HuuHoang88
HuuHoang88
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Privacy is a major issue when mapping the physical world.
@vangrid_io approaches it at the edge.
Sensitive details like faces and license plates can be blurred before the capture leaves the device.
That matters.
Spatial intelligence becomes much more useful when privacy is built into the pipeline, not added later.
Web3 doesn’t need every user to start from zero.
We already have years of valuable data across Web2 platforms. The challenge is bringing that data into Web3 in a way that can be trusted.
That’s why I’m keeping an eye on @primus_labs.
Verifiable data could open up some interesting possibilities for identity, reputation and onchain applications.
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.
