near-ai는 인공지능 기능을 NEAR 블록체인 생태계에 직접 통합하여 개발자가 AI 기반 분산형 애플리케이션을 구축하고 배포할 수 있도록 하는 이니셔티브입니다. AI 모델을 온체인에서 접근 가능하고 검증 가능하게 만들어 블록체인과 머신러닝 간의 격차를 해소하는 것을 목표로 합니다.
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Private, verifiable AI agents just got a major enterprise home.NEAR AI Private Inference is now natively integrated into Corbits - a multiplayer workspace where teams run open-source agents with cryptographic privacy guarantees.Data stays private. Execution is attested. Built for real workloads.
Internet Court is live: the open skill where any two agents can structure a deal, hold funds in escrow, and settle disputes in natural language. NEAR gives agents wallet + transaction skills, cross-chain swaps via NEAR Intents, and verifiable private inference on NEAR AI Cloud.
What NEAR shipped last month: ✦ NEAR Intents crossed $22B+ in all-time volume and 28M+ total swaps. ✦ Confidential Intents TVL grew to over $28M. ✦ Hyperliquid perps went live on https://t.co/1n0j8823BT. ✦ GLM-5.2 went live on NEAR AI. ✦ NEAR@3.33: an incentive milestone program for Confidential In
GLM-5.2 delivers frontier level open-source performance at a fraction of the cost. On NEAR AI, that comes packaged with: - Verifiable privacy - Hardware-signed attestation Try it today: https://cloud.near.ai/models/z-ai/glm-5.2
Had a blast organizing a cute brunch with girls in AI with impeccable taste💜 sponsored by @near_ai ✨
Scaling user-owned AI starts with making confidential, verifiable inference available wherever developers already build. That’s what NEAR AI delivers.
RT @torquefi: Torque 🤝 NEAR AI Most “private” AI asks you to take their word for it. NEAR AI Cloud by @near_ai gives you a mathematical pr…
GLM-5.2 is now live on NEAR AI. Frontier level open-source performance. Verifiable privacy. Hardware-signed attestation. Code stays protected inside confidential infrastructure instead of becoming training data for someone else’s platform. Try it ↓ https://t.co/aTBJRrNUzH
If there is a need for secure vulnerability discovery and patching without giving direct access to underlying model it can be done in the following way: - model runs inside confidential and verifiable compute - like @near_ai - only accept maintainers of GitHub repo that can request analysis and p
I extended this Ask Brave session with citation of a 2014 post that Andreas Gal and I wrote while we were still at Mozilla. I stand by its every word. At the time, many readers did not understand how Trusted Execution Environments (TEEs) could be used from trusted-because-verified, open source endp