> For the complete documentation index, see [llms.txt](https://supernet.gitbook.io/supernet/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://supernet.gitbook.io/supernet/distributed-network.md).

# distributed network

- [Supernet | Introduction](https://supernet.gitbook.io/supernet/distributed-network/supernet-or-introduction.md): By the time you reach here, you'll have discovered the coolest AI OS of the century and the decentralized autonomous network built on top of it.
- [AI Operating System](https://supernet.gitbook.io/supernet/distributed-network/ai-operating-system.md): The positioning, risks, and development of AI OS.
- [Transformer Decoder Architecture](https://supernet.gitbook.io/supernet/distributed-network/ai-operating-system/transformer-decoder-architecture.md): A detailed view of a Transformer decoder architecture with 100 billion parameters.
- [LLM OS](https://supernet.gitbook.io/supernet/distributed-network/ai-operating-system/llm-os.md): It has more knowledge than any single human about all OS.
- [AI OS Design](https://supernet.gitbook.io/supernet/distributed-network/ai-os-design.md): After exploring the features of AI OS, let’s dive into the details of its design.
- [Decentralize Validator](https://supernet.gitbook.io/supernet/distributed-network/ai-os-design/decentralize-validator.md): Our network, built on the AI OS system, is composed of decentralized validators. Let’s explore this section.
- [Decentralize Storage](https://supernet.gitbook.io/supernet/distributed-network/ai-os-design/decentralize-storage.md): In addition to nodes, there is a comprehensive data storage system that goes beyond DA, encompassing models and training data.
- [TEE-Enhanced Design](https://supernet.gitbook.io/supernet/distributed-network/tee-enhanced-design.md): TEE provides SuperNet with a robust foundation for ensuring security, privacy, and computational efficiency.
- [AVS of Agent](https://supernet.gitbook.io/supernet/distributed-network/avs-of-agent.md): Before reading this, you should understand the functionality of AVS in EigenLayer.
- [Fault Tolerance](https://supernet.gitbook.io/supernet/distributed-network/fault-tolerance.md): The current self-correcting algorithm for Agents references the sampling method of RS encoding and performs adaptive training based on it.


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