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🎙️MiniCPM-o 4.5: Full-duplex interaction in motion. Watch the 9B model track and identify fruit price tags in a dynamic live stream. Unlike traditional reactive systems, MiniCPM-o 4.5 processes continuous video and audio inputs to see, listen, and respond simultaneously—without mutual blocking. 🚀This end-to-end architecture enables low-latency, proactive feedback even...

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OpenBMB

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✨ Excited to share QVQ-Max, our visual reasoning model that's still evolving We've been experimenting with this approach for a while - try it out on Qwen Chat! ( 🚀 Just upload any image or video, ask away, and hit the "Thinking" button to see how it processes visual information step-by-step. It's a work-in-progress but fascinating to watch! Your early feedback will be super helpful as we continue developing! 🙏 Blog:
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✨ Excited to share QVQ-Max, our visual reasoning model that's still evolving We've been experimenting with this approach for a while - try it out on Qwen Chat! ( 🚀 Just upload any image or video, ask away, and hit the "Thinking" button to see how it processes visual information step-by-step. It's a work-in-progress but fascinating to watch! Your early feedback will be super helpful as we continue developing! 🙏 Blog:

Qwen

147,074 просмотров • 1 год назад

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.
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🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.

TheNewPhysics

23,196 просмотров • 2 месяцев назад

Scottish fans drank Boston dry? 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🍺 We asked UpRock Semantic Video Search. Seconds later it nailed the exact timestamp. Not the title. Not a guess. The precise moment it happened. This is the new gap in AI. Most tools read transcripts or the metadata and quit. UpRock actually "watches" the video: what is said, what is shown, the action, the context, the vibe, why it matters. Last livestream we connected UpRock Video MCP to one of OpenAI's cheapest models. Without UpRock the model was blind. With it? The model spotted background signs, clothing details, objects, and live action like a pro. More and more of the internet's richest knowledge lives inside video. We are unlocking it. Over 100,000 videos indexed and climbing fast. Web agents learned to read. Now they are learning to watch. Watch the demo (Scottish fans moment): UpRock enables your AI to understand video the way humans do, saving you from slow, manual digging. Who else wants their AI to actually watch and save time?
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Scottish fans drank Boston dry? 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🍺 We asked UpRock Semantic Video Search. Seconds later it nailed the exact timestamp. Not the title. Not a guess. The precise moment it happened. This is the new gap in AI. Most tools read transcripts or the metadata and quit. UpRock actually "watches" the video: what is said, what is shown, the action, the context, the vibe, why it matters. Last livestream we connected UpRock Video MCP to one of OpenAI's cheapest models. Without UpRock the model was blind. With it? The model spotted background signs, clothing details, objects, and live action like a pro. More and more of the internet's richest knowledge lives inside video. We are unlocking it. Over 100,000 videos indexed and climbing fast. Web agents learned to read. Now they are learning to watch. Watch the demo (Scottish fans moment): UpRock enables your AI to understand video the way humans do, saving you from slow, manual digging. Who else wants their AI to actually watch and save time?

UpRock

75,135 просмотров • 1 месяц назад

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:
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Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:

Bilawal Sidhu

44,822 просмотров • 2 лет назад

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 просмотров • 1 месяц назад

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.
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This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 просмотров • 3 месяцев назад

⚠️ LEAKED FOOTAGE OF THE FIRST REAL MEDBED UNIT ⚠️ THE TECHNOLOGY THEY HID FROM HUMANITY FOR DECADES IS NO LONGER IN THE DARK. THIS DEVICE WAS RESERVED ONLY FOR ELITES, BLACK PROJECT OPERATIVES, AND BILLIONAIRE FAMILIES — WHILE THE WORLD SUFFERED. THEY USED IT TO REGENERATE ORGANS. THEY USED IT TO ERASE DISEASE. THEY USED IT TO EXTEND THEIR LIVES. AND THEY NEVER INTENDED TO SHARE IT WITH YOU. BUT THAT ERA IS OVER. THE TRUMP ADMINISTRATION FORCED OPEN THE CLASSIFIED PROGRAMS, BROKE THE CONTRACTS OF BIG PHARMA, AND EXPOSED THE TECHNOLOGY DESIGNED TO HEAL THE WORLD. WHAT YOU SEE IN THIS VIDEO IS NOT A CONCEPT — IT IS A WORKING MEDBED. A QUANTUM HEALING PLATFORM CAPABLE OF CELL REPAIR, DNA RESTORATION, AND FULL-BODY REGENERATION AT THE ATOMIC LEVEL. THIS IS THE TECHNOLOGY THAT WILL END THE AGE OF DISEASE. THIS IS THE MOMENT HUMANITY TAKES BACK WHAT WAS STOLEN. THE MEDBED ERA BEGINS NOW. RESERVATIONS ARE OPEN FOR THE FIRST PUBLIC ACCESS WINDOW. ENTER THE GRID BELOW TO SECURE YOUR POSITION: 👉 THE FUTURE OF HEALING IS NO LONGER SECRET. IT IS YOURS. #MEDBED #QUANTUMHEALING #TERAHERTZ #SCALARTECH #DNAREPAIR #ENDOFBIGPHARMA #TRUMP2025 #HUMANITYRISING #HEALINGREVOLUTION #THEGREATREVEAL
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⚠️ LEAKED FOOTAGE OF THE FIRST REAL MEDBED UNIT ⚠️ THE TECHNOLOGY THEY HID FROM HUMANITY FOR DECADES IS NO LONGER IN THE DARK. THIS DEVICE WAS RESERVED ONLY FOR ELITES, BLACK PROJECT OPERATIVES, AND BILLIONAIRE FAMILIES — WHILE THE WORLD SUFFERED. THEY USED IT TO REGENERATE ORGANS. THEY USED IT TO ERASE DISEASE. THEY USED IT TO EXTEND THEIR LIVES. AND THEY NEVER INTENDED TO SHARE IT WITH YOU. BUT THAT ERA IS OVER. THE TRUMP ADMINISTRATION FORCED OPEN THE CLASSIFIED PROGRAMS, BROKE THE CONTRACTS OF BIG PHARMA, AND EXPOSED THE TECHNOLOGY DESIGNED TO HEAL THE WORLD. WHAT YOU SEE IN THIS VIDEO IS NOT A CONCEPT — IT IS A WORKING MEDBED. A QUANTUM HEALING PLATFORM CAPABLE OF CELL REPAIR, DNA RESTORATION, AND FULL-BODY REGENERATION AT THE ATOMIC LEVEL. THIS IS THE TECHNOLOGY THAT WILL END THE AGE OF DISEASE. THIS IS THE MOMENT HUMANITY TAKES BACK WHAT WAS STOLEN. THE MEDBED ERA BEGINS NOW. RESERVATIONS ARE OPEN FOR THE FIRST PUBLIC ACCESS WINDOW. ENTER THE GRID BELOW TO SECURE YOUR POSITION: 👉 THE FUTURE OF HEALING IS NO LONGER SECRET. IT IS YOURS. #MEDBED #QUANTUMHEALING #TERAHERTZ #SCALARTECH #DNAREPAIR #ENDOFBIGPHARMA #TRUMP2025 #HUMANITYRISING #HEALINGREVOLUTION #THEGREATREVEAL

