboard state, mouse delta, 3D position, camera yaw/pitch. All paired to the visual stream. Why...

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92,118 görüntüleme • 19 gün önce •via X (Twitter)

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We tried to record a normal AI launch video for this. And then we realized… The best way to showcase Moby 2 is just to let you have it. Because I genuinely think it’s the best product we’ve ever built at Triple Whale. It’s a DTC operator’s dream — a proactive agent that works for you. It can: - Monitor your ads and improve them - Launch and optimize campaigns - Run your Klaviyo flows - Generate creatives (images + video) The whole reason we founded Triple Whale was simple: 1. Get all your data in one place 2. Give you clarity 3. Help you automate and scale Moby 2 is the first time it really feels like we got there. Next week, we’ll onboard the first cohort of users and then expand as quickly as we can. But I cannot wait to get it in your hands. Request early access now →
1:15

We tried to record a normal AI launch video for this. And then we realized… The best way to showcase Moby 2 is just to let you have it. Because I genuinely think it’s the best product we’ve ever built at Triple Whale. It’s a DTC operator’s dream — a proactive agent that works for you. It can: - Monitor your ads and improve them - Launch and optimize campaigns - Run your Klaviyo flows - Generate creatives (images + video) The whole reason we founded Triple Whale was simple: 1. Get all your data in one place 2. Give you clarity 3. Help you automate and scale Moby 2 is the first time it really feels like we got there. Next week, we’ll onboard the first cohort of users and then expand as quickly as we can. But I cannot wait to get it in your hands. Request early access now →

AJ Orbach 🐳

168,727 görüntüleme • 4 ay önce

Hollywood isn't dead. It's evolving. And we're leading that evolution. We just shipped Koyal v2.5: The best Agentic AI filmmaking platform. It goes from your script or music to full video with consistent characters, settings & storylines. Large Production houses, music labels & ad agencies use koyal.ai (YC F25) for storyboarding & pre-viz. Smaller studios use it for making content they never had the time, budget or resources to create. Write a scene. Direct the camera. Build a world. koyal.ai (YC F25) brings it to life. Here's what's new: - Go from script to video: Write a scene with dialogue, pacing, emotion. Koyal fully realizes it. Using the best voice models on the planet. - Direct the camera: Push in. Pull back. Drift through a scene. Real 3D camera blocking. Shape the shot the way you see it in your head. - Build a world that stays:Lock in a location and return to it. Your scenes stay consistent from the first frame to the last. Plus: dialogue, SFX, annotations for editing, and more control over every shot. Under the hood, we benchmark 40+ models weekly with real humans so Koyal always uses the best available (more on that next week!) Our agents pick the best models for each scene in run-time so you focus on the story, we handle the complexity. You don't need to prompt a film, you can direct it. With Koyal, we're looking to replace the camera, not the filmmaker behind it. Try it now at beta [dot] koyal [dot] ai DM me for credits!
3:23

Hollywood isn't dead. It's evolving. And we're leading that evolution. We just shipped Koyal v2.5: The best Agentic AI filmmaking platform. It goes from your script or music to full video with consistent characters, settings & storylines. Large Production houses, music labels & ad agencies use koyal.ai (YC F25) for storyboarding & pre-viz. Smaller studios use it for making content they never had the time, budget or resources to create. Write a scene. Direct the camera. Build a world. koyal.ai (YC F25) brings it to life. Here's what's new: - Go from script to video: Write a scene with dialogue, pacing, emotion. Koyal fully realizes it. Using the best voice models on the planet. - Direct the camera: Push in. Pull back. Drift through a scene. Real 3D camera blocking. Shape the shot the way you see it in your head. - Build a world that stays:Lock in a location and return to it. Your scenes stay consistent from the first frame to the last. Plus: dialogue, SFX, annotations for editing, and more control over every shot. Under the hood, we benchmark 40+ models weekly with real humans so Koyal always uses the best available (more on that next week!) Our agents pick the best models for each scene in run-time so you focus on the story, we handle the complexity. You don't need to prompt a film, you can direct it. With Koyal, we're looking to replace the camera, not the filmmaker behind it. Try it now at beta [dot] koyal [dot] ai DM me for credits!

