# Shadowgraph
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<img src="https://drscdn.500px.org/photo/1079998425/q%3D80_m%3D1000/v2?sig=cb2e22168e784ad8c85edeb5b11d86cee6181480a00f46884c97484b949475eb" width="66%"></center>
## Addressing the unique problem space of the Hyperdimensional Social Graph
### Exploring the Problem Space and its Emergent Properties
The traditional paradigms of social networking operate in a flattened space, constrained by simplified social structures and linear timelines. In contrast, Shadowgraph is pioneering a hyperdimensional social graph that unfolds in a much richer, multi-dimensional space.
This unique approach creates emergent properties and challenges not present in existing social networking models.
### Why Traditional Social Networking Falls Short
Conventional social networks offer a rather binary experience, where interactions are limited to 'likes,' 'follows,' or 'connections.' These one-dimensional metrics fail to capture the complexity of human relationships and interactions. Furthermore, they are ill-equipped to handle the nuanced trust metrics and adaptive personalisation that a hyperdimensional social graph demands.
### The MetaPersona AI Imperative
In this unique problem space, the role of MetaPersona AI becomes not just beneficial but crucial. MetaPersona serves as the 'local guide' for each user, navigating the complexities of the hyperdimensional social graph. Through its local vector database, MetaPersona AI enables a level of personalisation and security that is both unprecedented and necessary for the efficient functioning of Shadowgraph.
```mermaid
graph LR
style A fill:#555,stroke:#333,stroke-width:2px;
style B fill:#666,stroke:#333,stroke-width:2px;
style C fill:#777,stroke:#333,stroke-width:2px;
A[Hyperdimensional Social Graph] -->|Complex Interactions| B[MetaPersona AI]
B -->|Personalisation & Security| C[User Experience]
```
## The Technical Architecture Shaped by the Problem
Given the unique challenges posed by the hyperdimensional social graph, the technical architecture for Shadowgraph is designed to be equally unique and advanced. Each technological choice reflects a conscious decision to address specific challenges:
1. **Decentralised Identifiers (DID)**: Essential for enabling a unified yet flexible identity across the hyperdimensional space.
2. **Zero-Knowledge Machine Learning (ZKML)**: Ensures data privacy while allowing for sophisticated local computations.
3. **Evidence-Based Subjective Logic (EBSL)**: Provides a nuanced trust metric that adapts to the complex dynamics of the hyperdimensional graph.
```mermaid
graph TD
style D fill:#555,stroke:#333,stroke-width:2px;
style E fill:#666,stroke:#333,stroke-width:2px;
style F fill:#777,stroke:#333,stroke-width:2px;
D[Unique Challenges] -->|DID| E[Unified Identity]
D -->|ZKML| F[Data Soveregnty & Trustless Compute]
D -->|EBSL| G[Nuanced Trust Metrics]
```
By defining this new problem space, Shadowgraph is not just another social network but a fundamentally different experience. It is an exploration into what social interaction could become, enabled by technology that is as groundbreaking as the vision behind it. This unique challenge, and our unique solution to it, becomes a compelling narrative for stakeholders and users alike.
## Introduction and Background
Shadowgraph aims to break the constraints of traditional social networks by offering a multi-dimensional canvas for human interaction. Using advanced technologies, Shadowgraph seeks to create a secure, highly personalised, and deeply meaningful social experience.
---
## Objectives and Goals
1. **Deep Personalisation**: Continuously adapt to individual user preferences.
2. **Data Security and Sovereignty**: Empower users with full control over their data.
3. **Dynamic Trust Metrics**: Provide a nuanced, evolving trust metric based on EBSL.