Mr. Pool

2,390,105 просмотров • 9 месяцев назад

Today's recap: - Initial prototype of Divine's face was printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.
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Today's recap: - Initial prototype of Divine's face was printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.

Parallel

35,848 просмотров • 1 год назад

How to use China's best AI video model (Seedance 2.0) completely free and without watermarks. Most people think you need expensive subscriptions or get stuck with watermarked outputs (mandatory in China). Not if you use this loophole. The Setup • VPN to Hong Kong/China (only to start download). • Download the "Doubao" app from doubao[dot]com. • Install (Android: allow from browser / iOS: switch appstore region to HK). • Register with a non-US phone number, if you're from US and need a SMS service DM me, I'll send you one. The "Glitch" Workflow The Seedance 2.0 feature in app often fails. Do this instead: • Toggle Image Model (Seedream 4.5). • Type your prompt. • Add this specific text at the end: "generate video, not the image". • Result: 10s video, bypassing the busy server errors. How to Remove the Watermark If you save directly, it stamps it. Here is the fix: • Tap Share → Copy Link. • Open that link in your mobile browser. • Long press video → Download. Congratulations, you just got the clean video without watermarks. Total Cost: $0 Daily Limit: 5 free videos per account This method is only one so far. Bookmark if you need this.
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How to use China's best AI video model (Seedance 2.0) completely free and without watermarks. Most people think you need expensive subscriptions or get stuck with watermarked outputs (mandatory in China). Not if you use this loophole. The Setup • VPN to Hong Kong/China (only to start download). • Download the "Doubao" app from doubao[dot]com. • Install (Android: allow from browser / iOS: switch appstore region to HK). • Register with a non-US phone number, if you're from US and need a SMS service DM me, I'll send you one. The "Glitch" Workflow The Seedance 2.0 feature in app often fails. Do this instead: • Toggle Image Model (Seedream 4.5). • Type your prompt. • Add this specific text at the end: "generate video, not the image". • Result: 10s video, bypassing the busy server errors. How to Remove the Watermark If you save directly, it stamps it. Here is the fix: • Tap Share → Copy Link. • Open that link in your mobile browser. • Long press video → Download. Congratulations, you just got the clean video without watermarks. Total Cost: $0 Daily Limit: 5 free videos per account This method is only one so far. Bookmark if you need this.

Alchemiz

16,888 просмотров • 5 месяцев назад

Yes, you can really see the aurora with your eyes — but not always as brightly or colorfully as photos suggest. The vivid greens, purples, and reds often seen online are enhanced by camera sensors that collect light over long exposures. Human vision works differently: in low light, our rod cells dominate, allowing us to detect faint movement and brightness but not much color. Because of this, many people perceive dim auroras as grey, white, or pale green rather than the vibrant tones shown in images. When the aurora is strong and directly overhead — especially in darker, high-latitude locations — your cone cells (which detect color) can engage, and you may see genuine greens and even pink or red hues. However, from lower latitudes or during weak displays, the colors are far subtler and may seem washed out. Factors like moonlight, mild light pollution, or even briefly flashing a light in your eyes can momentarily enhance color perception by activating your cone cells. So while the aurora is very real to the naked eye, it usually appears less colorful but more dynamic than in photographs — shimmering, shifting, and alive in motion. The true magic lies not in its brightness, but in the experience of seeing the sky itself come to life.
0:16

Yes, you can really see the aurora with your eyes — but not always as brightly or colorfully as photos suggest. The vivid greens, purples, and reds often seen online are enhanced by camera sensors that collect light over long exposures. Human vision works differently: in low light, our rod cells dominate, allowing us to detect faint movement and brightness but not much color. Because of this, many people perceive dim auroras as grey, white, or pale green rather than the vibrant tones shown in images. When the aurora is strong and directly overhead — especially in darker, high-latitude locations — your cone cells (which detect color) can engage, and you may see genuine greens and even pink or red hues. However, from lower latitudes or during weak displays, the colors are far subtler and may seem washed out. Factors like moonlight, mild light pollution, or even briefly flashing a light in your eyes can momentarily enhance color perception by activating your cone cells. So while the aurora is very real to the naked eye, it usually appears less colorful but more dynamic than in photographs — shimmering, shifting, and alive in motion. The true magic lies not in its brightness, but in the experience of seeing the sky itself come to life.

