Where AI meets developers — a single platform delivering curated AI news with full automation.

In a world where AI developments happen at breakneck speed, staying informed shouldn't be a full-time job. AI Dictionary exists to bridge that gap by automatically curating, summarizing, and presenting the most relevant AI tools, news, and insights in one accessible platform. We believe developers and AI enthusiasts deserve a streamlined way to discover what matters without the noise.

AI Dictionary is an intelligent content platform that combines automated AI news aggregation with a clean, developer-friendly interface. The platform leverages LangGraph agents to generate summaries and titles, pulls trending AI tools from Product Hunt, and presents everything through a modern blog system with dynamic routing and syntax-highlighted code examples.

• Secure cookie-based authentication with JWT (powered by fastapi-users)
• Dark/light mode theming for comfortable reading
• Responsive design that works everywhere

• Automated content generation using LangGraph workflows
• Multi-LLM support (Google Gemini, OpenAI, Groq)
• Product Hunt API integration for trending AI tools
• Smart title and summary generation

• Dynamic routing for blog posts with unique slugs
• Rich content blocks: code snippets, explanations, examples, images
• Server-side syntax highlighting with Shiki
• Type-safe content rendering system

• FastAPI with modular architecture
• Supabase database with row-level security
• Repository pattern for clean data access
• Atomic transactions for data integrity

Frontend: Next.js 16 · TypeScript · TailwindCSS · Framer Motion
Backend: Python 3.12 · FastAPI · fastapi-users · LangGraph · LangChain
Database: PostgreSQL (Supabase) with RLS
AI/LLM: Google Gemini · OpenAI GPT-4 · Groq
Dev Tools: uv (package manager) · Ruff (backend only) · Pre-commit hooks · TypeScript strict mode

Note: Ruff linting and formatting is currently enabled for the backend/ folder only. LangGraph bot integration coming soon.

• Node.js 18+ or latest LTS
• Python 3.12.5
• pnpm (npm i -g pnpm)
• uv (Python package manager)
• Supabase CLI

cd frontend
pnpm install --frozen-lockfile
pnpm dev

The frontend will be available at http://localhost:3000.

Environment Setup: Create .env.local in the frontend/ directory with your configuration (never commit this file).

The backend uses uv for fast, reliable Python package management.

cd backend
uv sync
uv run uvicorn main:app --reload
# Or alternatively:
# uv run python main.py

The API will run at http://127.0.0.1:8000.

Note: uv sync automatically creates a virtual environment and installs all dependencies from pyproject.toml.

1. Install Supabase CLI following the official guide

2. Start local Supabase instance:

   supabase start

3. Apply migrations:

   supabase migration up

4. (Team Members Only) Link to production:

   supabase link

cd frontend
pnpm build
pnpm start

├── frontend/              # Next.js application
│   ├── app/              # Pages and routing
│   ├── components/       # React components
│   ├── lib/              # Utilities and API client
│   └── types/            # TypeScript type definitions
├── backend/              # FastAPI application
│   ├── api/              # API endpoints
│   ├── core/             # Configuration and utilities
│   ├── db/               # Database client and repositories
│   ├── model/            # Pydantic models
│   └── services/         # Business logic and integrations
├── langgraph_bot/        # AI agent workflows
│   ├── agents/           # Agent implementations
│   ├── nodes/            # Workflow nodes
│   ├── tools/            # LangChain tools
│   └── workflow/         # Graph definitions
└── supabase/             # Database configuration
    └── migrations/       # SQL migration files

Completed:

• ✅ Authentication system with secure cookie-based JWT
• ✅ Blog platform with dynamic routing and content rendering
• ✅ AI content generation pipeline with LangGraph
• ✅ Product Hunt API integration
• ✅ Database layer with Supabase and RLS
• ✅ Dark/light mode theming
• ✅ Code syntax highlighting
• ✅ Repository pattern for data access

In Progress:

• 🔄 Backend integration with frontend
• 🔄 Automated content pipeline deployment
• 🔄 User dashboard and personalization

See CHANGELOG.md for detailed version history.

We welcome contributions! Whether it's bug fixes, new features, or documentation improvements, your help makes AI Dictionary better for everyone.

1. Fork the repository
2. Create your feature branch
3. Follow the existing code style (we use Ruff for Python, ESLint for TypeScript)
4. Write clear commit messages
5. Submit a pull request

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