To replace animal testing with AI, we need MASSIVE... human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!show more

Eastworlds aims to build the largest dataset of high-quality... humanoid teleop data outside of China. While we believe human egocentric data will play a big role in pretraining embodied AI, robot teleop data remains the closest thing to expert demonstration.show more

Today we're dropping the "beta" tag from Adaptyv, launching... our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.show more

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository.... SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQshow more

⬛️ We are currently accelerating the incubation of GPU... Nodes into the infraX Network, with 12 H100’s currently available for operation. Despite the incubation of such immense GPU power, the infraX Platform is optimally designed to run on the least amount of computational power possible, meaning a lot of our available GPU nodes are currently sitting idle. Currently, we're utilising a single gigantic NVIDIA H100 server with 80GB of VRAM and over 220GB of RAM to run our Platform. To put that in perspective, it rivals the computational power of an adult human brain. This setup enables us to handle immense computational load and deliver high-quality AI content to our users, however we have much more in store. Our remaining, immense network of GPU units is currently being prepared for rental operations as we look to transform the corporate GPU lending sphere through our corporate GPU lending protocol. We already have many high tier Web3 Players ready for technical integration, with more approaching us daily. Through our V3 DApp we look to make these integrations publicly viewable with real time usage graphs integrated directly into our Platform, allowing for exceedingly unique viewing opportunities. $INFRAshow more

Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos... with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.show more

How can we address the scarcity of data required... for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control over coverage, complexity, and quality. More →show more

Grasps are one of the primary ways in which... we interact with and shape our environments. How can we faithfully capture human grasps with details such as hand/object shape and contact points? At #CVPR2026, we present MANUS, a method to accurately reconstruct grasps and contacts. 🧵show more

The newest version of our Almanac preprint is out,... and just in time for our demo at the Stanford AIMI Symposium 2023! Almanac is a retrieval-augmented LLM that provides up-to-date and verifiable answers to medical queries. Link: We benchmark our approach on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified & resident physicians, and demonstrate significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties. More interestingly, because the retrieved data acts as a single source of truth, we find retrieval-based LLMs to be more robust to prompt injection and manipulation! Future work will involve expanding the scope of our dataset to more specialties and multimodal settings. #Medtwitter #MedEdshow more

Over the last few months, our engineering teams have... been heads down building a high throughput, low-latency blockchain coupled with an exchange replicable across different data centers. We’ve invested thousands of developer hours designing our data infrastructure stack from scratch. We designed our own streaming indexer to serve data to our frontend in real-time, and shipped optimized memory crates to push our general shared IPC to under 100 nanoseconds. This architecture enables us to colocate our sequencers with the key sources of price discovery across all asset classes: Tokyo for crypto, New Jersey for equities, Chicago for commodities. In turn, institutional market makers colocated with GTE bare metal racks will be able to post orders with minimal roundtrip latency, resulting in the tightest spreads and pristine liquidity. We call this GTE Turbo. By optimizing the entire stack from hardware, to the software, into the networking stack, we will build a world where anyone, anywhere, can trade anything, at anytime. lfGTEshow more

Over the Reality just hit another major milestone in... the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.show more

🌠Today, we’re excited to relaunch Airtable as the AI-native... app platform, combining the magic of vibe coding business apps with real production-readiness and scalability, and embedding them with an army of agents that automate thousands of hours of work in seconds. Instead of just adding more AI capabilities to our existing platform, we treated this as a refounding moment for the company. We started with a clean-slate imagining of the ideal form factor for building apps in the agentic era. (If you want to skip all the backstory and just try it out, you can just go to All new signups get the new AI experience, and existing accounts can switch over using this link: Thread and demos below👇show more

We are well aware that the Avenged Sevenfold family... consists of many incredible musicians. That’s why we wanted to lift the veil a bit and give you all some cool tools to play with. Music stems and remixes aren’t necessarily a part of the rock and metal culture, but we felt it would be fun to see what some of you can do. We have provided all the high quality stems to “Cosmic” and we wanna hear what you can do with it. If you enjoy hearing these and creating with them, we will think about doing it with more songs. The grand prize winner will have their remix played live at our next concert in front of thousands of fans. How to Enter: • Download the audio files here: • Join our discord: • Post a link to your remix from SoundCloud in the #CosmicRemix channel before August 20th. • We will pick our top 3 remixes. Contest starts today. Rule info and prize details:show more

🚀 Introducing EgoExo Forge - built on top of... Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tunedshow more

A Letter to Our Community: The Road Ahead for... Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷show more

Can AI rewrite our human genome? ⌨️🧬 Today, we... announce the successful editing of DNA in human cells with gene editors fully designed with AI. Not only that, we've decided to freely release the molecules under the Profluent OpenCRISPR initiative. Lots to unpack👇show more

