Ghost Neural Network (GNN): A Local-First Architecture for Autonomous AI Agents

This post is intended as a technical overview of an architecture called Ghost Neural Network (GNN), focused on design choices rather than token economics or market considerations.

Ghost Neural Network is a framework for running stateful, autonomous AI agents (initially applied to trading systems) with an emphasis on local execution, fault tolerance, and deterministic recovery.

Problem being addressed

Most automated agent systems today rely on:
• Always-on centralized servers
• Stateless restarts after failure
• Cloud orchestration that obscures agent state and decision paths

This makes recovery, auditing, and long-running autonomy difficult.

GNN explores a different approach.

Architectural approach
• Local-first execution
Agents are designed to run directly on user hardware (browser, desktop, edge devices), reducing reliance on centralized infrastructure and minimizing trust assumptions.
• Session-based lifecycle
Agents operate within explicit sessions that maintain checkpoints and write-ahead logs. This allows agents to resume from known-good states after crashes or interruptions rather than restarting from zero.
• Deterministic control layer
Core logic is implemented using finite-state machines with explicit transitions. This improves inspectability, reproducibility, and bounded behavior compared to opaque black-box systems.
• Decentralized compute escalation
When local resources are insufficient, agents can lease external compute from a decentralized network rather than defaulting to centralized cloud providers.

Blockchain integration (minimal)

A blockchain layer (Solana) is used primarily for:
• Session access control
• Metering and settlement for external compute
• Incentivizing compute providers
• Potential governance primitives

The token is usage-coupled rather than inflation-scheduled.

Reference contract (Solana):
5EyGMW1wNxMj7YtVP54uBH6ktwpTNCvX9DDEnmcsHdev
(Provided for technical verification and transparency.)

Why this is interesting from a systems perspective
• Emphasizes state durability and recovery in autonomous agents
• Treats AI agents as long-lived processes, not disposable jobs
• Combines edge execution with optional decentralized compute
• Avoids assuming continuous connectivity or centralized orchestration

TL;DR

Ghost Neural Network is an experiment in building long-running, fault-tolerant AI agents using local execution, deterministic state machines, and decentralized compute coordination, with blockchain used as an enabling layer rather than the core focus.

Posting for technical discussion and critique.