Mehul Agarwal

73,853 görüntüleme • 3 ay önce

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!
0:51

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 görüntüleme • 1 yıl önce

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.
0:24

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.

tut™

32,236 görüntüleme • 2 ay önce

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.
0:45

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

Grass

471,103 görüntüleme • 1 yıl önce

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.
0:22

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Fireside Alpha

10,037 görüntüleme • 9 gün önce

Elon Musk reduced the oldest question in human history to basic math. No one has found a flaw in it. Musk: “What are the odds that we are in base reality? And that this has not happened before.” You don’t need a physics degree to follow it. You need a timeline. Musk: “If you look at the advancement of video games, it’s gone from Pong, two rectangles and a square batting it back and forth, to photorealistic, real-time games with millions of people playing simultaneously.” Fifty years. That is all it took to close the gap between two rectangles on a screen and a world you cannot tell apart from the one outside your window. Musk: “If that trend continues, video games will be indistinguishable from reality.” The visuals are not what seals it. The intelligence is. Musk: “Think of how sophisticated the conversations are you can have with an AI today, and that’s only going to get more sophisticated.” We are not scripting characters anymore. We are building minds that reason, adapt, and surprise the people who made them. We are nowhere near finished. Musk: “The future, if civilization continues, will be millions, maybe billions of photorealistic, indistinguishable from reality, video games. And with characters in those video games that are very deep, and where the dialogue is not pre-programmed.” One base reality. Billions of perfect copies. Each one running minds that feel exactly as conscious as you do right now. Each one certain it is the original. Musk: “So then what are the odds that we are in base reality?” If even one civilization crosses that threshold, simulated minds outnumber real ones by billions. The probability you are sitting in the real one is not low. It is nearly zero. Not as philosophy. As mathematics. We are not watching this happen. We are building it. Right now. Every AI that reasons without a script. Every world rendered one frame closer to indistinguishable. We are constructing the exact technology that makes our own existence statistically implausible. And we will never stop. Because the curiosity that questions reality is the same force that builds it. If the math holds, something built us. Something conscious enough to create consciousness. They stood where we are standing. Same question. Same inability to stop. And whatever built them never answered it either. There is no top floor. There is no original. None of that changes what you feel right now. Consciousness was never about what you are made of. It was about what you experience. Musk did not float a theory. He held up a mirror with no back wall. And the math does not need you to believe it. It only needs time.
1:55

Elon Musk reduced the oldest question in human history to basic math. No one has found a flaw in it. Musk: “What are the odds that we are in base reality? And that this has not happened before.” You don’t need a physics degree to follow it. You need a timeline. Musk: “If you look at the advancement of video games, it’s gone from Pong, two rectangles and a square batting it back and forth, to photorealistic, real-time games with millions of people playing simultaneously.” Fifty years. That is all it took to close the gap between two rectangles on a screen and a world you cannot tell apart from the one outside your window. Musk: “If that trend continues, video games will be indistinguishable from reality.” The visuals are not what seals it. The intelligence is. Musk: “Think of how sophisticated the conversations are you can have with an AI today, and that’s only going to get more sophisticated.” We are not scripting characters anymore. We are building minds that reason, adapt, and surprise the people who made them. We are nowhere near finished. Musk: “The future, if civilization continues, will be millions, maybe billions of photorealistic, indistinguishable from reality, video games. And with characters in those video games that are very deep, and where the dialogue is not pre-programmed.” One base reality. Billions of perfect copies. Each one running minds that feel exactly as conscious as you do right now. Each one certain it is the original. Musk: “So then what are the odds that we are in base reality?” If even one civilization crosses that threshold, simulated minds outnumber real ones by billions. The probability you are sitting in the real one is not low. It is nearly zero. Not as philosophy. As mathematics. We are not watching this happen. We are building it. Right now. Every AI that reasons without a script. Every world rendered one frame closer to indistinguishable. We are constructing the exact technology that makes our own existence statistically implausible. And we will never stop. Because the curiosity that questions reality is the same force that builds it. If the math holds, something built us. Something conscious enough to create consciousness. They stood where we are standing. Same question. Same inability to stop. And whatever built them never answered it either. There is no top floor. There is no original. None of that changes what you feel right now. Consciousness was never about what you are made of. It was about what you experience. Musk did not float a theory. He held up a mirror with no back wall. And the math does not need you to believe it. It only needs time.

