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:
โข Reusable server framework for P2P networking, peer discovery, consensus startup, telemetry, P2P connection management, and node lifecycle
โข Merkle DAG framework with signed immutable DAG nodes, multi-head support, state materialization, synchronization, reconciliation, orphan recovery, and pluggable storage backends
โข DAG GossipSub base classes that handle publishing, validation, replication, synchronization, parent selection, and message routing
โข Reusable request/response protocol framework for P2P stream protocols and DAG synchronization
โข Network API bridge for external services, AI workers, dashboards, and local applications
โข Consensus, telemetry, scoring, and runtime utilities for production decentralized AI operations
โข 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.