Vincent Ledvina

16,699 просмотров • 8 месяцев назад

WATCH THIS VIDEO CAREFULLY. FORENSIC ANALYSIS OF A VIDEO CURRENTLY BEING CIRCULATED AND SPREAD ON ARAB TELEGRAM CHANNELS (Mor Edge Insight in conjunction with GAZAWOOD - The Pallywood Saga - BACKUP - July 6) What you are about to see is raw footage of an active arrest operation and genuine footage. This clip is currently circulating on Palestinian Telegram channels and is being prepared for wider distribution on X. It follows a familiar pattern of real footage with heavy manipulation and inauthentic audio to create a perception and narrative that doesn’t exist and is not what the footage actually shows. Here is the step-by-step forensic breakdown. The audio track contains multiple sharp “gunshots.” However, frame-by-frame examination shows no muzzle flashes at any point, even in bright daylight where unsuppressed firearms would produce clear, visible bursts. There is also no visible recoil or weapon movement on the individuals holding rifles. The barrels show no suppressors, yet the sounds are relatively clean “pops” rather than the overwhelming cracks expected from unsuppressed fire at that range. The audio of the shots fired are more reminiscent of a children’s toy than a real gunshot. More critically, the visual action is happening at a clear distance across the road, at a distance of an estimated 60-100m away from the camera, yet the gunshots and shouting sound as if recorded right next to the camera. Real distant gunfire would be thinner, more muffled, and accompanied by environmental echoes. This audio was added in post-production. How distance was determined: The white car in the immediate foreground (partially visible on the left) is only 5–10 meters away. The road width and the position of the parked vehicles and people with guns put the core action clearly in the mid-ground, across the full width of the street and shoulder. Reference objects: Standard car lengths (4.5–5m), average adult height (1.7m), and the spacing of streetlights/power poles all support a distance in that 60–100 meter range for the shooters and the SUV. The black SUV drives a noticeable distance across the frame without appearing overly large or close, further confirming it’s not right next to the camera. This distance makes the audio mismatch even more obvious. Real gunfire at 60–100 meters would sound significantly more distant and muted, with clear delay and environmental filtering. The overlaid “cracks” sound like they were recorded (or synthesized) much closer. Summary 1. Real gunshots, especially in an open outdoor environment like this, produce a sharp initial crack (supersonic bullet) followed by a broader report/echo, with significant low-frequency rumble, reverberation off the ground/cars/objects, and environmental decay. These sound more like clean “pop/crack” samples layered on top. 2. They lack the natural variations in volume, timing, or distortion you’d expect from actual firearms in a real chaotic scene (muzzle blast, echoes, distance differences) even with silencers which from that distance you wouldn’t even hear. They feel “pasted in” during editing. 3. The overall audio mix (ambient road noise, car sounds, voices) doesn’t interact naturally with the “shots”, there is no proper masking, reverb bleed, or mic overload you’d get from real loud events captured on the same recording device. Always examine the audio against the visuals, check for continuity errors, and watch how people actually behave when they think no one is watching the performance. Share if you value this kind of detailed verification.
0:12

WATCH THIS VIDEO CAREFULLY. FORENSIC ANALYSIS OF A VIDEO CURRENTLY BEING CIRCULATED AND SPREAD ON ARAB TELEGRAM CHANNELS (Mor Edge Insight in conjunction with GAZAWOOD - The Pallywood Saga - BACKUP - July 6) What you are about to see is raw footage of an active arrest operation and genuine footage. This clip is currently circulating on Palestinian Telegram channels and is being prepared for wider distribution on X. It follows a familiar pattern of real footage with heavy manipulation and inauthentic audio to create a perception and narrative that doesn’t exist and is not what the footage actually shows. Here is the step-by-step forensic breakdown. The audio track contains multiple sharp “gunshots.” However, frame-by-frame examination shows no muzzle flashes at any point, even in bright daylight where unsuppressed firearms would produce clear, visible bursts. There is also no visible recoil or weapon movement on the individuals holding rifles. The barrels show no suppressors, yet the sounds are relatively clean “pops” rather than the overwhelming cracks expected from unsuppressed fire at that range. The audio of the shots fired are more reminiscent of a children’s toy than a real gunshot. More critically, the visual action is happening at a clear distance across the road, at a distance of an estimated 60-100m away from the camera, yet the gunshots and shouting sound as if recorded right next to the camera. Real distant gunfire would be thinner, more muffled, and accompanied by environmental echoes. This audio was added in post-production. How distance was determined: The white car in the immediate foreground (partially visible on the left) is only 5–10 meters away. The road width and the position of the parked vehicles and people with guns put the core action clearly in the mid-ground, across the full width of the street and shoulder. Reference objects: Standard car lengths (4.5–5m), average adult height (1.7m), and the spacing of streetlights/power poles all support a distance in that 60–100 meter range for the shooters and the SUV. The black SUV drives a noticeable distance across the frame without appearing overly large or close, further confirming it’s not right next to the camera. This distance makes the audio mismatch even more obvious. Real gunfire at 60–100 meters would sound significantly more distant and muted, with clear delay and environmental filtering. The overlaid “cracks” sound like they were recorded (or synthesized) much closer. Summary 1. Real gunshots, especially in an open outdoor environment like this, produce a sharp initial crack (supersonic bullet) followed by a broader report/echo, with significant low-frequency rumble, reverberation off the ground/cars/objects, and environmental decay. These sound more like clean “pop/crack” samples layered on top. 2. They lack the natural variations in volume, timing, or distortion you’d expect from actual firearms in a real chaotic scene (muzzle blast, echoes, distance differences) even with silencers which from that distance you wouldn’t even hear. They feel “pasted in” during editing. 3. The overall audio mix (ambient road noise, car sounds, voices) doesn’t interact naturally with the “shots”, there is no proper masking, reverb bleed, or mic overload you’d get from real loud events captured on the same recording device. Always examine the audio against the visuals, check for continuity errors, and watch how people actually behave when they think no one is watching the performance. Share if you value this kind of detailed verification.

