robots are learning how to move, but who’s teaching them what the real world actually looks like? that’s the gap i see @vangrid_io going after. streets, loading bays, corridors and all the random places that traditional mapping fleets don’t update nearly fast enough. instead of relying only on controlled environments, Vangrid turns smartphones into capture nodes where people can complete specific location bounties funded with USDC on Base. and the interesting part is that the capture isn’t just a video file sitting somewhere. multi angle footage can be turned into 3D geometry, point clouds, Gaussian splats or textured GLB models, with provenance tied back through hashes and EAS attestations. privacy is part of the workflow too. faces and license plates are blurred on device before the raw footage leaves the phone. the current network snapshot is already showing 1,024,912 captures, 423,143 active nodes, 3,823 EAS attested Merkle trees and around $311K settled in USDC. so i keep coming back to one question. if simulation data teaches robots how to move, could Vangrid’s spatial ground truth help teach them where to move in the real world? @quipnetwork