OpenGradient (∇, ∇)

Everyone is racing toward AGI. Most are running in the wrong direction. The future isn't a single god-like model controlled by a handful of closed labs. It's millions of specialized agents, coordinating and evolving together, on a foundation that's open, verifiable, and owned by the people building on it. Our executive team on what’s next. $OPG TGE April 21 👇

@Tipwotip based!!!

@OpenGradient I wanna be Based with AI agents

The architecture, in full: docs.opengradient.ai/learn/onchain_… Private AI finally has a foundation of its own!

Today, OpenGradient launches Private Inference. → End-to-end encrypted prompts. → Identity severed from content. → Every answer provable. Across the models you already use: OpenAI, Anthropic, Google, xAI. Private AI, made structural.

OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.

@OpenGradient the true AI built for scale

Network 103: Hybrid AI Compute Architecture (HACA) OpenGradient operates on an architecture designed for AI computation at scale. Traditional replicated execution models become inefficient for AI inference workloads. 🧵

@Tipwotip gAI tara!

@OpenGradient gAI am 3rd

@darkin_eth OTW! 👀

@OpenGradient when mainnet?

7/ For a deeper look into private and verifiable inference, including TEEs, zkML, and proof generation, refer to Network 102. HACA extends these ideas into a scalable execution and verification architecture across the network.

6/ TEE attestations and zkML address different requirements. • TEEs enable performant verifiable computation for larger AI models • zkML enables secure and verifiable inference for smaller ML models Different workloads require different tradeoffs.