CLSTR - Decentralized AI: Super Swarm of Models

The goal is to build a decentralized AI ecosystem where a super swarm of specialized, smaller models collaborates to perform complex tasks, coordinated through a community-driven, modular architecture and governed by DAOs that manage LLM weights. This approach sidesteps the resource-intensive, monolithic LLMs of centralized giants, offering asymmetric advantages in efficiency, innovation, and community engagement.

Definition

A "super swarm" refers to a decentralized network of AI models, each specialized for specific tasks or domains, that collaborate to perform complex, multi-step tasks. Unlike a single, general-purpose LLM, which handles diverse tasks within one model, a super swarm distributes intelligence across many smaller models, coordinated through a decentralized protocol. This mimics biological swarms (e.g., ant colonies), where simple agents collectively achieve sophisticated outcomes.

Join the Swarm

Fine-Tune Specialized Models

Fine tune models for Rust, Solidity, or Move smart contract generation, or other specialities. Contribute lightweight, specialized models that excel in specific domains while using minimal compute resources.

Dataset Curation & Training

Curate high-quality training datasets for smart contract languages. Earn tokens for providing clean, well-labeled data that improves model performance across the swarm.

Model Testing & Validation

Test fine-tuned models, validate smart contract outputs, and provide feedback on model performance. Help ensure the swarm maintains high-quality, reliable code generation.

DAO Governance Participation

Vote on model weights, fine-tuning parameters, and ecosystem direction. Shape which programming languages and frameworks the swarm should support next.

Smart Contract Coordination

Participate in the blockchain-coordinated model sharing and task allocation. Help route requests to the most suitable specialized models in the swarm.

Compute Efficiency Rewards

Earn bonus tokens for creating models that achieve high performance with minimal computational requirements, supporting the swarm's distributed architecture.