One thing about training AI on the physical world: the places that matter most aren't always the ones that get mapped properly. A delivery entrance behind a building. A narrow passage between shops. The back of a warehouse where workers actually move goods. These aren't impressive locations to look at, but for a robot trying to navigate them, missing information can become a real obstacle. That’s where @Vangrid_io has a different approach to spatial data. Its grid can turn smartphone captures from ordinary environments into 3D data that machines could eventually use to understand those spaces. The bigger opportunity isn't just building another digital map of the world. It's filling in the gaps that conventional mapping may overlook, especially in places that change or rarely get documented. Of course, capturing those places is only the first step. The data still has to be accurate, useful and accessible to the systems that need it. But I like the direction: instead of expecting every corner of the world to be mapped already, build a network that can help map what’s missing. vangrid.io/