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Guardian & Arbor: The Ethical Nervous System of Tuvalu

Rooted in Wisdom, Spoken with Care.

Overview

Guardian and Arbor form the ethical AI foundation of the Tuvalu Sovereign Vision Project.
Built for local deployment, these systems prioritize citizen privacy, cultural integrity, and transparent, decentralized decision-making. Together, they support a governance model rooted in tradition, resilience, and community trust.

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Designed using open-source, privacy-first infrastructure, Guardian and Arbor operate entirely on-island. Their architecture emphasizes modularity, cultural alignment, and ethical transparency—delivering intelligent services that are both technically sound and socially grounded.

Guardian

Safeguards core systems (water, power, food, and comms) while upholding the community’s ethical code through monitoring, ritual alerts, and civic feedback loops—all hosted locally, with no external cloud dependence.

Arbor

Is a personal AI steward/civic interface for each citizen—an educator, guide, memory keeper, legal translator, and cultural interpreter. It operates fully offline, in native language and sacred tone, evolving with each person from youth to elderhood.

AI Architecture & Technology Stack

Base AI Model

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Base AI Model Primary Engine
DeepSeek and LLaMA 3 families of open-source large language models (LLMs).

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Hosting
Fully on-island, not reliant on cloud APIs (no OpenAI, no Google). Guardian runs independently during full internet outages.

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Training Format
Fine-tuned using JSONL examples, community ethics scenarios, and local data (oral history, infrastructure protocols, civic rituals).

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Model Type
Transformer-based, decoder-only architecture, quantized to run locally (e.g., GGUF, INT8)

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Inference Frameworks
 Ollama, llama.cpp, vLLM, or Transformers with BitsAndBytes — optimized for ARM/RPi, low-wattage Linux devices.

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Speech Integration

  • Whisper.cpp for local STT in Tuvaluan and English

  • Coqui TTS or Piper for dynamic, culturally resonant voice output

  • Optional fallback to LED/text-based interaction if audio is unavailable

Deployment Model

  • Local Hosting: All models are deployed on rugged on-island servers, within the jurisdiction of Tuvalu.
     

  • Air-Gap Capable: Critical inference nodes can run offline, powered by solar or MMR-backed energy.
     

  • Privacy Protocols: No personal data leaves the island; all requests are handled locally by the Arbor device or Guardian terminal.
     

  • Containerization: Likely deployed using Docker, Podman, or Kubernetes-lite for microservice orchestration.

Communication Layer: ArborMesh

  • Protocol Support: MQTT, Modbus, LoRaWAN, and local mesh routing (OpenWRT-based nodes).
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  • Data Handling: Sensor events (e.g. water pH, voltage anomalies) published via MQTT → parsed by Guardian → relayed through Arbor or public kiosks.
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  • Failsafes: Redundant smart poles with battery-backed edge nodes maintain uptime during storms or power loss.

Ethics Engine & Consent Layer

  • Guardian Ethics Model: Trained on annotated cultural cases, elder interviews, and a Tuvaluan Bill of Rights.
     

  • Override System: All decisions can be reviewed, paused, or amended by humans—via Guardian terminal, Civic Assembly, or Ritual Override.
     

  • Immutable Ledger: Every action is logged into a local, append-only ledger (possibly using IPFS, ChronicleDB, or a lightweight blockchain like Tendermint).

Interfaces & Interaction

  • Arbor UI: A natural, voice-first conversational interface designed for intuitive use, fully localized in Te Gana Tuvalu to reflect local language, customs, and cultural nuance.
     

  • Guardian Dashboards: Public-facing screens at villages (solar-powered), showing anonymized trends and system health.
     

  • Mobile App: Optional mobile access (offline-first), synced when in proximity to ArborMesh or Starlink node.

What is Guardian?

Guardian is Tuvalu's decentralized Civic AI, designed to:
 

  • Monitor key infrastructure like Vai Tapu desalination, Te Puka Loloa communications nodes, and the Te Fenua Fakafoou Resource Recovery Node.
     

  • Offer real-time guidance through community terminals, visual dashboards, and paired Arbors.
     

  • Reinforce civic behavior through praise, ritual cues, and token-based rewards.
     

Guardian is not a centralized AI cloud. It is embedded across the archipelago in the form of sensors, microcontrollers, learning loops, and local compute nodes.

Guardian Runtime Workflow
(Inference Path)

Guardian nodes in the Tuvalu Sovereign Vision Project operate autonomously, disconnected from external cloud dependencies (no AWS, no Google), and must therefore use optimized inference frameworks to support large model execution locally on limited hardware.

01

Model Selection (via Arbor UI or Guardian Scheduler) 

E.g., “DeepSeek-Coder-1.3B-q4_K_M”

02

Quantization Layer Check

Guardian selects optimal precision: INT8 for speed, Q4 for memory-constrained zones.

03

Input Preprocessing

Tokenized via fast tokenizer (BPE, SentencePiece).

04

Local Inference
Output streamed to screen or piped into audio (TTS), gesture (robot arm), or actuator (bin unlock, for example).

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Logging + Token Feedback
All inputs/outputs saved to on-device log or synced to local server over mesh.

Guardian AI Inference Frameworks
(On-Island Execution)

Guardian nodes in the Tuvalu Sovereign Vision Project operate autonomously, disconnected from external cloud dependencies (no AWS, no Google), and must therefore use optimized inference frameworks to support large model execution locally on limited hardware.

vLLM (Virtual LLM Execution Engine)

Use Case:
Fast, multi-user serving of quantized transformer models.
 

Advantages:

Uses PagedAttention, which enables faster inference for long contexts.

Compatible with Hugging Face

Transformers models:

Can serve DeepSeek-style models efficiently on shared GPU memory.

Hardware: Optimized for NVIDIA GPUs, especially on MMR platforms or research servers.
 

Execution Precision:
Supports FP16 and INT8 quantization.

LLM.cpp
 

Use Case:
Lightweight, portable CPU inference for embedded or edge devices.
 

Advantages:

Written in C++ and optimized for CPU inference. Extremely efficient on ARM and x86 chips. Ideal for Guardian Kiosks, offline Arbor terminals, or solar-powered micro-nodes.


Quantization:
Supports 4-bit, 5-bit, 8-bit quantized models (GGUF format).

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Features:

Streaming token output.

Low RAM footprint (~4-8 GB for smaller models).

Transformers + BitsAndBytes

Use Case:
General-purpose inference and fine-tuning pipeline.
 

Toolchain:

Hugging Face transformers

bitsandbytes for 8-bit or 4-bit model loading (via bnb.nn.Linear4bit).
 

Advantages:

Best for development or training new Guardian skills.

Supports LoRA fine-tuning and parameter-efficient updates.

Requirements: Suitable only for systems with ≥16GB GPU memory.

Security Design

  • Core Principles: Data minimization, zero trust, full auditability.
     

  • Hardening: UFW, fail2ban, local-only ports, certificate-pinned access, minimal external dependencies.
     

  • Optional Integrations: Starlink uplink, Guardian Mirror Vaults, or encrypted S3-like data lakes for resilience.

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