Dustin

156,290 görüntüleme • 22 saat önce

Domain Randomization (DR) is a key component of the data augmentation pipeline at Axis Robotics. By applying DR, we are able to scale verified, high-quality human trajectories by 10x to 100x. During training, we systematically introduce variances in environmental parameters. This prevents the model from relying on spurious visual correlations. The objective is to ensure the policy learns rather than overfitting. To demonstrate the necessity and effectiveness of this approach, we evaluated both DR and No-DR models on Task 74 (pour_water_into_mug). The empirical results show a definitive impact on real-world deployment reliability: integrating DR into the pipeline increased the success rate from 0% to 90% (Fig. 1). This divergence stems from how the respective policies process visual observations (Fig. 2). The baseline (No DR) model overfits to the static visual background. It essentially memorizes the poses from the training dataset but fails to generalize when subjected to the inevitable variances of real-world deployment. Consequently, it cannot execute the correct manipulation on the target object. Conversely, the DR-trained model learns to extract essential geometric features and physical constraints, filtering out superficial visual noise. This leads to significantly higher robustness in dynamic environments. The structural difference in execution is clearly reflected in the end-effector trajectory data: These real-world deployment recordings further illustrate this difference (Videos 1 and 2). Scaling Physical AI requires turning raw trajectory data into robust policies, and a rigorously engineered DR infrastructure is an essential bridge to close the Sim2Real gap.
0:20

Domain Randomization (DR) is a key component of the data augmentation pipeline at Axis Robotics. By applying DR, we are able to scale verified, high-quality human trajectories by 10x to 100x. During training, we systematically introduce variances in environmental parameters. This prevents the model from relying on spurious visual correlations. The objective is to ensure the policy learns rather than overfitting. To demonstrate the necessity and effectiveness of this approach, we evaluated both DR and No-DR models on Task 74 (pour_water_into_mug). The empirical results show a definitive impact on real-world deployment reliability: integrating DR into the pipeline increased the success rate from 0% to 90% (Fig. 1). This divergence stems from how the respective policies process visual observations (Fig. 2). The baseline (No DR) model overfits to the static visual background. It essentially memorizes the poses from the training dataset but fails to generalize when subjected to the inevitable variances of real-world deployment. Consequently, it cannot execute the correct manipulation on the target object. Conversely, the DR-trained model learns to extract essential geometric features and physical constraints, filtering out superficial visual noise. This leads to significantly higher robustness in dynamic environments. The structural difference in execution is clearly reflected in the end-effector trajectory data: These real-world deployment recordings further illustrate this difference (Videos 1 and 2). Scaling Physical AI requires turning raw trajectory data into robust policies, and a rigorously engineered DR infrastructure is an essential bridge to close the Sim2Real gap.

