Overview

Trivial Triumph is a modern take on the classic Jeopardy game format, featuring AI-powered question generation to create unique trivia experiences every time. Players compete by answering questions across multiple categories, with point values ranging from 100 to 500. Built with cutting-edge web technologies and enhanced by AI, this project demonstrates full-stack development with intelligent features.

Features

AI-Generated Questions: Uses Hugging Face's Qwen2.5-72B-Instruct model to create unique trivia questions dynamically
Multiplayer Support: Up to 8 players can compete simultaneously
Persistent Game State: Auto-saves game progress using localStorage
Modern UI: Beautiful animations and dark mode support with shadcn/ui components
Responsive Design: Works seamlessly across desktop, tablet, and mobile devices
Interactive Gameplay: Click question values to reveal clues, award or deduct points
Custom Categories: Select from predefined categories or create your own

Tech Stack

Built with Next.js 15 and React 19 for modern web performance
Styled with Tailwind CSS and shadcn/ui component library
AI integration via Hugging Face Inference API
Written in TypeScript for type safety and better developer experience
State management with React hooks and localStorage persistence

How It Works

Setup Phase: Players add their names and select 5 trivia categories
Question Generation: AI generates 5 questions per category with varying difficulty (100-500 points)
Gameplay: Players select questions from the board, reveal the clue, and answer
Scoring: Points are awarded for correct answers or deducted for incorrect ones
Winner: The player with the highest score at the end wins!

Deployment

Deployed on Netlify with serverless function support for AI API calls. Features environment variable configuration for secure API key management.

Learning Outcome

This project gave me hands-on experience with AI API integration, real-time state management, localStorage persistence, and building interactive multiplayer game logic. I learned how to optimize API calls to reduce token usage, implement sequential AI generation with progress tracking, and create an engaging user experience with modern UI/UX patterns. The project also taught me valuable lessons in error handling, async operations, and responsive game design.