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One of the most exciting projects in the Physical AI space right now is @PrismaXai . They're bridging the gap between AI and the physical world through robotics and a powerful data flywheel: 1. More robots 2. More data 3. Smarter AI 4. More complex tasks. What makes it unique is that humans are part of the learning process. Every interaction helps train AI through real-world guidance, not just static code. And this isn't happening in simulations. PrismaX is already working with advanced robotics platforms like Unitree G1, Reachy 2, and MIT Mini Cheetah. They're also making robotics more accessible through hands-on workshops and real-world learning experiences. The future of Physical AI is being built today, and PrismaX is helping lead the way.

Human Robot Data The future of robotics is not just about better hardware or smarter AI. It’s about how robots, humans, and data work together in real-world situations. That’s what @PrismaXai is building. When robots move from labs to the real world, things are not always perfect. Environments change, tasks become different, and human support becomes important. PrismaX is solving this by creating a system for teleoperation, data collection, and robot deployment where every interaction helps improve the system. The idea is simple: Robots collect data → models improve → robots work better → they can handle bigger tasks → more useful data is collected. This creates a cycle where the system keeps improving with real-world usage. What makes PrismaX more interesting is its owner-operator model. People can operate or deploy robots and help collect real-world data. This makes robotics more open and community-driven instead of being controlled by only a few companies. In simple words, PrismaX is building a network where robots learn and improve through real-world experience. Follow PrismaX on X For more updates👇 X - @PrismaXai Discord - discord.gg/hFhx3JqM Telegram - t.me/PrismaX_News

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What if robots didn’t learn in labs… but from people at home? That’s what @PrismaXai is building. A platform where everyday people remotely control real robots, earn money, and help train AI at the same time. Here’s how it works. Imagine a warehouse setup like Amazon or Flipkart. A robotic arm needs to pick boxes from a conveyor belt and place them on shelves. Some boxes are fragile. Some are oddly shaped. Some get stuck. Instead of full automation from day one, a human logs in from home using a laptop or VR setup and controls the robot in real time. One operator can handle 200–300 picks a day. Every move gets recorded the arm’s path, grip pressure, speed, adjustments. Over weeks and months, thousands of these sessions turn into high-quality training data. Gradually, the robot starts doing most of the work on its own maybe 85–95%. Humans only step in when something unusual happens. The result? Warehouses move faster. Companies cut repetitive manual work. Operators earn from home. And robots actually learn from real-world situations, not just simulations. It’s not about replacing humans. It’s about humans teaching machines to take over the boring stuff. That’s a smarter way to build AI.

What is QRB on PrismaX? QRB stands for Quick Return Bonus. It’s a reward feature inside @PrismaXai teleoperation system. What does QRB do? ➥ ewards speed with accuracy: Operators who complete robot arm tasks quickly while maintaining precision earn bonus rewards on top of their base pay. ➥ Improves data quality: Fast, controlled, and consistent movements generate cleaner and more useful data. That data helps improve the system over time. ➥ Highlights top operators: Those who consistently perform well get access to better tasks, higher earnings, and priority opportunities. ➥ increases overall efficiency: When more tasks are completed quickly and correctly, the entire network becomes more productive and valuable. How QRB fits into PrismaX’ model The process works like this: Humans control robots → Data is generated → The system improves → Robots become more independent → Demand for more tasks and data grows. QRB strengthens this cycle by encouraging fast, high-quality work. It also works alongside features like staking, guilds, a quality scoring system, and future token rewards.

PrismaX is betting on something different 👀 What's different ? = The real-world data. Instead of focusing on building the best robot, it focuses on collecting real interactions through a decentralized teleoperation network. Because here’s the truth: simulations are useful, but the real world is messy. Sensors fail. Surfaces slip. Environments change. Real-world data is harder to get, and more valuable. So this is not just robot vs robot. It’s hardware power vs data scale. - Tesla builds robots. - Boston Dynamics builds robots. - DeepMind builds models. @PrismaXai is building the network behind them. And sometimes, the layer no one sees becomes the one that matters most.