MirrorDNA: Personal AI Infrastructure on Consumer Hardware

I just published a new paper: MirrorDNA: Personal AI Infrastructure on Consumer Hardware.

It documents 10 months of building a fully sovereign AI operating system — one person, one Mac Mini M4, $120/month.

What is Personal AI Infrastructure?

Personal AI Infrastructure (PAI) is a new category of computing: an individual builds, owns, and operates a complete AI system on hardware they control. No cloud dependency. No platform governance. Everything inspectable.

MirrorDNA is a working PAI system. Here's what runs on a single Mac Mini M4 with 24 GB unified memory:

• 61 autonomous services with a cryptographic governance layer
• 85+ LaunchAgent daemons for persistent orchestration
• 260 operational scripts (66,000+ lines)
• 51,000+ note knowledge vault (17 GB, Obsidian-compatible)
• 5 edge devices in a Tailscale mesh
• 4 local Ollama models for on-device inference
• Tiered execution: Claude (Tier 1) → Gemini (Tier 2) → Local Ollama (Tier 3)

Total monthly cost: $120 (Claude Max + ChatGPT Plus). Everything else is free or self-hosted.

The Six Primitives

The paper defines six primitives required for an AI operating system:

1. Persistent Memory — A filesystem-based event store (the "bus") that survives across sessions, models, and crashes
2. Governance — Ed25519 cryptographic signing for high-risk actions, with phone-based approval workflows
3. Identity Continuity — Session crystals, handoff protocols, and continuity files that let any model resume work
4. Knowledge Management — A 51,000-note vault with YAML frontmatter, wiki-links, and autonomous maintenance (decay checks, compression, triage)
5. Tiered Execution — Routing AI reasoning across paid, free, and local models based on task complexity
6. Observation — MirrorPulse self-healing monitor, hook-based behavioral enforcement, and 30+ Claude hooks

Key Results

From 10 months of operation:

• 441 session reports crystallized with graph metadata
• 527 bus changelog entries providing an auditable trail
• 200 governance decisions across 7 hook types
• 6 Zenodo publications (4 papers + 1 erratum + 1 re-upload)
• 117 GitHub repositories (60 public, 57 private)

The system's self-healing layer (MirrorPulse) runs every 5 minutes, auto-diagnosing and fixing common failures. The governance layer has prevented real incidents — including catching hallucinated hardware specs that caused 2 published errata.

Why This Matters

Consumer hardware can now run AI infrastructure that was previously institutional-only. The M4 chip's unified memory architecture runs 4 local models alongside 60 services simultaneously. Quantized open-weight models (GGUF format) make on-device inference viable.

This isn't a framework or a library. It's a deployed, running system with persistent state, governance enforcement, and identity continuity. The difference is like the difference between Flask and a configured production web server.

The Paper

The full paper covers:

• Formal definition of PAI and its six primitives
• Complete architecture with 19 sections
• Threat model for autonomous AI operations
• Empirical evaluation from 10 months of deployment
• Comparison with AutoGPT, CrewAI, LangGraph, AutoGen, BabyAGI
• Scalability model for distributed sovereign AI meshes
• Response to initial reviewer critique

All quantitative claims are verifiable against the running system via a live stats endpoint.

Read the full paper: Available on Zenodo (DOI pending) and at activemirror.ai

Author: Paul Desai, N1 Intelligence (OPC) Private Limited, Goa, India

The model is interchangeable. The bus is identity.