Axis Robotics

27,125 görüntüleme • 3 ay önce

⚠️🚨STATEMENT REGARDING CHEATING A few days ago, we played a match in the Champion of Champions Tour CS2 tournament against the team Khan, and we have strong reasons to believe that this team does not win matches in the spirit of fair play. As you can see in the attached video, rotations are occurring without any information, just like in many other rounds, and also involve locking through walls, etc. This is not the first time suspicions of cheating have appeared on the Tier 2/3 scene. We all surely remember, for example, the team Senza. We decided to wait a few days to see how the situation would develop, but we can no longer remain silent. In the meantime, matches have taken place against teams such as EYEBALLERS and SINNERS Esports, where the same situations occurred as in our game. Therefore, we kindly ask all organisers, especially Champion of Champions Tour CS2, to put an end to this, to actively investigate such cases, and to stop inviting suspicious teams to their tournaments. A loss is a loss, but it must be fair — that’s what esports is about. By ignoring this problem, we are hurting not only the players but also the fans, who should be at the heart of it all. LET’S HELP EACH OTHER AND MAKE THE TIER 2/3 SCENE FAIR-PLAY AGAIN, SO THAT WE CAN ENJOY THE GAME! Thank you, UNiTY Esports
2:24

⚠️🚨STATEMENT REGARDING CHEATING A few days ago, we played a match in the Champion of Champions Tour CS2 tournament against the team Khan, and we have strong reasons to believe that this team does not win matches in the spirit of fair play. As you can see in the attached video, rotations are occurring without any information, just like in many other rounds, and also involve locking through walls, etc. This is not the first time suspicions of cheating have appeared on the Tier 2/3 scene. We all surely remember, for example, the team Senza. We decided to wait a few days to see how the situation would develop, but we can no longer remain silent. In the meantime, matches have taken place against teams such as EYEBALLERS and SINNERS Esports, where the same situations occurred as in our game. Therefore, we kindly ask all organisers, especially Champion of Champions Tour CS2, to put an end to this, to actively investigate such cases, and to stop inviting suspicious teams to their tournaments. A loss is a loss, but it must be fair — that’s what esports is about. By ignoring this problem, we are hurting not only the players but also the fans, who should be at the heart of it all. LET’S HELP EACH OTHER AND MAKE THE TIER 2/3 SCENE FAIR-PLAY AGAIN, SO THAT WE CAN ENJOY THE GAME! Thank you, UNiTY Esports

UNiTY ESPORTS

178,856 görüntüleme • 9 ay önce

🚀We are thrilled to open-source Hunyuan-GameCraft, a high-dynamic interactive game video generation framework built on HunyuanVideo. It generates playable and physically realistic videos from a single scene image and user action signals, empowering creators and developers to "direct" games with first-person or third-person perspectives. Key Advantages: 🔹High Dynamics: Unifies standard keyboard inputs into a shared continuous action space, enabling high-precision control over velocity and angle. This allows for the exploration of complex trajectories, overcoming the stiff, limited motion of traditional models. It can also generate dynamic environmental content like moving clouds, rain, snow, and water flow. 🔹Long-term Consistency: Uses hybrid history condition to preserve the original scene information after significant movement. 🔹Significant Cost Reduction: No need for expensive modeling/rendering. PCM distillation compresses inference steps, boosting speed and lowering costs. This allows the quantized 13B model to run on consumer-grade GPUs like the RTX 4090. Project Page: Code: Technical Report: Hugging Face：
0:40

🚀We are thrilled to open-source Hunyuan-GameCraft, a high-dynamic interactive game video generation framework built on HunyuanVideo. It generates playable and physically realistic videos from a single scene image and user action signals, empowering creators and developers to "direct" games with first-person or third-person perspectives. Key Advantages: 🔹High Dynamics: Unifies standard keyboard inputs into a shared continuous action space, enabling high-precision control over velocity and angle. This allows for the exploration of complex trajectories, overcoming the stiff, limited motion of traditional models. It can also generate dynamic environmental content like moving clouds, rain, snow, and water flow. 🔹Long-term Consistency: Uses hybrid history condition to preserve the original scene information after significant movement. 🔹Significant Cost Reduction: No need for expensive modeling/rendering. PCM distillation compresses inference steps, boosting speed and lowering costs. This allows the quantized 13B model to run on consumer-grade GPUs like the RTX 4090. Project Page: Code: Technical Report: Hugging Face：

