HuuHoang88
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I like the demand-driven model behind @vangrid_io.
Someone needs spatial data from a location.
A bounty is created.
A contributor captures it.
The accepted submission becomes useful 3D data.
Simple loop:
Demand → Capture → Verify → Reward.
Much better than collecting random data and hoping someone wants it later.
AI needs data, but that doesn’t mean every piece of data should be exposed.
@primus_labs is exploring this problem with zkFHE, combining encrypted computation with proofs that help verify the results.
For me, that’s an interesting direction for AI: making data useful while keeping sensitive information protected.
One thing I like about @primus_labs is how it approaches the gap between Web2 and Web3.
With zkTLS, data from existing websites can become verifiable without requiring users to reveal everything.
Imagine proving something about your online activity without handing over your entire account.
That’s a use case worth exploring.
The smartest part of @vangrid_io might be the hardware strategy.
Instead of deploying expensive sensor fleets everywhere, the network uses something billions of people already carry:
A smartphone.
Less infrastructure.
More coverage.
Faster data collection.
That can become a powerful advantage for Physical AI.
