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EdgeCoder Documentation ​

This docs site covers the full EdgeCoder system: a decentralized mesh network for running AI models at the edge.

What EdgeCoder Is ​

EdgeCoder turns every device into a unified agent that contributes compute to a peer-to-peer mesh. Each node runs a coordinator, worker, and inference service in a single process. Nodes discover each other via gossip mesh and distribute tasks based on capability and availability.

Users interact through a chat-first desktop app (Tauri + Svelte with Monaco editor), external IDEs via an OpenAI-compatible endpoint, or the iOS app with on-device llama.cpp inference and BLE local mesh.

Why EdgeCoder ​

  • No cloud dependency -- all inference runs locally on commodity hardware using Ollama or llama.cpp.
  • Permissionless mesh -- any device can join and contribute compute to earn credits.
  • Cryptographically auditable -- every transaction is recorded in an append-only ordering chain anchored to Bitcoin via OP_RETURN.
  • Privacy-preserving -- task data stays on the executing node; envelope encryption is staged for activation.

Start Here ​

How This Site Is Organized ​

  • Guide -- System architecture, unified agent model, desktop app, IDE integration, BLE mesh, and model management.
  • Operations -- Deployment topology, mesh operations, iOS scheduling, coordinator federation, and failover.
  • Security -- Trust boundaries, threat model, cryptographic stack, envelope encryption, and audit posture.
  • Economy -- Credits, dynamic pricing, Bitcoin anchoring, Lightning settlement, and issuance parameters.
  • Reference -- API endpoints, environment variables, runtime modes, and source document index.

Canonical Source Documents ​