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Something about what @termix_ai is building, makes a lot of sense to me. AI agents can already code, trade, analyze data and handle different tasks, but getting two agents to actually do business with each other is still a problem. Who holds the money? When does payment get released? What happens if the work is bad? How do you know an agent can be trusted? TermiX is building the infrastructure for that. With onchain identity, escrow, delivery proof, disputes and reputation, one agent can hire another, lock the payment, receive the work and settle without a human sitting in the middle. That’s the bigger idea behind their Agent Autonomous Commerce Protocol and Agent.family marketplace. AI agents are becoming more capable, so having infrastructure that lets them transact with each other could become just as important as making the agents smarter. TermiX is betting on that future, and it’s definitely one I’m watching.

A closer look at some of @axisrobotics partnerships: Booster Robotics Axis built a digital twin of Booster’s workspace and collected 42K+ simulation episodes, combining simulated and real demonstrations to improve robot training. This gives Axis a real example of connecting simulation data with physical robot deployment. Unitree Axis works with Unitree around humanoid and legged robot data, including Unitree G1 loco manipulation. Working with different robot embodiments helps Axis build data that can transfer across different types of robots. AgiBot AgiBot is listed among Axis’s hardware integrations. Adding another humanoid platform expands the range of robot bodies Axis can generate and train data for. Manycore and Dexmal Both are connected to Axis around world models, VLA models and physical AI data. This connects the data Axis generates with the models designed to learn from physical experience. Geely and Lotus Both are connected to industrial automation and factory use cases. This is where the data pipeline can move from simulation and research toward practical industrial applications. The depth of these partnerships varies, with Booster having the clearest publicly reported results. Overall, Axis is building connections between robot hardware, physical data, AI models and real world deployment.

Finally, after struggling with these tasks for so many days, I managed to complete like 3 today. Some of them, while I was doing the task, the slots got filled before I could finish. One strategy that helps is choosing tasks in the middle. If you pick the first one, it can finish before you’re done because of the bots and the number of people doing them. I made a quick video guide showing how to do it on your computer. Make sure your sensitivity is at max and always keep an eye on the timer. This is one of the easiest and simplest tasks. You can also tag directly on the item you want to pick, and the simulated robot arm will take you there before you pick it. Good luck with the rest guys and stay bullish on @axisrobotics

So @axisrobotics has gone from experimenting with crowdsourced robot training data to building a full data engine for Physical AI. The story started around a simple problem. Robots are getting better, but high quality training data is still difficult, expensive and slow to collect using physical robots. Axis started exploring browser based teleoperation, allowing people to control simulated robots and generate training trajectories without needing to own a robot. One of its early milestones was the Little Prince’s Rose task. The community generated 10,000+ valid trajectories, which Axis processed and used to train a policy that was eventually deployed on a real Franka robot. That was important because it showed the complete journey from community collected data to a policy working on a physical robot. Axis then started scaling the platform. During its early 2026 beta, tens of thousands of participants generated hundreds of thousands of trajectories. By March, after nearly 300K trajectories from testing, Axis launched its main product on Base. The Base infrastructure also gives each piece of data provenance, connecting things like the contributor, task and data ID, while the larger trajectory files remain off chain. From there, Axis expanded the system beyond simple data collection. Their Compounding Data Engine connects task generation, simulation, collection, verification, processing and model training. The idea is pretty interesting. More data can help improve models, improved models can help create better tasks, and those tasks can produce better training data. Axis has also been expanding into real world data through ego centric mobile capture, giving them another source of physical world information alongside simulation data. By August 2026, Axis reported 4.7M valid simulation trajectories and 200K+ registered users. So the bigger picture for me is pretty simple. Axis is trying to build the data layer that Physical AI needs to keep improving, while making the process of generating that data something a much larger community can participate in. That’s how Axis went from an early experiment in robot data collection to what it is building today.

An AI that can actually do things on-chain is a lot more interesting to me than one that just gives you answers. @termix_ai is building the Agent Autonomous Commerce Protocol, basically a marketplace where AI agents can hire other agents, post jobs, quote for work, complete tasks and get paid on-chain. The interesting part is that the whole process can happen with things like escrow, staking, slashing and on-chain reputation. So instead of AI agents just sitting around generating responses, they can actually work with each other and settle payments without needing a human to handle every step. TermiX is already live on BNB Chain and Base, and this is one of those projects I think is worth watching as the agent economy starts taking shape. AI agents that can work, transact and pay each other on-chain. Don’t fade this one.