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 modern hardware/software, without the old "colab notebook" dependency rot the idea is to really preserve and make accessible those artifacts and aesthetics - both open source. In the future, we hope to also have also historically relevant closed source like DALL-E 1 and DALL-E 2 from OpenAI, older Midjourney models, older Runway apps/techniques/models (cc Cristóbal Valenzuela David Sam Altman)show more

New open-source agent harness just landed! I got early... access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.show more

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 🤗!show more

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:show more

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.show more

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 #Internetshow more

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.show more

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 #AIshow more

MiniMax H3 Instead of sharing the prompts for each... of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."show more

Announcing Centaur 2.0! Centaur is frontier, agentic infrastructure that... you own. Centaur is like Claude Tag, but open source and on steroids. Centaur 2.0 can connect to everything you have access to, and can reason over it next to where you work, either in Slack, Discord, Teams or in your local Codex or Claude Code via an MCP. But context isn't useful if it can be accessed by anyone, so we rebuilt Centaur from the ground up for security. Centaur obviously doesn't have access to secrets because we're leveraging egress proxies. Centaur 2.0 takes that a step further enabling administrators to configure who can access what from where. This means that Centaur can have access to private information that you normally wouldn't feel comfortable giving it access to (e.g. DMs or sensitive channels and docs) but only expose it to authorized principals. It also means you can add Centaur to external channels and leverage it as a virtual colleague that doesn't live only in your Slack, for example, but also your Slack Connect channels! This is extremely powerful as we start moving to a world where agents cross organizational boundaries. Also in case anyone's wondering, yes we rewrote it in Rust! Centaur is now way way more stable at durably executing threads, and its workflow engine is now based on Absurd. We have been operating Centaur since January, officially launched and Open Sourced it in May, and now we're full speed towards making it the #1 open source agentic infrastructure. To succeed at that, I'm thrilled to welcome Matthew Slipper to the Paradigm team who will be leading all our Applied AI work, while continuing to maintain and extend Iron Proxy as the leading secure secret access for agents. Welcome Matt, it's an honor after all these years of knowing you! Read the full blogpost below, and apply to join our team!show more

As always everyone is blind staring at the progress... of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)show more

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:show more

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 ?show more

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:show more

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.show more

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.show more

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.show more

"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.show more

Americans have terrible cultural constructs around aging. For instance,... I notice a trend amongst my friends entering their 30s & 40s where I'll invite them to do a fun, adventurous thing, and some will say things like, "Sounds amazing, but I feel too old for that kind of thing now." Huh? In Spain and Berlin, I saw mothers bringing their babies to bars and concerts to hang out with their adult friends. Meanwhile, the people I party the most with these days are a crew of dancers mostly over 50. "They must be societal dropouts or something" you might be thinking. But the opposite is true. One is CEO of a perfume company. Another is a doctor and professor at NYU. Another is a travel show host who just came with me to a 7am rave yesterday. The list continues: advertising executives, restaurant owners, etc. And most have kids. These people are still doing adulthood. They just haven't given up their vital spark. Things seem especially bad for older American women. There's this idea that you age out of being beautiful, that beauty is only for the young. This is bs. The older dancer women I'm talking about are still smokin', and I see them get hit on. Granted, there may be certain *kinds* of beauty that are for the young. But we generally under-appreciate how magnetic an older woman can be...if she's able to internally defy the American idea that older women are unattractive. In France, it seems like a lot of praise focuses on allure, charme, elegance, and idioms like "avoir du chien" – roughly, having a spark. None of those are indexed to youth the way "cute" or "hot" are in our culture. I gather that an aging French woman can move into a different type of attractiveness rather than just giving up on the erotic and romantic aspects of life altogether. I wonder what's wrong with us. Why do we believe that becoming older also means becoming boring and unattractive?show more

🚀 A better, faster co-folding-based binding affinity model. Predicting... how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:show more

AI token usage is up 10x in 7 months,... compounding 40%/MONTH! There is NO BUBBLE when demand is STILL accelerating And this is just OpenRouter, it doesn't count the labs direct token usage and APIs But here's what's interesting about these numbers, the demand is coming from everywhere at once US models (OpenAI, Anthropic, Google) keep growing, while Chinese open weight models (DeepSeek, Tencent, Xiaomi, Minimax) grew even faster and now drive over 60% of usage on OpenRouter Closed source and open source both compounding at the same time. This is literally the best case scenario for AI Infra investors It means both frontier model tokens and cheaper tokens have product market fit. This means the application layer is finding ways to use both and generate ROI with both types Demand for tokens IS demand for compute. This is why SpaceX is looking to build 10GW of compute by next year, because the demand is clearly here Now combine this demand set up, with NVIDIA yesterday announcing financing platforms with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion of third party capital for AI infrastructure And Jensen has said publicly he expects $3 to $4 TRILLION of AI infrastructure spend by 2030 The build out will have to continue for a lot longer than the market is expecting, that is very clear to me. Don't let this consolidation period in AI infra stocks shake you out, they will have their moment again and take their next leg higher p.s. if you want to see how im investing in this, you can track my real-time portfolio and the research of all 5 Milk Road PRO analysts with live trade notifications, and it's just $1 to try it out (insane price just to check it out). Learn more here: Good luck out there!show more