Tencent Hy

169,638 görüntüleme • 11 ay önce

Today we're launching iMessage video ads in Claude. Faceless video is the best-performing ad format right now — and Claude can now generate a full one end to end, in one prompt. Here's how it works: 1. Install: npx gooseworks install --all 2. Run: /goose-video make an iMessage ad for [your product] Claude writes the script, generates the photos in the thread, animates the conversation, and closes on your brand. SFX, music, end card, all of it. The trick: it's just HTML. No video generation models, so it's fast and cheap — and it works because it doesn't look like an ad. It's that easy. But this is just the start. We have created a library with 100+ open-source skills for growth that some of the fastest growing startups in the world are using every day to run ads, content, competitor research, gtm, seo and more. Comment Goose and I'll DM you the full open-source skill library.
0:28

Today we're launching iMessage video ads in Claude. Faceless video is the best-performing ad format right now — and Claude can now generate a full one end to end, in one prompt. Here's how it works: 1. Install: npx gooseworks install --all 2. Run: /goose-video make an iMessage ad for [your product] Claude writes the script, generates the photos in the thread, animates the conversation, and closes on your brand. SFX, music, end card, all of it. The trick: it's just HTML. No video generation models, so it's fast and cheap — and it works because it doesn't look like an ad. It's that easy. But this is just the start. We have created a library with 100+ open-source skills for growth that some of the fastest growing startups in the world are using every day to run ads, content, competitor research, gtm, seo and more. Comment Goose and I'll DM you the full open-source skill library.

Soham Mehta

47,012 görüntüleme • 7 gün önce

Code and data are now online for CameraHMR, our state-of-the-art parametric 3D human pose and shape (HPS) estimation method that will appear at hashtag#3DV2025. There are 4 key contributions that make it so accurate and robust: 1. To get accurate 3D shape and pose as well as good alignment to image features, you need to know the focal length of the camera. To solve this, we train HumanFOV to compute the field of view. 2. We introduce CameraHMR, which integrates HumanFOV into HMR2.0 to exploit the estimated focal length. 3. To get accurate pseudo ground truth (pGT) training data, we compute the focal length for images in 4DHumans dataset and modify SMPLify to take this into account. 4. But SMPLify only uses sparse 2D keypoints, which do not capture body shape. So we train a dense surface keypoint detector, DenseKP, on BEDLAM and run it on 4DHumans, resulting in improved body shape. The resulting method is CamSMPLify. We iterate training CameraHMR and running CamSMPLify on the training set initialized with CameraHMR. This results in much improved pGT for 4DHumans and a SOTA single-image HMR method.
4:38

Code and data are now online for CameraHMR, our state-of-the-art parametric 3D human pose and shape (HPS) estimation method that will appear at hashtag#3DV2025. There are 4 key contributions that make it so accurate and robust: 1. To get accurate 3D shape and pose as well as good alignment to image features, you need to know the focal length of the camera. To solve this, we train HumanFOV to compute the field of view. 2. We introduce CameraHMR, which integrates HumanFOV into HMR2.0 to exploit the estimated focal length. 3. To get accurate pseudo ground truth (pGT) training data, we compute the focal length for images in 4DHumans dataset and modify SMPLify to take this into account. 4. But SMPLify only uses sparse 2D keypoints, which do not capture body shape. So we train a dense surface keypoint detector, DenseKP, on BEDLAM and run it on 4DHumans, resulting in improved body shape. The resulting method is CamSMPLify. We iterate training CameraHMR and running CamSMPLify on the training set initialized with CameraHMR. This results in much improved pGT for 4DHumans and a SOTA single-image HMR method.

