WhiteBox: Bridging AI and Human Intelligence

A revolutionary approach to creating AI systems with holistic intelligence and grounded understanding.

Features

Key Capabilities

WhiteBox combines advanced AI techniques with human-like reasoning to create a more holistic intelligence system.

Holistic Intelligence

Combines computational power with human-like understanding and reasoning capabilities.

Neo4j Integration

Leverages graph database technology to model complex relationships and knowledge structures.

Python-Powered

Built primarily with Python, enabling rapid development and integration with AI libraries.

Modular Architecture

Designed with separate frontend, server, and script components for flexibility and scalability.

Advanced Reasoning

Goes beyond pattern recognition to implement true reasoning and understanding capabilities.

Open Source

Fully open source, encouraging collaboration and community contributions.

Architecture

System Design

WhiteBox is built with a modular architecture that combines multiple components to create a robust AI system.

Frontend
User interface and interaction layer
  • HTML/CSS interface (38.6%)
  • User interaction components
  • Visualization of AI responses
  • Responsive design for multiple devices
Server
Backend processing and API endpoints
  • RESTful API endpoints
  • Request handling and processing
  • Integration with AI models
  • Data validation and security
Neo4j Database
Graph-based knowledge representation
  • Graph database for complex relationships
  • Knowledge storage and retrieval
  • Semantic network representation
  • Query optimization for AI operations
Scripts
Core AI processing and algorithms
  • Python-based AI algorithms (58.3%)
  • Data processing pipelines
  • Machine learning model integration
  • Custom reasoning modules
System Flow
How data and requests flow through the WhiteBox system
User Input
Frontend Processing
Server API
Neo4j Database
Python Scripts
AI Processing
Response Generation
User Output
Technical Details

Under the Hood

Explore the technical implementation details of the WhiteBox project.

Programming Languages
The languages used in the WhiteBox project

Python (58.3%)

Primary language used for AI algorithms, data processing, and backend logic.

HTML (38.6%)

Used for frontend interface and user interaction components.

Dockerfile (2.4%)

Used for containerization and deployment configuration.

Shell (0.7%)

Used for automation scripts and deployment processes.

Team

Contributors

Meet the talented individuals behind the WhiteBox project.

KS
Kaaustaub Shankar
Project Lead

Creator and main contributor to the WhiteBox project, focusing on the core architecture and vision.

RR
Raihan Rafeek
Core Developer

Key contributor to the WhiteBox project, working on implementation and technical aspects.

AG
Arya Garg
Developer

Contributor to the WhiteBox project, working on algorithms and implementation details.

Get Started

Start Using WhiteBox

Follow these steps to get started with WhiteBox and explore its capabilities.

Clone Repository
Get the source code from GitHub
git clone https://github.com/KaaustaaubShankar/WhiteBox.git
Install Dependencies
Set up the required components
cd WhiteBox
pip install -r requirements.txt
Run the Application
Start using WhiteBox
docker-compose up
# or
python server/app.py
Ready to Explore?
Dive deeper into WhiteBox and discover how it can revolutionize AI understanding.

WhiteBox is an ongoing project that aims to bridge the gap between AI's computational power and human understanding. Join the community and contribute to this exciting endeavor.