Qubic #OpenScience #Neuraxon Life 2.5 (Research) version code released... & and a new Hugging Face massive dataset, almost 500Gb of raw TimeSeries Game data, 10 Sessions with 270+ rounds each, in total 100K+ Nxers in 2791 games with 3.76 Million Neurons & 17M Synapses to explore in a huge 1 Billion + events. By Jose Sánchez David Vivancos - e/acc for Qubic Science: Open Data Source Code: Dataset: Paper : Stay Tunned for more releases this week!show more

Finally Neuraxon is out!!! Our new paper & code... & demo by Jose Sánchez & David Vivancos - e/acc expanding on the Qubic #Aigarth #intelligenttissue by Come-from-Beyond the path towards #TrueAI "A New Neural Growth & Computation Blueprint" one step forward to #AGI & #ASI Read the preprint Paper: (All the details of our ongoing research) Explore the Code: (Please give us a⭐fork and build!) Play with the Interactive Demo: (Build your own Neuraxon NetWork) Andrej Karpathy do you want to give it a try? Beff (e/acc) can we try it on Extropic hw? And more to come stay tuned! #EAGI #Neuraxon #Trinarystates #Continuousprocessing #Synapticdynamics #Neuralplasticity #Spontaneousactivity #Temporalsynchronisation #Bioinspiredcomputation #Artificiologyshow more

Yes Ilya Sutskever "human neurons actually do more compute... than we think" that's why Jose Sánchez & David Vivancos - e/acc just published the preprint of #Neuraxon finally updating the 1958 Perceptron to what we know in 2025 of real #Neuroscience and #BioInspiredAI for #QubicScience Qubic in the path to #TrueAI #AGI #ASI #BeyondLLMs Read The preprint here: Explore the Code: Intereactive Demos at Hugging Face: And the ongoing researh on #ArtificialLife BTW Great Dwarkesh Patel Interviewshow 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

What does a real humanoid robotics dataset actually look... like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.show more

This reported breakthrough apparently used a dataset that’s open... for researchers. It’s the work of Eddy Xu, a teenager who was one of the first to get in on the video training data gold rush. He dropped out of Columbia last year to launch Build AI, which has raised around $22 million so far. Build AI’s Egocentric-1M dataset reportedly includes 1 million hours of data recorded using the startup’s self-developed devices across factories in Southeast Asia. A lot of it is from India. Xu has said he’s moved his team to Bengaluru, dedicating $10 million to get data from Indian factories. India has become one of the prime locations for collecting this kind of data. While enrolled at Columbia Engineering, Xu went viral in January 2025 after showing Meta Ray-Ban smart glasseshe modified to cheat at chess. The student, then 17, connected the device’s camera to a chess engine that calculated the best move and relayed it in real-time. The tech reached a wider audience thanks to popular streamer and chess master Alex Botez publicly tested them. Before college, the Long Island-raised Xu won DECA’s global business championship and sold an edtech startup that reached more than 178,000 users in 90 days. He also launched a startup called Omega Robotics in middle school, raising about $120,000 to run an independent, coach-free competitive robotics team out of a basement. Xu and co-founder Jonathan Jia, who serves as CTO, moved to San Francisco to build the first recording devices with a small team. They quickly moved operations to Shenzhen to quickly iterate and scale production. Build previously offered smaller datasets with 10,000 and 100,000 on Hugging Face but the 1M dataset requires emailing Xu directly. I’m sure he’s flooded with requests now.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

(1/n) 🚀 With FastVideo, you can now generate a... 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licensesshow 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

This isn’t a 3D model file. It’s 100% code... Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the webshow more

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

🚨SPACEX DIDN'T ACQUIRE CURSOR FOR TALENT A rocket company... buying software for the largest startup acquisition in history Here's what almost nobody realizes: They didn't buy a talent or a coding app. They bought the data underneath it. Cursor's users generate ~150 million lines of code a day. Every keystroke is a record of how real engineers build and fix software The exact raw material to train an AI that can code. You can't buy that on a market. You have to own the thing that produces it. So SpaceX bought a permanent data firehose for Grok. The product is almost a side effect. The entire $60B was covered by a fraction of their market-cap gains since the IPO This is a catch-up bet with Anthropic and openAI You see how they are slowly building it don't you?show more

I've been editing this article about "brain mapping" and... connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)show more

We’ve seen humanoid robots walk around for a while,... but when will they actually help with useful tasks in daily life? The challenge here is the diversity and complexity of real-world scenes. Our new work tackles this problem via 3D visuomotor policy learning. Using data from only 1 scene, our Improved 3D Diffusion Policy (iDP3) enables a full-sized humanoid robot to autonomously pick&place objects, pour water, and wipe tables, in the wild open world. (and all these skills are useful, right?) Web: Fully open-sourced code:show more

Introducing ASAL: Automating the Search for Artificial Life with... Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.show more

I genuinely don't understand why everyone isn't using this... yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. You point Claude Code at a folder, drop in any source, an article, a transcript, a PDF, and Claude reads it, links it, and files it into a living wiki of everything you know. It compounds like interest, the more you feed it, the smarter it gets. Here's the whole thing: > Install Obsidian, create a vault, open it in Claude Code > Paste Karpathy's wiki idea file and tell Claude to build it > Claude makes three folders: raw for sources, wiki for its pages, a CLAUDE.md that runs it > Drop any source into raw and say "ingest this" > Ask questions across everything, forever Five minutes to set up, and you never start from a blank chat again. Full step-by-step guide with Claude and Obsidian, link below. Bookmark thisshow more

Google just wired DeepMind and Earth Engine directly into... the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.show more

Robot Utility Models (RUMs) enable basic tasks – door... opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇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

I still don't understand why everyone is not using... this yet. Thanks to it, a year ago I increased my income to 17,000 dollars a month Andrey Karpathy, co-founder of OpenAI, published a simple idea that got 16 million views: stop using AI to write code, use it to build a second brain You point Claude Code to a folder, drop any source in there (an article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. It compounds like interest: the more you feed it, the smarter it gets Here is the gist: Install Obsidian, create a vault, open it in Claude Code Paste the file with Karpathy's wiki idea and tell Claude to build it Claude creates three folders: raw for sources, wiki for its pages, CLAUDE which runs everything Drop any source into raw and say "ingest this" Ask questions across everything, forever Five minutes to set up, and you never start with an empty chat again The full step-by-step guide is in the article. Save to bookmarksshow more

🚨 JUST IN: CHINA just released an AI EMPLOYEE... that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.show more