Mor Edge Insight

23,394 просмотров • 1 месяц назад

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."
0:14

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

Rohan Paul

12,761 просмотров • 6 месяцев назад

Wow. WOW. WOOOOOOOW. So um, that first Wildcard+Thousands stream was... *amazing* and also... a *lot* 😅 In the end, it was *exactly* what we were hoping for - a true stress test of ALL these systems coming together for the first time. We are SO grateful for the thousands of people who showed up today to play, attend, tune-in and help us PLAYTEST all this new stuff. We can't wait to see you all again at NEXT WEEK'S EX2 EVENT! So, now let's talk about how it went... Stuff that worked: - Our community SHOWED UP. Oh boy did you show up 😅. Our servers were straining under the load... which is good actually, in fact it's the whole point. Even more importantly, we have already received insanely valuable feedback, bug reports, stuff people loved/hated - and it's only been a few hours since the stream ended. I can't even explain to y'all how valuable this process is. Yes it's stressful, it reminds me of trying to keep Words With Friends online during that first insane year, but it's EXACTLY what we were hoping for (NEED) to turn this into the polished, top-notch game and streaming experience we are on a mission to deliver. I truly can't thank y'all enough, and hope to see you again when we run it all back again next week 🥹 - The stream itself stayed up and was mostly stable! Phew 😅. For context, ThousandsTV is not a twitch wrapper, it's a web3-native streaming tech stack built we built specifically to connect game, web/mobile, and blockchain together all at the same time. There are a LOT of moving pieces going on behind the scenes. - We brought viewers INTO THE GAME! Viewers showed up in the stands of the arena, with connected wallets/assets, triggered actions/rallies from chat, and were seen and heard during the whole stream. - The brand new 2v2 build of Wildcard was (mostly) stable and our players and viewers seemed to be having a blast down on the field and up in the stands. It was thrilling to watch Team Blue dominate, even though Team Red held their own in game 3! - Our production crew did an insanely good job running the stream, managing the players, shoutcasters, and talent, and producing a top-notch show. Of course we will work hard to make every stream better than the last, but I was super proud of how our team "rolled with the punches" during today's event. Stuff that didn't work (and/or needs to be dramatically improved): - Although it's fun to see chat going crazy, chat spam is actually something we are passionate to FIX. As you can see from the attached video, chat spam dominated today's stream and made it impossible for anyone to even see anyone else's messages. We have some GREAT ideas for how to fix this and actually turn chat spam into a FUN and exciting and not annoying thing - but those improvements didn't get shipped in time for this event. - Credits purchasing flow needs a LOT of work. As I'm sure y'all know, bringing money on chain is pretty complicated, and although we've been working hard to make this as seamless as possible, it still needs a TON of improvements. Many users who WANTED to spend money today weren't able to and/or ran into frustrating bugs in the credit purchase flow. Fixing this is obviously a top priority for our team. - Rallies need a LOT of work. Spectator-interactive features like rallies are at the heart of our vision for Wildcard. These "stream apps", as we call them, are the UNLOCK for how spectators, viewers and fans directly connect and interact with their favorite competitors, content creators, and communities. The current rally feature HINTS at this potential, but it needs to be WAY easier to understand, use, and have fun with. Improvements are ON THE WAY. - Referees were only partially working. Referees are a key innovation of the Thousands platform. They are AI-driven "personalities" (NPCs) that pay attention to everything that's happening in the arena, both on the field and especially in the stands (i.e. in chat, during rallies, etc.) The referees then make "calls" at the end of every match, rewarding users for their engagement and participation. Unfortunately, the referees weren't fully functional and seemed to drop the ball on recognizing everyone's contributions (especially people who showed up holding valuable assets such as Wildpasses in their wallets, and people who boosted those rallies with credits.) What's happening next: 1. We are combing through ALL the logs from the event right now, to make sure we don't miss a SINGLE action that our viewers and fans took during the event, including what they brought in their wallets (i.e. Wildpass holders!), any credits that were purchased, rallies that users engaged with, etc. This information is normally processed by our referees, who then determine dynamically how they're going to distribute $WC awards. We were originally hoping to complete this process and the subsequent airdrops within a few hours after the event, but given the amount of data, we need a bit more time to run these scripts (and airdrops) in batches instead of all at once, and make sure ALL of the data is being included. IMPORTANT: I will keep y'all updated in real-time here on twitter/X as this process is ongoing, and let you know the moment it's complete and all the awards have been distributed (i.e. when to go check your wallets 😎) 2. PLEASE keep sending us your feedback and bug reports. Open a ticket on our Discord and let us know what you loved, what you hated, and especially what we need to FIX. Given the overwhelming response to this event, it will likely take us several days to process everyone's feedback and fix all the bugs, but we WILL NOT REST until every ticket is closed/resolved. Thank you in advance for your patience. 3. We turn it up another notch next week. As our dear friends Wolves DAO just announced, the Wildcard Exhibition Event #2 is streaming LIVE from the WOLVES DEN AT GDC next Friday! If you missed out on all the action today, DON'T WORRY, because as I keep saying: we are just getting warmed up (and there is a LOT more b that needs to be distributed, get what I'm sayin??? 😎) Finally: Just wanted to say THANK YOU, again. Truly, from the bottom of my (our) hearts. Your excitement and enthusiasm for what we're building is why we do this. Even (especially, in fact) when you tell us all the things you want us to improve. We thrive off this feedback, it's how this game and this platform go from good to GREAT. I am so grateful for those of you who are taking this journey with us. SEE YA NEXT WEEK!!!
0:10

