Skip to content
Home / Thesis / Experiments / BLOB

BLOB: Collaborative AI Framework

Overview

BLOB (Building Local Open Bots) is a comprehensive framework for decentralized AI governance that emerged from learnings across multiple experiments. It represents a synthesis of technical infrastructure and governance mechanisms designed to support community-controlled AI systems.

Concept

"A framework that enables communities to collectively govern AI systems while maintaining technical simplicity and community autonomy."

BLOB Framework Architecture

Core Principles

Community Governance

1. Community Sovereignty

  • Communities maintain full control over their AI systems
  • No external dependencies for core governance functions
  • Local data storage and processing capabilities

2. Adaptive Governance

  • Flexible governance structures that can evolve with community needs
  • Transparent decision-making processes
  • Clear mechanisms for community input and oversight

3. Technical Accessibility

  • Simplified deployment and maintenance
  • Minimal technical expertise required for operation
  • Clear documentation and support resources

Framework Architecture

Governance Layer

Community Decision-Making Tools

  • Proposal and voting systems
  • Model evaluation and selection processes
  • Privacy and data handling policies
  • Usage guidelines and community standards

Transparency Mechanisms

  • Public logs of AI decisions and reasoning
  • Community audit capabilities
  • Model performance metrics and reporting
  • Clear documentation of system limitations
Distributed Infrastructure
Local Processing

Technical Layer

Local Infrastructure

  • Edge computing deployment options
  • Model quantization for resource efficiency
  • Containerized deployment for easy setup
  • Backup and recovery systems

Adaptability Features

  • Modular architecture for community customization
  • Plugin system for extending functionality
  • Integration capabilities with existing community tools
  • Version control and update mechanisms

Implementation Strategy

Phase 1: Core Framework Development

  • Basic governance interface
  • Local AI model deployment
  • Essential transparency tools
  • Community testing and feedback

Phase 2: Community Pilot Programs

  • Partner with interested communities
  • Real-world testing and iteration
  • Documentation of best practices
  • Training material development

Phase 3: Open Source Release

  • Full framework release under open license
  • Community contribution guidelines
  • Support network establishment
  • Scaling and replication resources
Development Roadmap

Key Features

Community Dashboard

Governance Interface

Community Dashboard

  • Overview of AI system status and usage
  • Community voting and proposal tools
  • Performance metrics and reports
  • Configuration and policy management

Decision-Making Tools

  • Structured proposal system
  • Multi-stage voting processes
  • Consensus-building mechanisms
  • Conflict resolution procedures

Technical Components

Model Management

  • Local model deployment and updates
  • Performance monitoring and optimization
  • Resource usage tracking
  • Security and privacy controls

Data Governance

  • Local data storage and processing
  • Privacy protection mechanisms
  • Data retention and deletion policies
  • Community data ownership enforcement
Technical Architecture

Research Questions Addressed

How can communities maintain meaningful control?

  • Through transparent governance interfaces
  • Local deployment eliminating external dependencies
  • Clear decision-making processes and documentation

What makes AI governance accessible?

  • Intuitive user interfaces for non-technical users
  • Comprehensive documentation and training materials
  • Community support networks and peer learning

How do we balance simplicity with functionality?

  • Modular architecture allowing gradual feature adoption
  • Core functionality prioritizing essential governance needs
  • Optional advanced features for communities ready to adopt them

Current Development Status

Completed Components

  • Basic governance interface mockups
  • Local AI deployment proof-of-concept
  • Community consultation framework
  • Initial documentation and guidelines

In Development

  • Full governance dashboard implementation
  • Automated deployment system
  • Community testing protocols
  • Training and support materials

Future Work

  • Multi-community federation capabilities
  • Advanced privacy protection features
  • Economic sustainability models
  • Long-term maintenance strategies

Community Testing

Community Workshop

Pilot Community Characteristics

  • Small to medium-sized communities (50-500 members)
  • Existing governance structures
  • Interest in AI applications for community benefit
  • Willingness to participate in iterative development

Testing Methodology

  • Participatory design sessions
  • Regular feedback collection
  • Performance monitoring
  • Long-term sustainability assessment

Technical Specifications

Deployment Requirements

  • Hardware: Standard server or high-end desktop computer
  • Software: Containerized deployment (Docker)
  • Network: Standard internet connection
  • Maintenance: Weekly check-ins, monthly updates

Supported AI Models

  • Local language models (Ollama, Hugging Face)
  • Vision models for image processing
  • Specialized models for community-specific applications
  • Plugin architecture for custom model integration

Connection to Other Experiments

Building on Previous Work

  • Slack Workspace: Informed simplicity requirements
  • LAIA: Demonstrated community-specific application needs
  • LLUM 2025: Explored public engagement possibilities
  • Oatflake: Validated local deployment approaches

Informing Future Development

  • Framework serves as foundation for community AI initiatives
  • Governance principles applicable across different contexts
  • Technical patterns reusable for various community needs
Experimental Connections

Impact and Implications

For Communities

  • Increased autonomy over AI technologies
  • Reduced dependence on commercial platforms
  • Enhanced capacity for collective decision-making
  • Improved alignment between AI behavior and community values

For AI Development

  • Demonstrates viability of community-controlled AI
  • Provides alternative to centralized AI governance
  • Creates feedback loops between communities and developers
  • Establishes new models for AI system ownership

Conclusion

BLOB represents a comprehensive approach to collaborative AI governance that prioritizes community sovereignty while maintaining technical feasibility. By synthesizing learnings from multiple experiments, the framework offers a practical path toward AI systems that truly serve community interests and reflect local values.

The ongoing development of BLOB continues to be informed by community feedback and real-world testing, ensuring that the framework remains responsive to the needs of the communities it aims to serve.

Last update: June 29, 2025