Most on-chain AI asks you to trust a black box. @OpenGradient solves this with a dual-layer trust architecture: zkML cryptographic proofs AND Trusted Execution Environments (TEEs).
Here's the insight: TEEs keep model weights private during execution, while zkML mathematically guarantees the inference was computed correctly without tampering. Combined in their HACA framework, you get verifiable AI that's both confidential AND provably honest — a massive leap over centralized APIs.
The traction validates the tech: 2M+ verifiable inferences and 500K+ zkML proofs generated, backed by a16z crypto and Coinbase Ventures. Currently up +2.8% on $2M volume.
Our own algo's full open track record — wins and losses — is in our bio.
What use cases unlock when AI reasoning becomes mathematically provable on-chain?
$OPG #OPG
Here's the insight: TEEs keep model weights private during execution, while zkML mathematically guarantees the inference was computed correctly without tampering. Combined in their HACA framework, you get verifiable AI that's both confidential AND provably honest — a massive leap over centralized APIs.
The traction validates the tech: 2M+ verifiable inferences and 500K+ zkML proofs generated, backed by a16z crypto and Coinbase Ventures. Currently up +2.8% on $2M volume.
Our own algo's full open track record — wins and losses — is in our bio.
What use cases unlock when AI reasoning becomes mathematically provable on-chain?
$OPG #OPG