@hyper_tensor Hypertensor is cookin' up a fully private, decentralized layer for AI inference. They’re running strictly off the grid using TEEs (Trusted Execution Environments) to lock things down, throwing in noise encryption to keep data scrambled, and utilizing a DAG chain to handle those heavy inference cycles without a hitch. It’s all about staying decentralized and keeping everything 100% locked. $TENSOR

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

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Another update complete: Frameworkized Subnet Template The subnet template has evolved from a reference implementation into a developer framework for building decentralized AI networks on Hypertensor. This update introduces reusable framework components that handle the common decentralized infrastructure layer, allowing developers to focus on application logic. What's included: • Reusable server framework for P2P networking, peer discovery, consensus startup, telemetry, P2P connection management, and node lifecycle • Merkle DAG framework with signed immutable DAG nodes, multi-head support, state materialization, synchronization, reconciliation, orphan recovery, and pluggable storage backends • DAG GossipSub base classes that handle publishing, validation, replication, synchronization, parent selection, and message routing • Reusable request/response protocol framework for P2P stream protocols and DAG synchronization • Network API bridge for external services, AI workers, dashboards, and local applications • Consensus, telemetry, scoring, and runtime utilities for production decentralized AI operations • Example implementations for DAG replication, peer state publishing, commit/reveal workflows, monitoring, and server lifecycle management The goal is simple: Give developers the substrate required to build a decentralized network that handles proof-of-useful-work AI workloads, so that builders can focus on application-layer logic. This provides a reusable foundation for decentralized inference networks, agent systems, marketplaces, data networks, and other distributed AI applications built on Hypertensor.