gVangrid/gQuip frens.
The more I look into physical AI, the clearer one thing becomes: robots donโt just need smarter models , they need a better understanding of the world around them.
Thatโs the part of the robotics stack
@vangrid_io is focused on.
It turns Android phones into edge nodes that capture real-world spaces, transforming them into point clouds and Gaussian splats for robotics and AI. Raw footage stays private off-chain, while each capture is anchored on Base and attested through EAS.
The current numbers are already interesting:
- 1,024,912 captures
- 423,143 active nodes
- 3,823 EAS-attested Merkle trees
- $311K settled in USDC
My insight: robots will eventually operate everywhere , not only inside controlled warehouses. The data used to train them must come from streets, stores, homes, and loading docks too.
Vangrid is building a distributed layer for that missing spatial ground truth.
app.vangrid.io/
Also looking into
@quipnetwork .
Quip uses Substrateโs modular runtime and BABE-GRANDPA consensus to support upgrades toward quantum-resistant accounts and transactions without relying on contentious hard forks.
The interesting part is how it applies Polkadot-style resource allocation to compute: job bids can compete for scarce quantum and classical capacity, while veQUIP-directed emissions help route rewards.
But Quip isnโt a relay chain of parachains. Its subnets represent useful problem classes like Ising optimization, QVRF, and hidden-subgroup cryptanalysis.