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    Vangrid's bid to gather real-world spatial data through smartphones

    A user says phones could collect data for location-based bounties, with privacy protected on-device.

    3 Sources, 2h ago, first seen 2h ago

    TLDR

    One user describes Vangrid as building a decentralized network where phones could collect spatial data and bounties could coordinate location-based requests. Another says the system assigns provenance hashes to observations extracted from external video, including drone or vehicle-mounted footage; the hashes can later be checked through a verification endpoint. They see fresh, traceable real-world data as useful for physical AI.

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    3 Sources, first seen 2h ago

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    3 Sources, first seen 2h ago

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    3 Sources

    Gujil Ruipa 🪑@GujilRuipaPhysical AI needs more than powerful models it needs fresh high quality data from the real world thats what makes @vangrid_io interesting to me Vangrid is building a decentralized perception network where smartphones can become distributed capture points for spatial data while privacy is protected on device the bigger idea is even more interesting A location can become a data request, bounties can coordinate the capture, and contributors can use their phones to collect what’s needed With onchain provenance the data also gets a transparent record of where it came from As robotics moves from labs into everyday life, fresh environmental data could become just as important as the models themselves thats why i am watching Vangrid gVangrid guys 👋2h
    JUNIOR CRYPTO@Junior_crypto_0When AI uses physical world data, one useful question is: Can we trace the information back to where it came from? @vangrid_io addresses this through its provenance system. When video from an external sensor is processed, @vangrid_io extracts spatial observations and gives each one a "provenance_hash". That hash acts as a reference for the observation’s integrity and can later be checked through the verification endpoint. So the process isn’t just: Video → Data It becomes: Video → Observation → Provenance → Verification What I find interesting is that this applies to externally submitted footage too. The source can be a drone, fixed camera or vehicle mounted sensor, while the resulting observation still enters Vangrid’s broader provenance architecture. That makes the data easier to trace and verify after it has been produced.1h
    Ali@Svrkee01@HuuHoang88 @vangrid_io The direction seems promising without needing exaggerated claims.1h

    3 Sources

    Gujil Ruipa 🪑@GujilRuipaPhysical AI needs more than powerful models it needs fresh high quality data from the real world thats what makes @vangrid_io interesting to me Vangrid is building a decentralized perception network where smartphones can become distributed capture points for spatial data while privacy is protected on device the bigger idea is even more interesting A location can become a data request, bounties can coordinate the capture, and contributors can use their phones to collect what’s needed With onchain provenance the data also gets a transparent record of where it came from As robotics moves from labs into everyday life, fresh environmental data could become just as important as the models themselves thats why i am watching Vangrid gVangrid guys 👋2h
    JUNIOR CRYPTO@Junior_crypto_0When AI uses physical world data, one useful question is: Can we trace the information back to where it came from? @vangrid_io addresses this through its provenance system. When video from an external sensor is processed, @vangrid_io extracts spatial observations and gives each one a "provenance_hash". That hash acts as a reference for the observation’s integrity and can later be checked through the verification endpoint. So the process isn’t just: Video → Data It becomes: Video → Observation → Provenance → Verification What I find interesting is that this applies to externally submitted footage too. The source can be a drone, fixed camera or vehicle mounted sensor, while the resulting observation still enters Vangrid’s broader provenance architecture. That makes the data easier to trace and verify after it has been produced.1h
    Ali@Svrkee01@HuuHoang88 @vangrid_io The direction seems promising without needing exaggerated claims.1h