Community Scale AI: Protocols, Infrastructure, and Local Stewardship

The SIGPfB meeting focused on designing community-scale AI infrastructure that prioritizes local stewardship and human-centered interfaces over traditional software disruption. Boris, Jacob, and Rafa outlined a four-phase architecture: Phase 1 establishes a membership base with OpenAI-compatible API access; Phase 2 introduces a shared data commons and context harness; Phase 3 enables local clients with CRDT-based data mirroring; and Phase 4 creates a federated network where members run their own nodes over open protocols. The technical implementation uses infrastructure-as-code with Proxmox, Ansible, and AT Protocol, with public repositories at zSpace Society.

Key points

• Self-hosting is only the easy part; the real challenge is building multiplayer systems that serve communities rather than individuals, requiring human support baked into the stewardship model from the start.
• Open source models lag frontier AI by roughly six months but are 'more than good enough' for community-scale applications, with a clear path to running inference locally via CRDTs and local clients.
• The solution to increasingly automated systems is paradoxically human-centered: chat-first interfaces, community context pipelines, and accessibility to non-technical users as the primary litmus test.
• A federated protocol-based approach (Phase 4) enables members to run their own nodes and leave at any time, avoiding platform lock-in while maintaining collaborative shared models over open protocols.
• The model is being validated through Jacob's 84-unit housing co-op and tested for replication in other communities (Salmon Arm, Vancouver coworking spaces), suggesting broader applicability beyond tech communities.