I have been following the interesting Physical AI project @ axisrobotics recently. Simply put, why was it included in the list 1. The track is new enough: Physical AI is a direction worth paying attention to in the coming years. 2. There are Web3 attributes: it is not a traditional robot company that enforces a token, but introduces Crypto through data contribution, verification, incentives, and assetization. 3. Good background: $12 million Seed, led by Hack VC. 4. Early stage: The biggest advantage of early projects is the low cost of participation. 5. Real interaction: quite challenging. The simple understanding of what Axis does is to have the world help robots "collect experience" and then use this data to train robots in reverse. The biggest advantage of traditional AI is massive Internet data, but robots are different. Robots require a large amount of physical interaction data, which is precisely the most difficult to obtain on a large scale. What Axis wants to do is to solve this data bottleneck. Axis Robotics positioning is a physical AI infrastructure, with the core idea of: human contribution data → simulated environment training → data validation/enhancement → training robot models → feedback to real robots. The official emphasis on Simulation First is to first generate a large amount of robot training data through simulated environments, and then use models and data augmentation to expand the scale. Interestingly, it did not completely follow the path of traditional AI companies, but instead incorporated Crypto incentive mechanisms into the entire data production system. Simple understanding: In the past, robot companies spent money to hire people to collect data themselves; Axis aims to create a global network where ordinary users can also participate in contributing data, and record contributions and verify data value through on chain mechanisms. Future data IDs, models, Skill Modules, etc. can all become verifiable and traceable assets on the chain. So I think the most noteworthy aspect of Axis is the Physical AI x Crypto x Data Network. I personally pay attention to three things about Axis. 1) Data Platform: Allow users to participate in data contribution and convert the operational behaviors of different individuals into robot training data. 2) Augmentation Engine: By simulating, rendering, enhancing, and other methods, limited data is further expanded. 3) Model Layer: Ultimately, we need to return to robot model training to truly serve Physical AI with this data. Axis attempts to connect the entire chain of data collection, data processing, model training, and robot execution. From the perspective of airdrop, how do you play now? Participation link: https://hub.axisrobotics.ai/login?invite_code=2OtOQ3k9 Connect Wallet → Hub → Select Task → Operate Robot → Complete Training → Obtain Contribution/Points Record. The gameplay of Axis is quite different from regular DeFi trading volume manipulation. It values real participation behavior and data contribution more. So I don't really recommend simply hanging up. Personal suggestion: ① Run all the basic tasks first. First, experience all beginner tasks, basic tasks, and different types of tasks to ensure that the account generates complete behavior records. ② Don't just focus on one task If the future of the integral system is really related to "contribution quality", then a single repeated operation may not necessarily be the optimal solution. You can try: different task types+different difficulty levels+different stages, to make your contribution dimensions more diverse. ③ Both pre training and post training are worth doing There are two key stages in Axis' task mechanism: Pre training: The user remotely controls the robot to complete the task first. Post training: Then let the trained strategy execute on its own, and the user is responsible for observing and taking over to correct errors when they occur. This design is actually quite interesting. It is collecting: how humans do it → how AI does it → where AI makes mistakes → how humans correct them. This type of data itself is a very important training material for Physical AI. So if the official incentives are based on the quality of contributions in the future, personally, I would be more inclined to do more effective corrections rather than mechanical brushing times. ④ The difficulty can gradually increase If you are already proficient in low difficulty tasks, you can gradually challenge higher difficulty. But don't blindly pursue difficulty here just for the sake of points. Completion rate>single difficulty. Because if the failure rate of the task is too high, the time cost will actually increase rapidly. ⑤ Maintaining continuous participation is more important than just a day of explosive updates The gameplay I prefer for this early project is: small, continuous, and real participation. For example, taking 10-20 minutes every day to complete tasks is more reasonable than spending several hours on a single day without touching for a week. If future projects use dimensions such as activity, contribution frequency, contribution quality, and user level as incentives, sustained behavior is usually easier to form effective records than one-time behavior. ⑥ Don't ignore on chain behavior either Axis itself emphasizes the Crypto powered human network, and its official website displays network metrics such as Onchain Agent and Active Nodes. So if more on chain features are opened up in the future, I will focus on: Wallet binding → Base interaction → Data contribution → Agent/Node → Subsequent official tasks, this entire path. But here is one important point: don't interact, authorize, or transfer money randomly for the sake of so-called "points". Axis is currently more like a 'Physical AI early infrastructure+potential airdrop observation target'. Low cost participation+continuous contribution+leaving complete behavioral records+official follow-up incentives. If you can, go ahead and do it, after all, the early time cost is high!