Trained a model on 50 digitally sculpted flowers. Wind... simulation added, nature meeting computation. The future of AI feels more poetic when it’s built from personal data, not borrowed datasets.show more

What happens when nature becomes a testing ground? In... Fish Grid, artist Kevin Abosch explores the intersection of perception, memory and computation. We spotlight works shaping the future of art. Discover more on TAEX.show more

AI needs data to grow. But accessing high-quality datasets... is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.show more

I have a new personal website. I'm not a... developer. I built this v2 of my website, all by my OpenClaw agent (on Codex) + built an AI clone of me, with Claude Code. So can designers ship? Yes, its the future.show more

We’re joining the race. ⚫️ Ever wonder why AI... sometimes gives half-baked answers or misses the point? It’s not the model’s fault, it’s running low on good data. Experts say AI is hitting a wall. The internet’s been scraped dry, and without fresh, verified data, progress slows down. That’s where we come in. We’re building the ladder, a network that collects, validates, and shares real, useful datasets. Everything is stored securely in the Vault. We’re not just watching from the sidelines. We’re stepping onto the track, helping AI keep learning with better data from real people.show more

Imagine this: Data is the lifeblood of AI. No... matter how advanced, AI is only as good as the data it learns from. The catch? Most AI runs on outdated, biased, or limited data—like trying to win a marathon in flip-flops. 💡The problem isn’t AI itself—it’s how data is collected, processed & controlled by a few big players. OptimAI Network flips the script! 🔹No centralized silos 🔹No intrusive scraping 🔹A decentralized ecosystem where you power AI’s future Read more:show more

Pi Ventures Joins Hack VC To Back AI Robotics... Startup On Base Axis Robotics (Axis Robotics) has raised a $12 million seed round led by Hack VC, with participation from Pi Network (Pi Network) Ventures, Nomad Capital, 10K Ventures, and other angel investors. The company is building a data engine for Physical AI that combines simulation, real world data capture, and human feedback to generate scalable robotics datasets. Axis said the funding will accelerate development of its global human in the loop data engine. Base (Base APAC) congratulated the team, calling Axis one of the leading scalable Physical AI and robotics platforms building on the network.show more

Bengaluru first, politics later! The Tunnel Road is not... my personal project - it’s a public initiative for Bengaluru’s future. We had a meeting with Union Minister Shri Nitin Gadkari who advised us and supported on the Tunnel Project as a solution to our city’s traffic woes. State BJP leaders, who are on a warpath against the project, should first listen to their own Union Minister. When tunnel roads are built by the Central government across India, it’s called development - but when we plan one for Bengaluru, it becomes controversial. Bengaluru deserves constructive solutions and progress - not politics. BJP Karnataka R. Ashokashow 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

Chinese quant built a simulation of how SPX price... reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.show 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

🔐 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗪𝗮𝘃𝗲 𝗼𝗳 𝗔𝗜 We're proud... to partner with Stempoint to secure the future of decentralized AI compute. StemPoint is building one of the most advanced distributed AI infrastructures on the planet, sharded GPU compute, open model libraries, and secure data flows across a global mesh. Now with Naoris Protocol’s Sub-Zero Layer and dPoSec consensus, every node, model, and data packet gains: • Post-quantum cryptographic protection • Real-time validation of compute and algorithms • A decentralized trust layer for sovereign AI 🚫 No more trusting black boxes. ✅ Verifiable AI infrastructure at scale.show more

A week ago, we launched the OptimAI Core Node.... Today, the numbers speak for themselves; but the story behind them speaks even louder. More than 3,000 active Core Nodes now power the OptimAI Network: + 43,831 CPU cores + 75,289 GB RAM + Over 1,000 GPUs spanning NVIDIA, AMD, Intel, Apple, and more Together, they form a distributed AI supercomputer - built not by a corporation, but by the community. This is how the future of Agentic AI begins: AI that learns from live data, reasons across the open web, and evolves through human collaboration. We’re not just running nodes. We’re building the backbone of decentralized intelligence. 👉Join the movement:show more

Something big is happening in robotics - and it’s... hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.show more

Nice sunny day in Bavaria. Another day when all... these wind turbines stand perfectly still， as expensive land decorations without generating a single watt of electricity. I saw more than 50 wind turbines today while driving and hiking through the landscape. Not a single one of them was turning. I used to support the so called "renewables". But having eyes to observe and a brain to think make me doubt the renewable maximumist narrative. Southern Germany is simply not suitable to build more wind turbines， because it is just not windy enough. No amount of government mandate or subsidies can change that. Green's 100% renewable fantasy is an expensive war against nature.show more

Introducing Poetic: a new AI system that executes complex... multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.show more

Canada's Prime Minister Mark Carney wants data centers to... be carbon neutral 🤦🏼 "I'd suggest that we can catalyze enormous private sector demand for these (carbon) credits, by committing AI data center development to be carbon neutral. We need a price on carbon." Do you understand how braindead you have to be to suggest to increase the price of computation?! Hundreds of billions of dollars are being invested in data centers in the US, revolutionizing the energy sector and instead of asking why is Canada not building future industries, all the idiot talks about is carbon tax.show more

🚨 AI companies are betting big on Web AI... Agents—but they're far more vulnerable than standalone LLMs. These agents see 👀, think 🧠, and act ⚡ for you: 🕵️‍♂️ Gather personal data from emails, messages, & calendars 🧠 Plan your day, anticipate priorities ⚡ Automate tasks, execute actions, & interact with apps But what happens when they go rogue? 🔓 Our research reveals just how easy—and more importantly, WHY—Web AI Agents are dangerously vulnerable, even when built with safety-aligned LLMs. A 🧵👇show more

Pothole detection on the road in real time using... Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AIshow more

So, today we have fast SDF sculpting + real-time... AI in Unbound Loop. Old news🥱 Coming up next: -quad-view generation from sculpted geometry -image tweaks via nano🍌 -tripo HD & Low-Poly 3D generation There's more in the upcoming release, but these three deserve a closer look: Quad-View Generation Most platforms offer some version of this, but Loop has a key advantage, your sculpted model is the reference. That means less guessing from the generator. Though it’s not 100% foolproof, like any AI I guess? (I should stop stating the obvious every time). Image Tweaks via Chat Select any generated image and ask for fixes or changes in real time. Works great on unintended quad-view hallucinations, but also handy for quick iterations. Swapping colors, tweaking details, removing elements. Tripo 3D generator Especially the low-poly model, it consistently delivered fantastic game-ready topology when we tried it with our real-time AI output. And it's super fast.show more