This project needs a lot of time and hardware requirements. Help me accomplish my goal of serving humanity new technology for better life. Buy me a coffee! >> https://ko-fi.com/joedeldalioan

or send me some love via paypal or crypto >>

• Paypal: paypal.me/Dalioan
• Bitcoin (Unisat): bc1qu8pknsrwvssspq4c6a5j4p3x835phq8n3ajcgw
• Solana: FNEdD3PWMLwbNKxtaHy3W2NVfRJ7wqDNx4M9je8Xc6Mw
• Tron: TGVrpryTJAmWEPWTKYB3tGRv5gWhE2g9Lo
• Metamask: 0x9F32B8346bD728DF3AB7775971705D02fb86dD9c

AI Quantum-Secure Dynamic Mesh Ledger (QSDM+)

Overview

QSDM+ is a scalable, AI-integrated, hardware-optimized architecture designed to provide a quantum-secure dynamic mesh ledger. It progressively evolves from a 2D to a 3D mesh network, balancing adoption and innovation while optimizing for mid-tier hardware and leveraging AI for automation and threat detection.

Core Innovations

• Phased Evolution: Gradual transition from 2D to 3D mesh to ensure stability and innovation.
• Hardware-Aware Design: Optimized for mid-tier PCs (32GB RAM, GTX 3050, HDD) with cloud offloading capabilities.
• DeepSeek-R1 AI Integration: Automates submesh balancing, threat detection, and documentation.

System Architecture

Phase 1: 2D Mesh Launch (Stability, Bootstrapping)

• Networking: libp2p (Go) generates submesh templates.
• Consensus: Simplified Proof of Entanglement (PoE) with 2 parent cells detects early double-spending.
• Storage: SQLite with Zstandard compression auto-documents node setup guides.
• Cryptography: CRYSTALS-Dilithium (OQS library) for quantum-safe signatures.
• Node Types: Light (Python) and Full (Go) nodes, recommended submeshes by AI.
• Hardware Use: 8GB RAM, 2 CPU cores, GTX 3050 for ONNX inference, 500GB HDD for compressed cells.

Key Workflow:

• User creates transaction → DeepSeek-R1 suggests optimal submesh (e.g., "Micropayments-EU").
• Node validates via 2D PoE and syncs via libp2p.
• SQLite stores cells; AI compresses older data to HDD.

Phase 2: AI-Driven Growth (Scaling, Automation)

• Submesh Balancing: Scikit-learn (Python) + ONNX Runtime predict traffic spikes and rebalance nodes.
• WASM SDK: Emscripten + WASMEdge generate SDK code snippets.
• Database: ScyllaDB (SSD-optimized, HDD-compatible) analyzes storage patterns for defragmentation.
• Validation: Federated Learning (PyTorch) trains spam-detection models across nodes.
• Hardware Use: GTX 3050 (FP16 models), 16GB RAM for WASM runtime, 800GB HDD for ScyllaDB, CPU/GPU hybrid training.

Key Workflow:

• AI predicts congestion (e.g., "Micropayments-Asia") → reroutes transactions to idle submeshes.
• Developers use WASM SDK to build wallets with auto-validation.
• Federated learning updates spam filters without central servers.

Phase 3: 3D Dynamic Mesh (Decentralization, Self-Healing)

• 3D Mesh: Rust + CUDA (GTX 3050) designs 3D entanglement links.
• Quarantines: Self-healing modules (Rust) identify attack patterns in real time.
• Governance: Reputation DAO (Solidity-compatible) drafts governance proposals.
• Cloud Offloading: Celery + Redis provide burst compute for 3D simulations.
• Hardware Use: 24GB RAM (in-memory submesh graphs), GTX 3050 for parallel validation, CPU-bound BFT consensus, AWS Lambda on-demand.

Key Workflow:

• Malicious cells trigger entanglement bomb → AI isolates submesh.
• GTX 3050 accelerates 3D PoE validation.
• Reputation-weighted voting retires poorly performing submeshes.

Visualization: How QSDM+ Development Process Works

flowchart TD
    A[Phase 1: 2D Mesh Launch] --> B[Phase 2: AI-Driven Growth]
    B --> C[Phase 3: 3D Dynamic Mesh]

    subgraph Phase1 [Phase 1: 2D Mesh Launch]
        A1[User creates transaction]
        A2[DeepSeek-R1 suggests optimal submesh]
        A3[Node validates via 2D PoE]
        A4[SQLite stores cells; AI compresses data]
        A1 --> A2 --> A3 --> A4
    end

    subgraph Phase2 [Phase 2: AI-Driven Growth]
        B1[AI predicts traffic spikes]
        B2[Rebalance nodes]
        B3[Developers build wallets with WASM SDK]
        B4[Federated learning updates spam filters]
        B1 --> B2 --> B3 --> B4
    end

    subgraph Phase3 [Phase 3: 3D Dynamic Mesh]
        C1[Malicious cells trigger entanglement bomb]
        C2[AI isolates submesh]
        C3[GTX 3050 accelerates 3D PoE validation]
        C4[Reputation-weighted voting retires submeshes]
        C1 --> C2 --> C3 --> C4
    end

Loading

Hardware Optimization Summary

Resource	Phase 1	Phase 2	Phase 3
RAM	8GB (nodes)	16GB (WASM + AI)	24GB (3D mesh) + Cloud
GPU	ONNX inference	FP16 training	CUDA parallelism
Storage	SQLite (500GB HDD)	ScyllaDB (800GB HDD)	ScyllaDB + Cloud archival

Mitigated Limitations

• Complexity: Phase 1 uses DeepSeek-R1 to auto-document node setup; Phase 3 uses WASM to abstract entanglement logic.
• Bootstrapping: Phase 1 attracts niche submeshes (e.g., "Healthcare-Africa"); Phase 2 AI rebalances nodes to fill gaps.

Comparative Analysis

Example Deployment

• Developer uses WASM SDK to build a micropayment app.
• Node Operator runs a Go-based full node on their GTX 3050 PC, earning fees from "Micropayments-EU".
• Attack Attempt: DeepSeek-R1 flags suspicious cells → quarantine triggers → healthy nodes reroute.

Conclusion

QSDM+ harmonizes cutting-edge technology (quantum-safe mesh, AI-driven optimization) with real-world hardware constraints. Leveraging DeepSeek-R1 for automation and phased rollout, it achieves scalability without sacrificing accessibility, evolving from a simple 2D ledger to a self-healing 3D network, setting a new benchmark for decentralized cash systems.

Developer 👨‍💻

Developed by Blackbeard | Ten Titanics | GitHub

© 2023-2025 Blackbeard. All rights reserved.