Michael Black

21,696 görüntüleme • 1 yıl önce

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.
5:58:26

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,194 görüntüleme • 2 ay önce

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.
0:59

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Kevin Tanaka

10,256 görüntüleme • 2 ay önce

The CS2 update we have been waiting for is finally here. Valve has created a Beta branch for animgraph2 for third person animations. This is NOT an April fools joke. While everyone was watching the launch of Artemis II, Valve suddenly released what they've been working on for the past couple of years. What this means is that the entire animation system for third person models has been completely redone. No longer does it use the unoptimized Half-Life: Alyx system. This significantly reduces packet size. You can now tell when someone is counter-strafing, and properly predict their movements based on visual feedback. Another massive change here is that the player's view now is accurate to the player model, meaning that we have a variable player height of +-4 units. You'll immediately notice that the player seems shorter. This completely changes many many angles, as well as many utility lineups, particularly when standing on slopes. Notice in my video example how you can no longer see over the Dust2 A site boxes from A ramp. The change is currently in Beta, so it's all subject to change. I will be testing it out extensively and making a video about my thoughts. First impressions: I am VERY excited about this, more so than the Artemis II rocket taking humans back to the moon for the first time in forever.
0:20

The CS2 update we have been waiting for is finally here. Valve has created a Beta branch for animgraph2 for third person animations. This is NOT an April fools joke. While everyone was watching the launch of Artemis II, Valve suddenly released what they've been working on for the past couple of years. What this means is that the entire animation system for third person models has been completely redone. No longer does it use the unoptimized Half-Life: Alyx system. This significantly reduces packet size. You can now tell when someone is counter-strafing, and properly predict their movements based on visual feedback. Another massive change here is that the player's view now is accurate to the player model, meaning that we have a variable player height of +-4 units. You'll immediately notice that the player seems shorter. This completely changes many many angles, as well as many utility lineups, particularly when standing on slopes. Notice in my video example how you can no longer see over the Dust2 A site boxes from A ramp. The change is currently in Beta, so it's all subject to change. I will be testing it out extensively and making a video about my thoughts. First impressions: I am VERY excited about this, more so than the Artemis II rocket taking humans back to the moon for the first time in forever.

WarOwl

450,875 görüntüleme • 3 ay önce

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.
0:52

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.

Dustin

23,499 görüntüleme • 4 ay önce

We built an AI-steered homing/killer drone in just a few hours I thought it would be fun to build a drone that chases you around as a game. It just uses an AI object detection model to find people in the frame, and then the drone is programmed to fly towards this at full speed as soon as it detects someone. This literally took just a few hours to build, and made me realize how scary it is. You could easily strap a small amount of explosives on these and let 100's of them fly around. We check for bombs and guns but THERE ARE NO ANTI-DRONE SYSTEMS FOR BIG EVENTS & PUBLIC SPACES YET. I was also able to add face recognition to it, and only make it attack someone it knew who was, it could easily identify the person from 10 meters distance. My bet is that we will see some sort of terror attack using this type of tech within the next few years. You still need some technical knowledge to build this now, but it becomes easier and easier. We need to build anti-drone systems for civilian spaces asap. I'm usually an open-source absolutist, but I will not post for now, it's honestly super easy to code but no point enabling. Disclaimer: this was built on a tiny drone for a game and would not hurt anyone, just wanted to spread awareness around this .
0:26

We built an AI-steered homing/killer drone in just a few hours I thought it would be fun to build a drone that chases you around as a game. It just uses an AI object detection model to find people in the frame, and then the drone is programmed to fly towards this at full speed as soon as it detects someone. This literally took just a few hours to build, and made me realize how scary it is. You could easily strap a small amount of explosives on these and let 100's of them fly around. We check for bombs and guns but THERE ARE NO ANTI-DRONE SYSTEMS FOR BIG EVENTS & PUBLIC SPACES YET. I was also able to add face recognition to it, and only make it attack someone it knew who was, it could easily identify the person from 10 meters distance. My bet is that we will see some sort of terror attack using this type of tech within the next few years. You still need some technical knowledge to build this now, but it becomes easier and easier. We need to build anti-drone systems for civilian spaces asap. I'm usually an open-source absolutist, but I will not post for now, it's honestly super easy to code but no point enabling. Disclaimer: this was built on a tiny drone for a game and would not hurt anyone, just wanted to spread awareness around this .