Wow. WOW. WOOOOOOOW. So um, that first Wildcard+Thousands stream was... *amazing* and also... a *lot* 😅 In the end, it was *exactly* what we were hoping for - a true stress test of ALL these systems coming together for the first time. We are SO grateful for the thousands of people who showed up today to play, attend, tune-in and help us PLAYTEST all this new stuff. We can't wait to see you all again at NEXT WEEK'S EX2 EVENT! So, now let's talk about how it went... Stuff that worked: - Our community SHOWED UP. Oh boy did you show up 😅. Our servers were straining under the load... which is good actually, in fact it's the whole point. Even more importantly, we have already received insanely valuable feedback, bug reports, stuff people loved/hated - and it's only been a few hours since the stream ended. I can't even explain to y'all how valuable this process is. Yes it's stressful, it reminds me of trying to keep Words With Friends online during that first insane year, but it's EXACTLY what we were hoping for (NEED) to turn this into the polished, top-notch game and streaming experience we are on a mission to deliver. I truly can't thank y'all enough, and hope to see you again when we run it all back again next week 🥹 - The stream itself stayed up and was mostly stable! Phew 😅. For context, ThousandsTV is not a twitch wrapper, it's a web3-native streaming tech stack built we built specifically to connect game, web/mobile, and blockchain together all at the same time. There are a LOT of moving pieces going on behind the scenes. - We brought viewers INTO THE GAME! Viewers showed up in the stands of the arena, with connected wallets/assets, triggered actions/rallies from chat, and were seen and heard during the whole stream. - The brand new 2v2 build of Wildcard was (mostly) stable and our players and viewers seemed to be having a blast down on the field and up in the stands. It was thrilling to watch Team Blue dominate, even though Team Red held their own in game 3! - Our production crew did an insanely good job running the stream, managing the players, shoutcasters, and talent, and producing a top-notch show. Of course we will work hard to make every stream better than the last, but I was super proud of how our team "rolled with the punches" during today's event. Stuff that didn't work (and/or needs to be dramatically improved): - Although it's fun to see chat going crazy, chat spam is actually something we are passionate to FIX. As you can see from the attached video, chat spam dominated today's stream and made it impossible for anyone to even see anyone else's messages. We have some GREAT ideas for how to fix this and actually turn chat spam into a FUN and exciting and not annoying thing - but those improvements didn't get shipped in time for this event. - Credits purchasing flow needs a LOT of work. As I'm sure y'all know, bringing money on chain is pretty complicated, and although we've been working hard to make this as seamless as possible, it still needs a TON of improvements. Many users who WANTED to spend money today weren't able to and/or ran into frustrating bugs in the credit purchase flow. Fixing this is obviously a top priority for our team. - Rallies need a LOT of work. Spectator-interactive features like rallies are at the heart of our vision for Wildcard. These "stream apps", as we call them, are the UNLOCK for how spectators, viewers and fans directly connect and interact with their favorite competitors, content creators, and communities. The current rally feature HINTS at this potential, but it needs to be WAY easier to understand, use, and have fun with. Improvements are ON THE WAY. - Referees were only partially working. Referees are a key innovation of the Thousands platform. They are AI-driven "personalities" (NPCs) that pay attention to everything that's happening in the arena, both on the field and especially in the stands (i.e. in chat, during rallies, etc.) The referees then make "calls" at the end of every match, rewarding users for their engagement and participation. Unfortunately, the referees weren't fully functional and seemed to drop the ball on recognizing everyone's contributions (especially people who showed up holding valuable assets such as Wildpasses in their wallets, and people who boosted those rallies with credits.) What's happening next: 1. We are combing through ALL the logs from the event right now, to make sure we don't miss a SINGLE action that our viewers and fans took during the event, including what they brought in their wallets (i.e. Wildpass holders!), any credits that were purchased, rallies that users engaged with, etc. This information is normally processed by our referees, who then determine dynamically how they're going to distribute $WC awards. We were originally hoping to complete this process and the subsequent airdrops within a few hours after the event, but given the amount of data, we need a bit more time to run these scripts (and airdrops) in batches instead of all at once, and make sure ALL of the data is being included. IMPORTANT: I will keep y'all updated in real-time here on twitter/X as this process is ongoing, and let you know the moment it's complete and all the awards have been distributed (i.e. when to go check your wallets 😎) 2. PLEASE keep sending us your feedback and bug reports. Open a ticket on our Discord and let us know what you loved, what you hated, and especially what we need to FIX. Given the overwhelming response to this event, it will likely take us several days to process everyone's feedback and fix all the bugs, but we WILL NOT REST until every ticket is closed/resolved. Thank you in advance for your patience. 3. We turn it up another notch next week. As our dear friends Wolves DAO just announced, the Wildcard Exhibition Event #2 is streaming LIVE from the WOLVES DEN AT GDC next Friday! If you missed out on all the action today, DON'T WORRY, because as I keep saying: we are just getting warmed up (and there is a LOT more b that needs to be distributed, get what I'm sayin??? 😎) Finally: Just wanted to say THANK YOU, again. Truly, from the bottom of my (our) hearts. Your excitement and enthusiasm for what we're building is why we do this. Even (especially, in fact) when you tell us all the things you want us to improve. We thrive off this feedback, it's how this game and this platform go from good to GREAT. I am so grateful for those of you who are taking this journey with us. SEE YA NEXT WEEK!!!

WildPaul - BEAST MODE

26,851 просмотров • 1 год назад

Two cinematic prompts for Seedance 2.0 right here 👇 [STYLE + CAMERA + ATMOSPHERE] Ultra-photorealistic cinematic 15-second action sequence from a grounded action thriller film. Nighttime at an abandoned industrial train yard. Old trains, rusted tracks, broken platforms and warehouses. Strong moonlight and industrial lighting. The visual language is gritty and realistic with hyper-realistic practical destruction. Camera is handheld-dynamic with urgent moves. [IMAGE REFERENCES] No reference images provided. Generate a fully consistent lead character: a male fugitive in his mid-30s, short hair, intense expression. He wears a dark jacket, jeans and boots. Maintain exact same appearance and clothing throughout the clip. [TIMELINE SECOND BY SECOND] 0-3s: [Dynamic handheld tracking shot from side-rear] The man runs across the train yard as a freight train derails violently after hitting a collapsed section of track. Massive train cars tip over and crash into the ground, sending metal, wood and cargo flying. The camera tracks him urgently as the destruction spreads. 3-7s: [Slow continuous orbiting camera moving around the frozen destruction] At the peak of the derailment, everything freezes in perfect realistic physics. Huge train cars, twisted metal, wooden debris and clouds of dust hang motionless in the air with accurate weight and trajectories. The camera performs a smooth, continuous orbit through the frozen chaos, moving between large suspended train cars and passing close to sharp metal fragments while the man remains visible. 7-15s: [Dynamic tracking shot as time resumes] At the 7-second mark time snaps back to forward motion with a slight realistic temporal residue. Some outer debris shows a very subtle reverse lag before continuing. The man has used the frozen moment to dive behind a concrete barrier. When motion resumes, several large pieces of metal miss him. He stays low and runs toward an open warehouse as debris finally crashes down. The camera tracks with him through the dust. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, hyper-realistic rigid body destruction with correct material properties (metal, wood, train parts), perfect frozen mid-air physics, seamless transition from freeze to resume, natural dust interaction, heavy cinematic motion blur on fast debris, stable character performance, movie-level practical destruction VFX quality.
0:15

