LLM Mood Tracker

Track and compare the daily performance of popular AI language models. Community-driven ratings for GPT-4, Claude, Gemini, and more.

Features

• 📊 Daily mood tracking for LLMs
• 🎯 Rate models on Performance, Speed, Intelligence, and Reliability
• 📈 Historical performance charts
• 🔒 GDPR-compliant, privacy-first design
• ⚡ Real-time updates
• 🚀 SEO optimized

Tech Stack

• Frontend: Next.js 14, TypeScript, Tailwind CSS
• Backend: Next.js API Routes
• Database: MySQL with partitioning
• Analytics: Plausible (GDPR-compliant)
• Charts: Recharts
• Rate Limiting: Fingerprint-based

Getting Started

Prerequisites

• Node.js 18+
• MySQL 8.0+
• npm or yarn

Installation

1. Clone the repository:

git clone https://github.com/FourTwentyDev/llmmoods.git
cd llmmood

2. Install dependencies:

npm install

3. Set up environment variables:

cp .env.local.example .env.local

Edit .env.local with your database credentials and Plausible domain.

4. Initialize the database:

npm run db:init
npm run db:seed

5. Run the development server:

npm run dev

Open http://localhost:3000 to see the app.

Database Setup

Create a MySQL database and user:

CREATE DATABASE llmmood;
CREATE USER 'llmmood'@'localhost' IDENTIFIED BY 'your_password';
GRANT ALL PRIVILEGES ON llmmood.* TO 'llmmood'@'localhost';
FLUSH PRIVILEGES;

Deployment

Vercel

1. Push to GitHub
2. Import to Vercel
3. Add environment variables
4. Deploy

Cron Jobs

Set up a daily cron job to sync models:

0 0 * * * curl https://llmmood.com/api/cron/sync-models -H "Authorization: Bearer YOUR_CRON_SECRET"

Contributing

Pull requests are welcome! Please read our contributing guidelines first.

License

MIT License - see LICENSE file for details.

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Built with ❤️ by FourTwenty Development