Good morning CT @vangrid_io AI can learn almost everything online. Robots don’t have that luxury. A robot needs to understand what a street, warehouse, loading area or piece of infrastructure looks like right now. That’s the gap @vangrid_io is building around. Instead of deploying another expensive sensor fleet, Vangrid turns the phones already in people’s pockets into a distributed perception network: → 3B+ potential edge nodes → location-specific data on demand → privacy processing at the edge → verifiable provenance onchain But the part I find especially interesting is the access layer. Physical intelligence doesn’t just need fresh data. Enterprises also need control over where that data can go. Vangrid’s enterprise API uses regional scopes such as: us-east-1 → eu-west-1 → apac-1 A credential authorized for one region cannot simply pull observations from another. Cross-region requests can be rejected instead of quietly returning data outside the permitted boundary. That matters for real-world applications: → robotics operating in a specific area → mapping within defined jurisdictions → infrastructure monitoring → sovereign deployments → enterprises with data-residency requirements At the same time, the API can work with verified observations, private observations, live spatial feeds and provenance verification — while keeping access tied to the authorized region. So the architecture becomes pretty interesting: Capture happens globally. Access stays scoped. That’s an important distinction for Physical AI. Because the future isn’t just about collecting more physical-world data. It’s about collecting fresh data, processing it responsibly, proving where it came from, and giving the right systems access to exactly what they’re authorized to use. Connect physical intelligence without turning the world into one giant unrestricted database. That’s the direction I’m watching with Vangrid. 🦾