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:
- Persistent Memory — A filesystem-based event store (the "bus") that survives across sessions, models, and crashes
- Governance — Ed25519 cryptographic signing for high-risk actions, with phone-based approval workflows
- Identity Continuity — Session crystals, handoff protocols, and continuity files that let any model resume work
- Knowledge Management — A 51,000-note vault with YAML frontmatter, wiki-links, and autonomous maintenance (decay checks, compression, triage)
- Tiered Execution — Routing AI reasoning across paid, free, and local models based on task complexity
- 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.
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