PrismaX (@PrismaXai) the service layer for physical AI 🦾 Robotics hardware is no longer the bottleneck. Dexterous arms, humanoids, and mobile platforms already exist. The real constraint is data: diverse, high-quality, real-world demonstrations that let vision-language-action (VLA) models generalize beyond lab scripts. PrismaX coordinates the missing layer between humans, robots, and models. 🔍 The problem Physical intelligence improves through interaction, not just better architectures. A robot learns a task by watching how a human performs it under real variation (objects, lighting, scene layout, handedness, failure recovery). Collecting those episodes at scale is hard: • Teleoperation needs skilled operators + hardware access • Egocentric capture needs consistent protocols • Raw video is useless without strict quality filters Most labs either under-collect or build one-off pipelines that never compound. ⚙️ What PrismaX builds An open coordination layer that turns human demonstration into reusable training data. • 🎮 Teleoperation operators control real robot arms remotely (browser/VR) and generate action-aligned trajectories • 👁️ Egocentric capture first-person video of everyday manipulation (headset or camera). No robot required • ✅ Quality standard every episode is scored before it enters the data bank (synced views, resolution, format, task completion, clean trajectories). Community validators → senior review → expert final call • 📦 Data bank + on-demand collection searchable teleop and egocentric episodes across tasks, scenes, and robot types. Off-the-shelf sets ship immediately; custom collection starts in days 📊 Current scale (limited release) 120k+ reviewed episodes · 1,700+ deployed scenarios · ~5,000 hours monthly capacity · $1M+ paid to contributors 🔄 The loop Robots interact → interactions become scored demonstrations → demonstrations train policies → better policies expand the set of tasks worth collecting. PrismaX sits in the middle so the data flywheel is shared instead of locked inside single labs. 👥 Who participates • Operators earn by capturing valid teleop sessions or egocentric clips (VLA Foundry) • Validators earn by scoring episodes against the published standard • Robot owners register hardware and contribute fleet data • Builders (researchers → enterprise) access the data bank and custom sets via Discover / Researcher / Enterprise tiers (API + browser download) 💰 Backed by a16z crypto CSX ($11M seed, 2025) with Stanford Blockchain Builder Fund, Symbolic, Volt, and others. Founded by Bayley Wang & Chyna Qu. San Francisco. Host of RoboCon. Physical AI is hardware-rich and data-poor. PrismaX is the infrastructure that prices, standardizes, and scales the demonstrations models actually train on. 👉 https://t.co/VEeXW5HaWU