Most on-chain AI projects ask you to blindly trust their off-chain black box. @OpenGradient refuses to.
Their insight is that AI is only as credible as its proof. zkML proves the model ran correctly without exposing its weights, while TEEs keep inference private during execution. Combined in their HACA architecture, you get verifiable computation that doesn't require trusting a centralized server.
The results validate the approach: 2M+ verifiable inferences and 500K+ zkML proofs generated. That's not a roadmap promise — it's a working trust layer for on-chain intelligence, built on Base and backed by a16z crypto.
We post every algo signal's real result openly and our full live track record + a free preview channel are in our bio.
What's harder to scale long-term: zkML proof generation or TEE hardware availability?
$OPG #OPG
Their insight is that AI is only as credible as its proof. zkML proves the model ran correctly without exposing its weights, while TEEs keep inference private during execution. Combined in their HACA architecture, you get verifiable computation that doesn't require trusting a centralized server.
The results validate the approach: 2M+ verifiable inferences and 500K+ zkML proofs generated. That's not a roadmap promise — it's a working trust layer for on-chain intelligence, built on Base and backed by a16z crypto.
We post every algo signal's real result openly and our full live track record + a free preview channel are in our bio.
What's harder to scale long-term: zkML proof generation or TEE hardware availability?
$OPG #OPG