flippedface(ARX MODE)☂️

Honored to be part of the first @PrismaXai Ambassador Cohort and proud to represent thailand 🇹🇭 community. Being selected from over 1,550 applicants means a lot to me, but more importantly, it comes with greater responsibility moving forward. My goal is to help onboard new members from around the world especially from thailand and introduce more people to PrismaX and its vision. This is only the beginning of the Physical AI and Teleoperation era. I’m excited to contribute by helping more people understand PrismaX, recognize the value of this technology, and gain clearer access to its products, opportunities and ecosystem. Thank you to everyone for the support. I look forward to growing alongside both thai community & the global PrismaX community🦾👀🇹🇭

After 4 rounds of @QFEX Daily Auction, it's finally my turn! RANK #9 LFGGGG!! For those who haven't joined yet, here's how it works: - new auction every Monday–Thursday. - register daily through rewards Hub. - trade to climb the leaderboard. - Top 20 ranks qualify, but only odd-numbered ranks (#1, #3, #5 ... #19) win prizes. - starting prize pool $2,000, growing throughout the campaign Finally secured a winning spot. Feels goooood! And something even bigger is coming next week! 👀 my ref : qfex.com/@ra37d94e

PrismaX just opened robotics data in limited release. What I found interesting is that they’re not just focused on collecting more data. Small details like camera placement, how objects are arranged, and how an operator performs a task can all affect what the model actually learns. PrismaX also trains on the data they collect and uses the results to improve how they collect the next round of data. The picture is getting clearer: VLA Foundry → people collecting & reviewing data Robotics Data → data for teams building Physical ai PrismaX started with teleoperation, but it looks like they’re moving beyond that now

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Join me on the Jumper waitlist. waitlist.jumper.xyz/r/JMP-1E…

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AI safety for physical AI is different. for LLMs, a wrong answer can be bad. for robots, a small error can damage hardware, break parts, or even hurt someone. the biggest challenge isn’t just preventing bad behavior, but minimizing physically inappropriate actions as close to zero as possible. sometimes, the safest training data is simply the data you choose not to put into the model in the first place. that’s why data quality and validation matter so much for physical AI. and this is one of the areas @PrismaXai is working on. 🦾