---
## Key Features and In-Depth UI Wireframes
### Dimensional Login & Registration
```mermaid
graph TB
style A fill:#555,stroke:#333,stroke-width:2px;
style B fill:#666,stroke:#333,stroke-width:2px;
style C fill:#777,stroke:#333,stroke-width:2px;
A[Portal: Landing Page]-->|Cryptographic Authentication web3, etc|B[Secure Dimensional Login]
A-->|New User?|C[Dimensional Registration]
```
### Hyperdimensional Hub
```mermaid
graph LR
style D fill:#555,stroke:#333,stroke-width:2px;
style E fill:#666,stroke:#333,stroke-width:2px;
style F fill:#777,stroke:#333,stroke-width:2px;
style G fill:#888,stroke:#333,stroke-width:2px;
D[Hyperdimensional Hub]-->|Profile Management|E[Ethereal Profile]
D-->|Settings & Preferences|F[Settings Nebula]
D-->|Real-Time Notifications|G[Dimensional Alerts]
```
### Dimensional Feeds
```mermaid
graph TD
style H fill:#555,stroke:#333,stroke-width:2px;
style I fill:#666,stroke:#333,stroke-width:2px;
style J fill:#777,stroke:#333,stroke-width:2px;
style K fill:#888,stroke:#333,stroke-width:2px;
H[Dimensional Feeds]-->|Content Exploration|I[Dimension Explorer]
H-->|Create New Posts|J[Dimensional Post Creator]
H-->|Interactions|K[Interaction Sphere]
```
### MetaPersona AI
```mermaid
graph LR
style L fill:#555,stroke:#333,stroke-width:2px;
style M fill:#666,stroke:#333,stroke-width:2px;
style N fill:#777,stroke:#333,stroke-width:2px;
style O fill:#888,stroke:#333,stroke-width:2px;
L[MetaPersona AI]-->|Analytics & Insights|M[Personal Insight Dashboard]
L-->|Contextual Suggestions|N[Quantum Suggestions]
L-->|Trust & Security|O[Trust Spectrum]
```
---
## Technical Architecture and Stack
Shadowgraph will utilise a hybrid stack that incorporates both Web2 and Web3 paradigms. A microservices architecture will provide the backbone for scalability.
- **Frontend**: Svelte and SvelteKit for UI, Three.js for 3D interactions
- **Backend**: Node.js with a GraphQL API
- **Blockchain**: Agnostic approach, allowing for flexible integration with various blockchain technologies
- **Data Storage**: Decentralised storage solutions
- **Machine Learning**: Custom models for ZKML
---
## MetaPersona AI: The Intelligence Core
### Local Vector Database
The local vector database serves as the engine for MetaPersona AI's real-time personalisation. This high-dimensional space, stored locally on the user's device, comprises vectors representing aspects of user behaviour, preferences, and interactions.
#### Structure and Schema
```mermaid
graph TD
style A fill:#555,stroke:#333,stroke-width:2px;
style B fill:#666,stroke:#333,stroke-width:2px;
style C fill:#777,stroke:#333,stroke-width:2px;
A[Local Vector Database] -->|Content Context| B[Content Vectors]
A -->|Social Context| C[Social Vectors]
A -->|Temporal Context| D[Temporal Vectors]
```
#### Signal Ingestion and Processing
```mermaid
graph TD
style E fill:#555,stroke:#333,stroke-width:2px;
style F fill:#666,stroke:#333,stroke-width:2px;
style G fill:#777,stroke:#333,stroke-width:2px;
E[Event Data] -->|Meta CaptureAI| F[Local Processing]
F -->|Vector Update| G[Local Vector Database]
```
#### Privacy and Security
```mermaid
graph LR
style H fill:#555,stroke:#333,stroke-width:2px;
style I fill:#666,stroke:#333,stroke-width:2px;
style J fill:#777,stroke:#333,stroke-width:2px;
H[Decision Making] -->|Local Query| I[Local Vector Database]
I -->|Response| J[User Experience]
```
---
MetaPersona AI offers a deeply personalised, yet privacy-preserving experience. This approach, which keeps all sensitive data on the user's device, is integral to Shadowgraph's core philosophy of data sovereignty and user-centric design.
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