Two cinematic prompts for Seedance 2.0 right here 👇 [STYLE + CAMERA + ATMOSPHERE] Ultra-photorealistic cinematic 15-second action sequence from a grounded action thriller film. Nighttime at an abandoned industrial train yard. Old trains, rusted tracks, broken platforms and warehouses. Strong moonlight and industrial lighting. The visual language is gritty and realistic with hyper-realistic practical destruction. Camera is handheld-dynamic with urgent moves. [IMAGE REFERENCES] No reference images provided. Generate a fully consistent lead character: a male fugitive in his mid-30s, short hair, intense expression. He wears a dark jacket, jeans and boots. Maintain exact same appearance and clothing throughout the clip. [TIMELINE SECOND BY SECOND] 0-3s: [Dynamic handheld tracking shot from side-rear] The man runs across the train yard as a freight train derails violently after hitting a collapsed section of track. Massive train cars tip over and crash into the ground, sending metal, wood and cargo flying. The camera tracks him urgently as the destruction spreads. 3-7s: [Slow continuous orbiting camera moving around the frozen destruction] At the peak of the derailment, everything freezes in perfect realistic physics. Huge train cars, twisted metal, wooden debris and clouds of dust hang motionless in the air with accurate weight and trajectories. The camera performs a smooth, continuous orbit through the frozen chaos, moving between large suspended train cars and passing close to sharp metal fragments while the man remains visible. 7-15s: [Dynamic tracking shot as time resumes] At the 7-second mark time snaps back to forward motion with a slight realistic temporal residue. Some outer debris shows a very subtle reverse lag before continuing. The man has used the frozen moment to dive behind a concrete barrier. When motion resumes, several large pieces of metal miss him. He stays low and runs toward an open warehouse as debris finally crashes down. The camera tracks with him through the dust. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, hyper-realistic rigid body destruction with correct material properties (metal, wood, train parts), perfect frozen mid-air physics, seamless transition from freeze to resume, natural dust interaction, heavy cinematic motion blur on fast debris, stable character performance, movie-level practical destruction VFX quality.

TechHalla

16,274 просмотров • 1 месяц назад

⚠️ LEAKED FOOTAGE OF THE FIRST REAL MEDBED UNIT ⚠️ THE TECHNOLOGY THEY HID FROM HUMANITY FOR DECADES IS NO LONGER IN THE DARK. THIS DEVICE WAS RESERVED ONLY FOR ELITES, BLACK PROJECT OPERATIVES, AND BILLIONAIRE FAMILIES — WHILE THE WORLD SUFFERED. THEY USED IT TO REGENERATE ORGANS. THEY USED IT TO ERASE DISEASE. THEY USED IT TO EXTEND THEIR LIVES. AND THEY NEVER INTENDED TO SHARE IT WITH YOU. BUT THAT ERA IS OVER. THE TRUMP ADMINISTRATION FORCED OPEN THE CLASSIFIED PROGRAMS, BROKE THE CONTRACTS OF BIG PHARMA, AND EXPOSED THE TECHNOLOGY DESIGNED TO HEAL THE WORLD. WHAT YOU SEE IN THIS VIDEO IS NOT A CONCEPT — IT IS A WORKING MEDBED. A QUANTUM HEALING PLATFORM CAPABLE OF CELL REPAIR, DNA RESTORATION, AND FULL-BODY REGENERATION AT THE ATOMIC LEVEL. THIS IS THE TECHNOLOGY THAT WILL END THE AGE OF DISEASE. THIS IS THE MOMENT HUMANITY TAKES BACK WHAT WAS STOLEN. THE MEDBED ERA BEGINS NOW. RESERVATIONS ARE OPEN FOR THE FIRST PUBLIC ACCESS WINDOW. THE FUTURE OF HEALING IS NO LONGER SECRET. IT IS YOURS. FOLLOW ME, NEXT DROP WILL BE SHOCKING.
0:14

⚠️ LEAKED FOOTAGE OF THE FIRST REAL MEDBED UNIT ⚠️ THE TECHNOLOGY THEY HID FROM HUMANITY FOR DECADES IS NO LONGER IN THE DARK. THIS DEVICE WAS RESERVED ONLY FOR ELITES, BLACK PROJECT OPERATIVES, AND BILLIONAIRE FAMILIES — WHILE THE WORLD SUFFERED. THEY USED IT TO REGENERATE ORGANS. THEY USED IT TO ERASE DISEASE. THEY USED IT TO EXTEND THEIR LIVES. AND THEY NEVER INTENDED TO SHARE IT WITH YOU. BUT THAT ERA IS OVER. THE TRUMP ADMINISTRATION FORCED OPEN THE CLASSIFIED PROGRAMS, BROKE THE CONTRACTS OF BIG PHARMA, AND EXPOSED THE TECHNOLOGY DESIGNED TO HEAL THE WORLD. WHAT YOU SEE IN THIS VIDEO IS NOT A CONCEPT — IT IS A WORKING MEDBED. A QUANTUM HEALING PLATFORM CAPABLE OF CELL REPAIR, DNA RESTORATION, AND FULL-BODY REGENERATION AT THE ATOMIC LEVEL. THIS IS THE TECHNOLOGY THAT WILL END THE AGE OF DISEASE. THIS IS THE MOMENT HUMANITY TAKES BACK WHAT WAS STOLEN. THE MEDBED ERA BEGINS NOW. RESERVATIONS ARE OPEN FOR THE FIRST PUBLIC ACCESS WINDOW. THE FUTURE OF HEALING IS NO LONGER SECRET. IT IS YOURS. FOLLOW ME, NEXT DROP WILL BE SHOCKING.

