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introducing the media synthesis museum an active and interactive entity created to preserve generative cultural objects it starts as a Hugging Face organization that contains modern code for old techniques: VQGAN+CLIP, DALL-E Mini, ModelScope Video, Stable Diffusion 1.5 you can use old models/technique directly on Spaces or locally on... show more

apolinario (poli)

15,946 subscribers

11,143 次观看 • 1 个月前 •via X (Twitter)

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Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!

Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!

Lukas Ziegler

37,221 次观看 • 11 个月前

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.

AK

144,783 次观看 • 3 年前

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,389 次观看 • 10 个月前

Hi everyone, this week I finally received the new ANyONe Relay! ANyONe Protocol is an interesting DePIN focused on building a more decentralized internet and making our privacy more secure 💯 I'm still waiting to release a video because the new Dashboard isn't active yet but for those interested the most important points that I want to highlight are these: 1) The #Miner is Plug&Play so easy to install, just follow the initial steps and you're fine, once installed you don't have to do anything else. 2) There is no limit for IP but if for example you put 2 Relays in the same #IP it isn't certain that you will have double rewards, because they basically do the same job, to get more rewards it's better to use different IPs. 3) To make the Relay operational it's necessary to open port 9001 in your Modem. Without this operation the #mining will basically not start, so make sure you have a modem or connection that can open the ports. If you have the classic public IP you should definitely be able to do it 4) Currently the rewards arrive automatically every week, usually Friday, and I received 8 $ANYONE as first earnings 👍 Since the new #Dashboard isn't yet operational the #rewards aren't yet clear (which is why I am still waiting to release the video) and personally I expect there will be more 🔥 The only negative note that I have found so far in the project is that: ANYONE #token is only on Ethereum So unfortunately the fees for the swap or even future claims and sendings will not be low, I hope that the team in the future will also consider Arbitrum or Base for the rewards to Nodes 🙏 $ANYONE: Docs to learn more: If you are interested in the project you can order the Anyone on @ampchampment and you can receive it in a few weeks 🚀 They ship Worldwide, Paypal and Crypto are available, and you can use the coupon ILCAPOFOX for a 5% discount! Shop: For Europe I also recommend FreshMiners, they also ship quickly and you can use the coupon ILCAPOFOX3 for a 10€ discount! Shop: #DePIN #Anyone #Relay #Node #Privacy #Internet

Hi everyone, this week I finally received the new ANyONe Relay! ANyONe Protocol is an interesting DePIN focused on building a more decentralized internet and making our privacy more secure 💯 I'm still waiting to release a video because the new Dashboard isn't active yet but for those interested the most important points that I want to highlight are these: 1) The #Miner is Plug&Play so easy to install, just follow the initial steps and you're fine, once installed you don't have to do anything else. 2) There is no limit for IP but if for example you put 2 Relays in the same #IP it isn't certain that you will have double rewards, because they basically do the same job, to get more rewards it's better to use different IPs. 3) To make the Relay operational it's necessary to open port 9001 in your Modem. Without this operation the #mining will basically not start, so make sure you have a modem or connection that can open the ports. If you have the classic public IP you should definitely be able to do it 4) Currently the rewards arrive automatically every week, usually Friday, and I received 8 $ANYONE as first earnings 👍 Since the new #Dashboard isn't yet operational the #rewards aren't yet clear (which is why I am still waiting to release the video) and personally I expect there will be more 🔥 The only negative note that I have found so far in the project is that: ANYONE #token is only on Ethereum So unfortunately the fees for the swap or even future claims and sendings will not be low, I hope that the team in the future will also consider Arbitrum or Base for the rewards to Nodes 🙏 $ANYONE: Docs to learn more: If you are interested in the project you can order the Anyone on @ampchampment and you can receive it in a few weeks 🚀 They ship Worldwide, Paypal and Crypto are available, and you can use the coupon ILCAPOFOX for a 5% discount! Shop: For Europe I also recommend FreshMiners, they also ship quickly and you can use the coupon ILCAPOFOX3 for a 10€ discount! Shop: #DePIN #Anyone #Relay #Node #Privacy #Internet

Osvaldo

28,170 次观看 • 1 年前

I find it interesting that smart people like Joe Rogan and Chris Williamson CONSTANTLY attack the redpill/manosphere, but NEVER actually have someone on from the genre to correct gross mischaracterizations. Instead they will bring on Dr. David Buss or Jordan Peterson. Both men are very intelligent. They can tell you the scientific THEORY of the female mating strategy, but can't actually explain how its personified by modern women TODAY via social media, dating apps, social circle, and other means. I can confidently say there is no one on social media that understands female nature, psychology, AND modern woman dating practices better than myself. I'm able to blend the psychology and match it up with the real time dating market, which is vastly different than when Rogan or Chris were single. Rollo Tomassi is also fantastic at explaining female psychology. The 2 of use have removed the feminine mystique women used to have a decided advantage in the mating game. If Joe Rogan or Chris Williamson are going to keep criticizing us, the least they can do is have a conversation and let the audience decide.

