Unboxing a Booster Robotic K1 to perform on @wallet event.🤣 Catch me tonight at OKX wallet event!

let me explain why some people has the see the unseen badge and some don't, and how the badge are distributed. - Why the program exists We have genuinely hard tasks that only experienced contributors can execute well. Low-quality trajectories fail QA, and data that doesn't pass QA isn't useful for training. The goal from day one: put the hardest, highest-value tasks in front of people who can actually do them well. - How badges are released Badges are not handed out manually. An algorithm runs every week, screens every address against the same criteria, and releases a batch. The easy-to-miss part: badges are released on demand. If there are no hard tasks needing skilled contributors that week, none are released. And when we already have enough skilled labor, even qualified addresses may not get one. - What the algorithm evaluates Four dimensions, applied identically to every address: Task completion quality (high success rate) Verification pass rate Performance on post-training tasks Performance on 4-star challenging work Two things it explicitly does not consider: how many trajectories you've completed, and how early you joined Axis. That's why ~1,000 trajectories can earn a badge while several thousand may not. This campaign was never about rewarding volume — it's about identifying who can contribute the data that most improves model training. - On "unfairness" If you've done a large volume and don't have the badge, I understand why that feels unfair. But volume and skill are different things, and this program is built around the latter. Same bar for everyone — no favoritism, no manual override. - No manual selection With hundreds of thousands of active addresses, manual selection isn't possible. We can't reliably judge who "looks skilled" by hand. — note on our task testers some of our part-time task testers were picked up by the algorithm and were participating. Because they test tasks regularly, they're genuinely very skilled — so the algorithm correctly identified them. We've now removed them from the program going forward to keep the field clean. Transparent communication is how we build this community. If anything is still unclear, ask away — I'm happy to go deeper. Thank you to the Axis community for the support and honest feedback.