Q The Storm Rider

1,027,338 просмотров • 3 месяцев назад

I experimented a lot with the prompt until I landed on this version😎 Only face reference + exact 15-second audio. Generated in Higgsfield AI 🧩 Seedance 2.0. Small tip: when uploading reference audio, cut it precisely to whole seconds (6, 8, 10, 15…) with no leftover tails 🎵 Prompt: A cinematic 15-second music video shot in a dark modern dance studio with smooth grey reflective floor, black walls and horizontal neon light tubes. Continuous dynamic camera movement, mostly medium shots and close-ups, never too wide. The woman is constantly moving, no freezes or static poses. Main subject: young woman with messy medium-length wavy brown hair with bangs partially covering her face, freckles on nose and cheeks, blue-grey eyes, full glossy lips. She wears a tight black fishnet bodysuit. She is singing the entire time with clear, precise lip-sync, mouth actively moving, intense emotional expression. Lyrics: "Ye! Ye! You keep a box of names / In the drawer by your bed / Polaroids and ticket stubs / Stuffed under the red / You never take one thing / You take the whole last spark / Leave a little thumbprint / On every private heart". 0-2s: Tight medium close-up. She leans her upper body back, head tilting, singing passionately with strong lip-sync, hair falling over her face, body arching, one hand sliding across her chest. 2-4s: Camera slowly pushes in and circles. She comes out of the deep arch, torso still bent forward, hands on her thighs, lifting her head and looking straight into camera while singing with aggressive lip-sync. Three male dancers of different appearances (different ethnicities, hair styles and builds) wearing black tank tops and black wide pants are already close around her, moving with her in low tense postures. The other two men are visible at the edges of the frame, approaching. 4-7s: Medium close-up. She drops lower, body still in constant motion, hair swinging, singing intensely with clear mouth movements, sharp head turns, eyes locked on camera. Male dancers stay close, their hands lightly touching her as they move together. 8-12s: Medium shot with slow camera drift. Exactly five male dancers of completely different appearances, all wearing black tank tops and black wide pants, surround her tightly on the floor in a dense, intertwined formation. She is in the center, body still moving, upper body rising and shifting, singing with strong lip-sync. All five men move subtly with her, never static. The men never fully obscure her body. 13-15s: Dynamic medium shot. The five diverse male dancers lift her into the air in a powerful deep backbend. Her body is fully extended and arched, head thrown back, still singing with clear lip-sync. While holding her they gently rock her up and down in time with the beat. The camera starts from a clear side view of her arched body and smoothly transitions to a frontal view of her face. At the end they lower her smoothly onto her feet; she lands and immediately continues singing as the five men stay low on the floor around her. The men never fully obscure her. High fashion dance energy, sweaty skin, sharp timing, continuous fluid motion of the woman, priority on accurate lip-sync in every frame. Rules: no hand morphing, no body distortions, clean stable anatomy, fingers and hands remain consistent and natural throughout the entire video. #AIvideo #AIMusicVideo #AIFilmmaking
0:15

I experimented a lot with the prompt until I landed on this version😎 Only face reference + exact 15-second audio. Generated in Higgsfield AI 🧩 Seedance 2.0. Small tip: when uploading reference audio, cut it precisely to whole seconds (6, 8, 10, 15…) with no leftover tails 🎵 Prompt: A cinematic 15-second music video shot in a dark modern dance studio with smooth grey reflective floor, black walls and horizontal neon light tubes. Continuous dynamic camera movement, mostly medium shots and close-ups, never too wide. The woman is constantly moving, no freezes or static poses. Main subject: young woman with messy medium-length wavy brown hair with bangs partially covering her face, freckles on nose and cheeks, blue-grey eyes, full glossy lips. She wears a tight black fishnet bodysuit. She is singing the entire time with clear, precise lip-sync, mouth actively moving, intense emotional expression. Lyrics: "Ye! Ye! You keep a box of names / In the drawer by your bed / Polaroids and ticket stubs / Stuffed under the red / You never take one thing / You take the whole last spark / Leave a little thumbprint / On every private heart". 0-2s: Tight medium close-up. She leans her upper body back, head tilting, singing passionately with strong lip-sync, hair falling over her face, body arching, one hand sliding across her chest. 2-4s: Camera slowly pushes in and circles. She comes out of the deep arch, torso still bent forward, hands on her thighs, lifting her head and looking straight into camera while singing with aggressive lip-sync. Three male dancers of different appearances (different ethnicities, hair styles and builds) wearing black tank tops and black wide pants are already close around her, moving with her in low tense postures. The other two men are visible at the edges of the frame, approaching. 4-7s: Medium close-up. She drops lower, body still in constant motion, hair swinging, singing intensely with clear mouth movements, sharp head turns, eyes locked on camera. Male dancers stay close, their hands lightly touching her as they move together. 8-12s: Medium shot with slow camera drift. Exactly five male dancers of completely different appearances, all wearing black tank tops and black wide pants, surround her tightly on the floor in a dense, intertwined formation. She is in the center, body still moving, upper body rising and shifting, singing with strong lip-sync. All five men move subtly with her, never static. The men never fully obscure her body. 13-15s: Dynamic medium shot. The five diverse male dancers lift her into the air in a powerful deep backbend. Her body is fully extended and arched, head thrown back, still singing with clear lip-sync. While holding her they gently rock her up and down in time with the beat. The camera starts from a clear side view of her arched body and smoothly transitions to a frontal view of her face. At the end they lower her smoothly onto her feet; she lands and immediately continues singing as the five men stay low on the floor around her. The men never fully obscure her. High fashion dance energy, sweaty skin, sharp timing, continuous fluid motion of the woman, priority on accurate lip-sync in every frame. Rules: no hand morphing, no body distortions, clean stable anatomy, fingers and hands remain consistent and natural throughout the entire video. #AIvideo #AIMusicVideo #AIFilmmaking

