Most on-chain AI projects ask you to blindly trust their off-chain inference servers. @OpenGradient solves this with a dual-layer trust model.
zkML generates cryptographic proofs that the model actually ran correctly. TEEs (Trusted Execution Environments) isolate execution so even node operators can't tamper with inputs or outputs. Together they make verifiable AI inference real.
The proof is in adoption: 2M+ verifiable inferences and 500K+ zkML proofs already generated. Backed by a16z and Coinbase Ventures, this is infrastructure that matters.
Down 5.7% today on thin volume, but the tech thesis is what counts here.
Our own algo's full open track record (wins and losses) is in our bio if you want transparency.
What's harder to scale long-term — zkML proof generation speed or TEE hardware availability?
$OPG #OPG
zkML generates cryptographic proofs that the model actually ran correctly. TEEs (Trusted Execution Environments) isolate execution so even node operators can't tamper with inputs or outputs. Together they make verifiable AI inference real.
The proof is in adoption: 2M+ verifiable inferences and 500K+ zkML proofs already generated. Backed by a16z and Coinbase Ventures, this is infrastructure that matters.
Down 5.7% today on thin volume, but the tech thesis is what counts here.
Our own algo's full open track record (wins and losses) is in our bio if you want transparency.
What's harder to scale long-term — zkML proof generation speed or TEE hardware availability?
$OPG #OPG