Tech Interview Wiki

A technical interview preparation platform leveraging AI to generate interconnected technical articles. The system uses a two-phase content generation approach with OpenAI and Anthropic for optimal content quality.

Technology Stack

Frontend

• React with Vite
• GraphQL (Apollo Client)
• Tailwind CSS
• react-markdown & react-syntax-highlighter
• Client-side caching and state management

Backend

• Flask with blueprints architecture
• PostgreSQL (SQLAlchemy ORM)
• Redis for caching
• Strawberry for GraphQL
• OpenAI & Anthropic APIs

Architecture

Database Design

• Self-referencing Article model for content relationships
• Efficient indexing for taxonomy/category queries
• Word count and relevance score tracking
• Relationship mapping for article connections

Content Generation Pipeline

1. Research Phase (OpenAI)

   • Low temperature for factual accuracy
   • Structured research document generation
   • Taxonomy-aware content organization

2. Content Generation (Anthropic)

   • Higher creative freedom for engaging content
   • Markdown formatting with code examples
   • Related article suggestions
   • Auto-excerpt generation

GraphQL Implementation

• Strawberry-based schema definition
• Efficient nested queries for related articles
• Automatic type generation
• Query optimization for N+1 problems

Core Features

Article Discovery

• Homepage displays articles by relevance score
• Taxonomy/category/tag-based navigation
• Dynamic content generation on first access
• Progress tracking for generation status

Content Display

• Markdown rendering with syntax highlighting
• Code block copy functionality
• Related articles suggestions
• Loading state management

Dynamic Generation

• Background content generation
• Progress feedback
• Caching of research results
• Error recovery mechanisms

CLI Commands

# Populate database with initial articles
flask populate-db [--force]

# Update article word counts
flask update-word-counts

# Recalculate article relevance scores
flask update-relevance-scores

Getting Started

Prerequisites

• Python 3.11+
• Node.js 18+
• Docker & Docker Compose
• OpenAI & Anthropic API keys

Development Setup

# Clone repository
git clone https://github.com/yourusername/tech-interview-wiki.git
cd tech-interview-wiki

# Backend setup
cp backend/.env.example backend/.env
# Edit .env with your API keys

# Start services
docker-compose up --build

# Run migrations
docker-compose exec backend flask db upgrade

# Populate initial content
docker-compose exec backend flask populate-db

Frontend Development

cd frontend
npm install
npm run dev

Environment variables:

VITE_API_URL=http://localhost:5173

Future Improvements

Backend

• API failure recovery mechanisms
• Rate limiting implementation
• Response caching optimization
• Parallel content generation
• Enhanced error logging

Frontend

• Full-text search
• Advanced filtering
• Personalized learning paths
• Interactive interview simulations
• Client-side search indexing

Performance

• Infinite scrolling
• Image optimization
• Enhanced caching strategies
• Response compression

License

MIT License