BIG NEWS. The Blockworks website has evolved: yesterday we... were the home of news, today we are the home of onchain data. Head over to the site to see for yourself, but here's a little snippet of what you can expect: 1. Sector leaderboards (chains, DEXes, borrow lend, DATs, etc...) 2. Comprehensive data dashboards protocols 3. The ability to compare pricing and onchain data easily (coming soon) We're doing this because the industry still has a gigantic data problem. As investors get more sophisticated and fundamentals driven, basic high level facts are no longer sufficient. Investors need to be able to trust the data they are seeing and go much deeper than the surface level info that's available today. Additionally, because many data providers allow companies to essentially self report, you can't trust what you are seeing. This site is our contribution to fixing that problem and to ensuring clear, accurate data for investors. Blockworks is fully dedicated to becoming the most comprehensive data company in crypto in 2026. This is the first of many, many announcements like this this year, stay tuned.show more

Exciting Milestone: Our First CEX Listing with MEXC! We’re... thrilled to announce that VentureMind AI ($VNTR) is officially listed on mexc_listings ! Choosing our first centralized exchange partner was a big decision, and we’re excited to team up with MEXC to bring $VNTR to a wider audience. This is a historic moment for us as the first AI incubator project launched on the Seedify platform to achieve a CEX listing. We couldn’t have reached this point without Seedify’s guidance, helping us navigate the journey with confidence and clarity. This partnership is just the beginning! It gives easier access to $VNTR and introduces the first onramp to purchase our token using fiat stablecoins, making it more accessible to a global audience. And we’re just getting started! Additional exchange listings are already in the works, and we can’t wait to share more milestones as we expand the reach and utility of $VNTR. To our incredible community, thank you for your unwavering support. This is only the start of an exciting journey, and we’re so grateful to have you with us every step of the way!show more

There’s a turf war in San Francisco. Fighting for... territory? Chihuahuas and labs. These are the two most common dog breeds found in the city, according to detailed data obtained by the Chronicle from San Francisco Animal Care and Control on every dog registered since 2020, a total of 50,000 pups. But depending on which neighborhood you are in, you are much more likely to see one or the other. There are also a few pockets of the city where other dogs dominate. While there’s more than enough data to give us a strong sense of the city’s dog scene, the dataset doesn’t represent every canine in San Francisco. Animal Care and Control estimated in 2018 that the dog population was between 120,000 and 150,000. The department said that not all dogs are licensed because people might not be aware of the law requiring it or don’t want to pay the fee, and there are only 11 officers who patrol the city to look for unlicensed dogs. With this bounty of doggo information, we have anointed this “Dog data week,” in which we will explore a different aspect of San Francisco’s four-pawed population each day. Today, we start with San Francisco’s great breed divide. In addition to names, colors, and breeds, the data also includes the ZIP code of every registered dog.show more

📢📢𝐍𝐞𝐑𝐒𝐞𝐦𝐛𝐥𝐞 𝐯𝟐 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞📢📢 Head captures of 7.1MP from... 16 cameras at 73fps: * More recordings (425 people) * Better color calibration * Convenient download scripts The new version of our dataset adds 156 participants for a total of 425 different people. In its entirety, the dataset provides now 65 million images from over 15 hours of diverse human facial expression performances. We improved the color consistency of the recorded images with a better color calibration procedure. As a result, 3D reconstructions with images from the NeRSemble dataset should become better and look more realistic. Finally, we made it much easier to download the recordings with our new download repository. It now just takes a single command to download all frontal hair shake videos of all participants or to download all recordings of a single participant. Check it out: Awesome work by Tobias Kirschstein Simon Giebenhain !!!show more

If you have Cancer, this is your visualization. That... is a cancerous cell reaching Apoptosis and Necrosis—they are gone. — Natural Killer (NK) cells play a the primary role in the immune system's defense against cancer. They induce cancer cell death primarily through two mechanisms: Apoptosis Necrosis Apoptosis is a programmed, non-inflammatory form of cell death, while necrosis is a more abrupt and inflammatory process. In many cases, NK cells can trigger a combination of both, leading to mixed forms of cell death. This dual capability allows NK cells to effectively target and destroy all tumor cells. Their ability to act without prior sensitization makes them vital for early cancer surveillance and curing existing cancer. One of the only ways cancer never starts or stops entirely is NK cells. They are the guerrilla warfare troops that never sleep. This is why it is vital for BioShield by to be approved, NOW for every cancer, not just a single cancer type. With the rest needing years and years of tests. Cancer is cancer, unregulated cell division and NK cells are NK cells. The ONLY thing that stops BioShield is government betrayal, and organized corruption disguised as following the “regulations” designed by the entrenched gatekeepers of corporate protection. Everyone knows it works that has even a molecule of biological understanding. Now you know.show more