This is a milestone for robotics. @DynaRobotics' Dyna-2 is a world-action model pre-trained on 1M+ hours of human video. It validates clear scaling laws and, for the first time, human-to-robot cross-embodiment transfer with zero robot data in pre-training. This aligns with our belief: noisy, in-the-wild data converges to robust policies at scale. We generate both egocentric and sim data at scale through our data engine. And we’d love to explore how our high-value, high-diversity datasets could help push this frontier even further.

Product Update 🛠️ Two small improvements to make finding the right tasks easier: 1️⃣ Embodiment labels and filters: You can now filter tasks by embodiment, including Franka, OpenArm V2, and more. 2️⃣ Persistent filters: Your filter selections now stay in place after you complete a task and return to the task list.

I don’t think people realise how much traction crypto robotics protocols are gaining One of the biggest bottlenecks in robotics is data; crypto is primed for co-ordinating resources to incentivise data contribution Booster is interesting because it’s building the full humanoid stack (hardware, OS + developer tooling) and getting increasingly capable robots into the real world Axis is now using those real-world demonstrations as seeds for simulation, multiplying scarce robot data into training environments at scale This “real → sim → model → real” loop is becoming one of the most important flywheels in Physical AI Axis also announced they’re deploying subnet 4 on @BitRobotNetwork recently who I’m following closely (think TAO purely for robotics) We will be publishing our second @KhalaResearch decentralised robotics report later this month, so stay tuned… A LOT has changed since January

Due to popular demand I made a video guide so you all don’t have to worry… 2026 is your last year of being poor 🫡 Everyone post, Farmercist guides. - Lock in all the task - Finish all your training - Make sure you sign them - And that’s all you need to be eligible 🫡 - link in quote

Excited to collaborate with @AxisRobotics. Booster delivers an open humanoid robotics platform with Booster Studio for robot development. Combined with Axis’ cloud-based simulation and data infrastructure, we’re building a more complete Physical AI solution—connecting simulation, model training, and real-world deployment. Looking forward to enabling developers and industry partners to shape the next generation of embodied AI.

Announcing our commercial partnership with @boosterobotics Booster builds humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.

Thanks @baseapac for having us. Physical AI is hitting a data wall. On @base, we’re building the infrastructure to scale beyond it. Axis brings together browser-based simulation, real-world egocentric capture, and a global contributor network to produce diverse training data at scale. Watch our co-founder & CMO @0xsexybanana unpack why we started Axis, what we’ve built so far, and what’s next. ⬇️

The Axis Point System is LIVE. Every valid data contribution is recorded, quantified, and reflected in your Points—so real contributors are recognized for honest, high-quality work that advances robotics. Check your Points → How it works ↓

Robotics is moving fast. But how close are we to its GPT-1 moment—and what’s still missing? Axis is hosting an X Space, co-hosted with @blocmates and joined by @BitRobotNetwork , @PrismaXai , and @FabricFND , to discuss what it will take to get there. We’ll also share a limited number of access codes with listeners during the Space. 👀 Join us on Friday, July 31 at 11:00 AM PT / 6:00 PM UTC. Set your reminder:

At Axis Robotics, our vision is to build a compounding data engine—one that connects large-scale pretraining data, corrective post-training data, model deployment, and failure feedback in a continuously improving loop. Over the next 6–12 months, we will advance this vision across three connected fronts. On the product side, we plan to scale our egocentric data pipeline in September, with tens of thousands of hours already collected and product requirements being shaped with frontier labs. In October, we will expand our simulation data across more robot embodiments and atomic capabilities. By year-end, we plan to release a large-scale post-training dataset built through human-gated DAgger (HG-DAgger), where the policy acts autonomously and contributors intervene only when it needs correction. On the network side, we will expand our contributor ecosystem into Latin America and Eastern Europe, strengthen our 100K+ contributor network and grow toward 10K DAU. This expansion is designed to support the production of more than 500 hours of egocentric data and 50 hours of simulation data per day while building capacity for corrective post-training data. On the commercialization side, we plan to complete two to three new paid pilots by year-end and work toward becoming a preferred vendor for foundation model companies in Q1 next year. The longer-term goal is to embed the data engine directly into the training and deployment workflows of robot hardware companies, model developers, and industrial operators. These are not separate tracks. They reinforce the same flywheel: broader data coverage produces stronger models; stronger models reach new states; and new failures reveal what data should be collected next. That is the future we are building toward: Scale to produce data continuously. Diversity to reflect the complexity of the physical world. A closed loop to turn deployment feedback and failures into the next round of model improvement. Our north star is not simply more data. It is faster model evolution.

We’re thrilled to announce a $12M Seed round, led by @hack_vc, with participation from @NomadCapital_io , @PiCoreTeam Ventures , @10kventure and top angel investors. Physical AI has a data problem. Models need more than static datasets—they need diverse data that evolves with them. Axis’s compounding Data Engine is here to fix this gap. Our end-to-end closed-loop workflow unites large-scale simulation, egocentric real-world capture, and human-in-the-loop post-training to unlock scalable production of structured, multi-diverse robotic data — the core missing piece for Physical AI. The capital will accelerate Axis’s mission to build a massively parallel, human-in-the-loop global data engine. We’re just getting started.

🚨 AMA Announcement 🚨 Today at 9PM KST / 8PM SGT 🎙 MC: Humbleman (@Cryptowombat125) 🎤 Speaker: Chris (@chris_anm01), CEO of @axisrobotics Join us to hear the vision behind Robotics General Intelligence (RGI) and the future of Physical AI. 📍 X Space: 🕘 9PM KST / 8PM SGT today Don't miss it!