Kiber Alla

65,308 просмотров • 29 дней назад

✨ Grok Imagine Video is now live on Photo AI It's hard to explain how impressive this is because of the speed that xAI got itself from literally nothing to the top of the leaderboards Six months ago Grok's video model was a joke, it wasn't even close to any of the video models out there, it looked cartoony and wasn't there and nobody took it seriously Now it's here and it's instantly the #1 video model out there now, it shot above Kling (which I used before on Photo AI and usually my favorite) and above Runway Gen 4.5 which was just launched 6 days ago! Mmore importantly it's now above xAI's biggest competitors' models: Google's Veo 3 and OpenAI's Sora 2 Being the best video model doesn't mean it's flawless: video is incredibly hard and actually because it looks so realistic now when it does make a mistakes it's even funnier One thing I noticed is that it still has a hard time with is voice, it does it well for a majority of the video but then slips up and produces unintelligible blabbering (which is really funny to hear) in both English (video 1: "it's where I find my naim", what's a "naim"?), and tested it in Portuguese too (video 3 at the end is unintelligible Portuguese I believe) In many ways Grok Imagine Video also reminds me of Sora, it has that weird but funny Sora conversation style But guys it's REALLY really really really close to getting perfect, we're so close to having full video productions being to be able done in AI, actually you already can if you just cut out the bad parts already Very exciting and I'm grateful I can experience this
0:15

✨ Grok Imagine Video is now live on Photo AI It's hard to explain how impressive this is because of the speed that xAI got itself from literally nothing to the top of the leaderboards Six months ago Grok's video model was a joke, it wasn't even close to any of the video models out there, it looked cartoony and wasn't there and nobody took it seriously Now it's here and it's instantly the #1 video model out there now, it shot above Kling (which I used before on Photo AI and usually my favorite) and above Runway Gen 4.5 which was just launched 6 days ago! Mmore importantly it's now above xAI's biggest competitors' models: Google's Veo 3 and OpenAI's Sora 2 Being the best video model doesn't mean it's flawless: video is incredibly hard and actually because it looks so realistic now when it does make a mistakes it's even funnier One thing I noticed is that it still has a hard time with is voice, it does it well for a majority of the video but then slips up and produces unintelligible blabbering (which is really funny to hear) in both English (video 1: "it's where I find my naim", what's a "naim"?), and tested it in Portuguese too (video 3 at the end is unintelligible Portuguese I believe) In many ways Grok Imagine Video also reminds me of Sora, it has that weird but funny Sora conversation style But guys it's REALLY really really really close to getting perfect, we're so close to having full video productions being to be able done in AI, actually you already can if you just cut out the bad parts already Very exciting and I'm grateful I can experience this

@levelsio

195,023 просмотров • 6 месяцев назад

📢 Gray Zone Warfare: Night Ops World Premiere Reveal Sept 6th After an exclusive first look at the Day/Night Cycle at Gamescom, we're bringing you the full Night Ops presentation this Friday, September 6th, at 1 PM ET (10 AM PT, 6 PM BST, 7 PM CEST) on Twitch. Join us live for a brief overview of what’s to come later this year. While it’s still a work in progress, the time has come to start sharing what we’ve been working on. Our first major update is designed for players seeking a deeper, more immersive combat experience. Night Ops will reward tactical prowess and adaptability, introducing new tools, highly requested features, and quality-of-life improvements that elevate gameplay and keep you coming back for more, both in daytime and nighttime gameplay. Creators and press will share their first impressions on the same day, offering valuable insights into the update. But the heart of our excitement lies in engaging directly with our community—your feedback and enthusiasm are what drive us. We look forward to your reactions and discussions as we gear up for the launch of #GZW: Night Ops. We're committed to keeping the community updated right up to the launch of our first major update and beyond. The Night Ops presentation is just a preview—future devlogs, posts, and streams will reveal more. Get ready for the first live reveal of #GrayZoneWarfare: #NightOps, this coming Friday on Twitch:
0:12

📢 Gray Zone Warfare: Night Ops World Premiere Reveal Sept 6th After an exclusive first look at the Day/Night Cycle at Gamescom, we're bringing you the full Night Ops presentation this Friday, September 6th, at 1 PM ET (10 AM PT, 6 PM BST, 7 PM CEST) on Twitch. Join us live for a brief overview of what’s to come later this year. While it’s still a work in progress, the time has come to start sharing what we’ve been working on. Our first major update is designed for players seeking a deeper, more immersive combat experience. Night Ops will reward tactical prowess and adaptability, introducing new tools, highly requested features, and quality-of-life improvements that elevate gameplay and keep you coming back for more, both in daytime and nighttime gameplay. Creators and press will share their first impressions on the same day, offering valuable insights into the update. But the heart of our excitement lies in engaging directly with our community—your feedback and enthusiasm are what drive us. We look forward to your reactions and discussions as we gear up for the launch of #GZW: Night Ops. We're committed to keeping the community updated right up to the launch of our first major update and beyond. The Night Ops presentation is just a preview—future devlogs, posts, and streams will reveal more. Get ready for the first live reveal of #GrayZoneWarfare: #NightOps, this coming Friday on Twitch:

Gray Zone Warfare 🔜 Gamescom

71,473 просмотров • 1 год назад

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.
0:13

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.

Shade

20,190 просмотров • 2 месяцев назад