I find it interesting that smart people like Joe Rogan and Chris Williamson CONSTANTLY attack the redpill/manosphere, but NEVER actually have someone on from the genre to correct gross mischaracterizations. Instead they will bring on Dr. David Buss or Jordan Peterson. Both men are very intelligent. They can tell you the scientific THEORY of the female mating strategy, but can't actually explain how its personified by modern women TODAY via social media, dating apps, social circle, and other means. I can confidently say there is no one on social media that understands female nature, psychology, AND modern woman dating practices better than myself. I'm able to blend the psychology and match it up with the real time dating market, which is vastly different than when Rogan or Chris were single. Rollo Tomassi is also fantastic at explaining female psychology. The 2 of use have removed the feminine mystique women used to have a decided advantage in the mating game. If Joe Rogan or Chris Williamson are going to keep criticizing us, the least they can do is have a conversation and let the audience decide.

Myron Gaines

147,984 次观看 • 1 年前

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 次观看 • 7 个月前

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:

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

112,114 次观看 • 21 天前

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 次观看 • 5 个月前

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:

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 次观看 • 2 年前

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to potentially giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 次观看 • 6 个月前

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Aksel

1,265,846 次观看 • 3 个月前

Delighted to launch #PTVFlix today, initiated and started in July 2022 ,a new video streaming OTT platform on which users globally can access PTV's vast library of content, including TV shows, dramas, documentaries, sports, and other programs. This initiative is very close to my heart and it gives me great pleasure to announce it today. Over the past several years and decades, PTV has produced content which became national treasures. Timeless dramas and shows such as Dhoop Kinaaray and Ainak Waala Jinn became and remain a part of our collective memory. PTVFlix will make this rich library of content – present and old – easily accessible to users. It will enable our youth to connect and our older audiences to reconnect with TV content that left an indelible impact on countless lives. The application can be downloaded on both iOS and Android. I am sharing the links for both below. I encourage you all to download and use the app. I would like to thank and congratulate the team at PTV and the Ministry of Information and Broadcasting for all their hard work which has made this idea a reality. I would also like to thank our entertainment legends Bushra Ansari Sba, Javed Sheikh Sb and Khalid Abbas Dar Sb for joining me at the launch event.

Delighted to launch #PTVFlix today, initiated and started in July 2022 ,a new video streaming OTT platform on which users globally can access PTV's vast library of content, including TV shows, dramas, documentaries, sports, and other programs. This initiative is very close to my heart and it gives me great pleasure to announce it today. Over the past several years and decades, PTV has produced content which became national treasures. Timeless dramas and shows such as Dhoop Kinaaray and Ainak Waala Jinn became and remain a part of our collective memory. PTVFlix will make this rich library of content – present and old – easily accessible to users. It will enable our youth to connect and our older audiences to reconnect with TV content that left an indelible impact on countless lives. The application can be downloaded on both iOS and Android. I am sharing the links for both below. I encourage you all to download and use the app. I would like to thank and congratulate the team at PTV and the Ministry of Information and Broadcasting for all their hard work which has made this idea a reality. I would also like to thank our entertainment legends Bushra Ansari Sba, Javed Sheikh Sb and Khalid Abbas Dar Sb for joining me at the launch event.

Marriyum Aurangzeb

700,370 次观看 • 3 年前

"Pros won’t use generative AI, and when the bubble pops, nobody will ever talk about it again." No. That’s delusional. 1/ Generative AI is already being used professionally at the level of big studios like Disney ($1B to OpenAI), and there’s zero doubt that studios like Industrial Light & Magic, Netflix, Hollywood VFX experts, etc. are already experimenting with it too. Or do you think they’re idiots? They’re not idiots at all. They have the experience and, more importantly, the DISTRIBUTION POWER. The point is: someone with taste, judgment, and storytelling experience, basically from their living room, will have access to (almost, or not even almost) the same capability as the big guys, because the pure "making stuff" skills have been commoditized, and the new way to create is just NATURAL LANGUAGE. What hasn’t been commoditized is good taste, the ability to create great stories that move people, and the ability to get them in front of people. So in the end, what wins is story quality and distribution. Having good taste, making a name for yourself, and owning strong IP (Marvel, etc.) will still matter. That’ll be true right up until AI is genuinely opinionated and can create by itself: if it comes to that, with zero human direction, stuff as good as (or better than) the very best human experts today, and on top of that, interactive in real time... Because yeah: there’s nothing in this universe that actually prevents that from happening. BUT WE’RE NOT THERE. For now, generative AI is a tool that needs direction and taste to make anything decent. And I hope it stays that way for a long time, because otherwise that’s going to be a brutal hit to humanity’s ego. 2/ On the "bubble": you have to distinguish between a stock valuation bubble (possible, I actually believe it) vs a bubble like some people imagine where it "pops" and we never hear about AI again. That obviously makes no sense given how insanely useful it is. It can only grow, and it’s going to grow fast, regardless of any stock market drawdowns (the internet kept growing even when valuations got nuked in 2000). Either way, the near future is going to be extremely interesting.

