Distributed Knowledge

Distributed Knowledge aims to fundamentally transform our approach to Large Language Models (LLMs) by unifying the entire internet into a single, federated model. Instead of relying on monolithic or proprietary systems, this approach leverages a decentralized network where each node independently contributes to a shared "collective context." This eliminates central control, fostering collective intelligence through distributed collaboration.

Why Distributed Knowledge?

Traditional LLM systems face significant limitations, including outdated knowledge, high resource demands, privacy issues, and centralized ownership. Distributed Knowledge addresses these challenges by providing:

Collective Intelligence: Aggregating insights across a distributed user network.
Real-time Knowledge: Ensuring information stays current without constant retraining.
Privacy-Preserving: Architecture: Protecting sensitive data while sharing common insights.
Resource Efficiency: Reducing computational overhead by distributing workloads.
Public Ownership: Maintaining open, decentralized control rather than proprietary ownership.

Key Features

Federated Architecture: Decentralized by design, with no central control point
Hybrid Privacy Model: Public when it matters, private when it counts
Dynamic Knowledge: No more retraining, the model evolves with every interaction
Autonomous Operation: Self-organizing systems that adapt and evolve
Open Ecosystem: Not owned or controlled by any single entity

Technical Capabilities

Privacy by Design: Your data is not shared with the network without your consent
End-to-End Encryption: Network peers are authenticated with encrypted messages
Real-time Synchronization: Unlock access to your network's data in real-time
Unified Context: Access to a network-wide contextual knowledge base
Ollama Compatible: Connect and run your favorite Ollama models
MCP Compatible: Fully compatible with regular MCP Hosts

Get started with Distributed Knowledge by visiting the Getting Started guide.