Luis Wenus

548,706 görüntüleme • 2 yıl önce

Building a RAG system that works with real-life documents is crazy hard. Why is nobody talking about this? (I'll show you what a complex document looks like in the attached video. Good luck with the 10-line code demos if you want to deal with this.) All I see online are "how to talk to a PDF" demos that won't take you far. If you think that's all you need, you won't like what happens when you try. In the video below, I'll show you what it takes to build a system that processes huge numbers of documents without losing accuracy. There are a few tricks here that I'm sure will impress you. The magic is happening in three phases: • In the way we process the documents • In the way we chunk the content • In the way we augment and store the chunks This is all built-in in GroundX, EyeLevel.AI's out-of-the-box RAG system. You can use it as a SaaS or install it in your own Kubernetes cluster and run it locally. If you have seen any other RAG system doing what I do in the attached video, please, let me know. I'd love to check them out. Disclaimer: I work with the team at EyeLevel. My advice is to take any document you have lying around, go to their site, create an account, and try them out. Here is their website:
10:11

Building a RAG system that works with real-life documents is crazy hard. Why is nobody talking about this? (I'll show you what a complex document looks like in the attached video. Good luck with the 10-line code demos if you want to deal with this.) All I see online are "how to talk to a PDF" demos that won't take you far. If you think that's all you need, you won't like what happens when you try. In the video below, I'll show you what it takes to build a system that processes huge numbers of documents without losing accuracy. There are a few tricks here that I'm sure will impress you. The magic is happening in three phases: • In the way we process the documents • In the way we chunk the content • In the way we augment and store the chunks This is all built-in in GroundX, EyeLevel.AI's out-of-the-box RAG system. You can use it as a SaaS or install it in your own Kubernetes cluster and run it locally. If you have seen any other RAG system doing what I do in the attached video, please, let me know. I'd love to check them out. Disclaimer: I work with the team at EyeLevel. My advice is to take any document you have lying around, go to their site, create an account, and try them out. Here is their website:

Santiago

75,788 görüntüleme • 1 yıl önce

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)
1:24

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Davide Scaramuzza

27,917 görüntüleme • 2 yıl önce

The children of Auchtertool made this video to tell us how they feel about the hyper scale data center planned for our village. Please take a few minutes to watch it. But this isn't an Auchtertool problem - it's a Scottish one. The proposed data centre would need roughly half the power currently used by every home in Scotland combined. Who is paying to build all that extra capacity? It would also swallow huge amounts of water, and take much-needed farming land out of the food chain, which we all rely on. And to be clear - this isn't about being "anti-Al". A data centre like this powers the everyday: your photo storage, your online shopping, and yes, Al too. The question isn't whether we need infrastructure. It's how we build it safely, and sustainably. And how the people of Scotland benefit from it. At this scale, the only obvious winner is the developer. It's not obvious what the tangible benefits are for the people of Scotland. If you think a project this big deserves to be properly thought through, visit our village website to find out more and object.
6:46

The children of Auchtertool made this video to tell us how they feel about the hyper scale data center planned for our village. Please take a few minutes to watch it. But this isn't an Auchtertool problem - it's a Scottish one. The proposed data centre would need roughly half the power currently used by every home in Scotland combined. Who is paying to build all that extra capacity? It would also swallow huge amounts of water, and take much-needed farming land out of the food chain, which we all rely on. And to be clear - this isn't about being "anti-Al". A data centre like this powers the everyday: your photo storage, your online shopping, and yes, Al too. The question isn't whether we need infrastructure. It's how we build it safely, and sustainably. And how the people of Scotland benefit from it. At this scale, the only obvious winner is the developer. It's not obvious what the tangible benefits are for the people of Scotland. If you think a project this big deserves to be properly thought through, visit our village website to find out more and object.

Simon Forrest

28,708 görüntüleme • 16 gün önce