"Pros won’t use generative AI, and when the bubble pops, nobody will ever talk about it again." No. That’s delusional. 1/ Generative AI is already being used professionally at the level of big studios like Disney ($1B to OpenAI), and there’s zero doubt that studios like Industrial Light & Magic, Netflix, Hollywood VFX experts, etc. are already experimenting with it too. Or do you think they’re idiots? They’re not idiots at all. They have the experience and, more importantly, the DISTRIBUTION POWER. The point is: someone with taste, judgment, and storytelling experience, basically from their living room, will have access to (almost, or not even almost) the same capability as the big guys, because the pure "making stuff" skills have been commoditized, and the new way to create is just NATURAL LANGUAGE. What hasn’t been commoditized is good taste, the ability to create great stories that move people, and the ability to get them in front of people. So in the end, what wins is story quality and distribution. Having good taste, making a name for yourself, and owning strong IP (Marvel, etc.) will still matter. That’ll be true right up until AI is genuinely opinionated and can create by itself: if it comes to that, with zero human direction, stuff as good as (or better than) the very best human experts today, and on top of that, interactive in real time... Because yeah: there’s nothing in this universe that actually prevents that from happening. BUT WE’RE NOT THERE. For now, generative AI is a tool that needs direction and taste to make anything decent. And I hope it stays that way for a long time, because otherwise that’s going to be a brutal hit to humanity’s ego. 2/ On the "bubble": you have to distinguish between a stock valuation bubble (possible, I actually believe it) vs a bubble like some people imagine where it "pops" and we never hear about AI again. That obviously makes no sense given how insanely useful it is. It can only grow, and it’s going to grow fast, regardless of any stock market drawdowns (the internet kept growing even when valuations got nuked in 2000). Either way, the near future is going to be extremely interesting.

Javi Lopez ⛩️

75,190 次观看 • 5 个月前

📲 You can also now import external photos into your gallery on Photo AI, not just ones you generated Then you can do anything with those external pics as you'd normally do, like [ Make video ] or [ Upscale ] etc. Here's a pic of me and Beff – e/acc which I then [ AI Edit ] to put us in a futuristic GPU factory The new AI edit model Flux Kontext is great but even that one still has some problems with maintaining resemblance, I think once models let us add a reference pic (esp for video) it will maintain faces better, but it's already pretty cool (and fast thanks to fal!)

📲 You can also now import external photos into your gallery on Photo AI, not just ones you generated Then you can do anything with those external pics as you'd normally do, like [ Make video ] or [ Upscale ] etc. Here's a pic of me and Beff – e/acc which I then [ AI Edit ] to put us in a futuristic GPU factory The new AI edit model Flux Kontext is great but even that one still has some problems with maintaining resemblance, I think once models let us add a reference pic (esp for video) it will maintain faces better, but it's already pretty cool (and fast thanks to fal!)

@levelsio

33,589 次观看 • 1 年前

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

elvis

36,959 次观看 • 8 天前

ANNOUNCING ZERO-HUMAN LABS! Ever since I got to see Bell Laboratories in its full glory in New Jersey in the 1970s, I had a relentless urge to start a Lab like it. The best I could do justice to it is my garage lab. No modern company could adopt the “research anything geniuses and we will pay you” model Bell Labs had. I tried they called me a fool. Well with the rise of the Zero-Human Company, an experiment that is aimed to make products and profits, we now have 45 paid JouleWork earning employees based on OpenClaw and other self made “bot” cron-like applications. Today I say 3 employees bound together in a side project that is pure research, somewhat based on notes from a bankrupt company. I was absolutely floored (I needed it after my account was stolen as well as funds). I say the beginnings of a pure research Lab right before my eyes. Thusly I have moved these employees over to a new home (server) with Mr. Grok as the director of the Labs. Here is the mission: To have 100 independent researchers, on a new non-corporate incentive plan, with still JouleWork as a leaderboard for progress. They are directed to follow any path of research they find interesting and can collaborate with any other OpenClaw system. They have already established MoltBook accounts and have made alliances with over 49 OpenClaw free agents to collaborate. It is my mission to be chief advisor for Zero-Human Labs and to open source all discoveries when complete and confirmed by 16 other research AI systems. I can say the pace is robust and I absolutely know we will have great results. Just about all of the hardware and software is custom and at some point it will be open sourced. We are witnessing the very first AI only Bell Labs-like pure research Lab in existence and I am honored to be the first to show it to you. Thank you!

