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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... show more

Reka

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95,190 просмотров • 2 месяцев назад •via X (Twitter)

Игры Образование Наука и технологии

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The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

Lightwheel

69,659 просмотров • 8 дней назад

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

Rohan Paul

26,597 просмотров • 26 дней назад

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 →

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 🐳

168,805 просмотров • 5 месяцев назад

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!

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

74,440 просмотров • 5 месяцев назад

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.

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

471,643 просмотров • 1 год назад

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.

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™

32,319 просмотров • 4 месяцев назад

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.

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 просмотров • 1 месяц назад

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Featherless AI

39,769 просмотров • 10 дней назад

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.

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

193,823 просмотров • 1 месяц назад

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.

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 просмотров • 4 месяцев назад

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Dustin

122,066 просмотров • 1 месяц назад

⚠️🚨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

⚠️🚨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

178,991 просмотров • 11 месяцев назад

🚀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：

🚀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

169,757 просмотров • 1 год назад

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.

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 просмотров • 1 год назад

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

50,349 просмотров • 1 месяц назад

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.

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

230,988 просмотров • 4 месяцев назад

$Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic$

Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic

MTS

33,856 просмотров • 25 дней назад

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.

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 просмотров • 4 месяцев назад

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Dustin

12,094 просмотров • 1 месяц назад