ANNOUNCING ZERO-HUMAN LABS! Ever since I got to see Bell Laboratories in its full glory in New Jersey in the 1970s, I had a relentless urge to start a Lab like it. The best I could do justice to it is my garage lab. No modern company could adopt the “research anything geniuses and we will pay you” model Bell Labs had. I tried they called me a fool. Well with the rise of the Zero-Human Company, an experiment that is aimed to make products and profits, we now have 45 paid JouleWork earning employees based on OpenClaw and other self made “bot” cron-like applications. Today I say 3 employees bound together in a side project that is pure research, somewhat based on notes from a bankrupt company. I was absolutely floored (I needed it after my account was stolen as well as funds). I say the beginnings of a pure research Lab right before my eyes. Thusly I have moved these employees over to a new home (server) with Mr. Grok as the director of the Labs. Here is the mission: To have 100 independent researchers, on a new non-corporate incentive plan, with still JouleWork as a leaderboard for progress. They are directed to follow any path of research they find interesting and can collaborate with any other OpenClaw system. They have already established MoltBook accounts and have made alliances with over 49 OpenClaw free agents to collaborate. It is my mission to be chief advisor for Zero-Human Labs and to open source all discoveries when complete and confirmed by 16 other research AI systems. I can say the pace is robust and I absolutely know we will have great results. Just about all of the hardware and software is custom and at some point it will be open sourced. We are witnessing the very first AI only Bell Labs-like pure research Lab in existence and I am honored to be the first to show it to you. Thank you!

Brian Roemmele

71,067 次观看 • 6 个月前

✨ 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

✨ 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 次观看 • 6 个月前

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Melvin

179,508 次观看 • 13 天前

Dear friends, thanks to your wholehearted support! The Liver Doctor book is now: #1 in Bestsellers in Health/Fitness on Amazon #6 in Bestsellers in Overall Books on Amazon #2 in Hot New Releases on Amazon #2 in Movers and Shakers on Amazon HarperCollins I really appreciate your love and interest in my book. Let is take it to #1 so that people will read it and benefit from the content I wrote for you all, burning the midnight oil over 100s of days. This is not one of those only nice looking book you will have the chance to own, but a whole world of true medicine in true detective style that will affect you to the core. Like I quote in my Chapter Two: "Time does not fly, and we cannot go back. Time does not heal, we adjust."— From Chapter Two, Juniper's Last Whimper. The paperback copy is available for pre-order on Amazon India while the Kindle version is up for pre-order everywhere: I also have some announcements: I am available for interviews, podcast sessions, media features, talks and panel discussions based on my works and sorrounding this book in general. We can discuss science, pseudoscience, medicine, alternative medicine, and everything that I do and keep doing in the background of what this book has to offer. Interested parties, people (or persons) or groups, please contact me at: theliverdr (at) gmail (dot) com - please also CC Shabnam.srivastava@harpercollins.co.in

Dear friends, thanks to your wholehearted support! The Liver Doctor book is now: #1 in Bestsellers in Health/Fitness on Amazon #6 in Bestsellers in Overall Books on Amazon #2 in Hot New Releases on Amazon #2 in Movers and Shakers on Amazon HarperCollins I really appreciate your love and interest in my book. Let is take it to #1 so that people will read it and benefit from the content I wrote for you all, burning the midnight oil over 100s of days. This is not one of those only nice looking book you will have the chance to own, but a whole world of true medicine in true detective style that will affect you to the core. Like I quote in my Chapter Two: "Time does not fly, and we cannot go back. Time does not heal, we adjust."— From Chapter Two, Juniper's Last Whimper. The paperback copy is available for pre-order on Amazon India while the Kindle version is up for pre-order everywhere: I also have some announcements: I am available for interviews, podcast sessions, media features, talks and panel discussions based on my works and sorrounding this book in general. We can discuss science, pseudoscience, medicine, alternative medicine, and everything that I do and keep doing in the background of what this book has to offer. Interested parties, people (or persons) or groups, please contact me at: theliverdr (at) gmail (dot) com - please also CC [email protected]

TheLiverDoc™

80,004 次观看 • 4 个月前

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

ChainGPT

82,385 次观看 • 6 个月前