Inspiration Developers often struggle to keep up with trending repositories on GitHub that align with their specific interests, goals, and skill levels. Existing platforms show what's popular but rarely offer meaningful personalization, insights, or guidance. We wanted to solve that by building a smart, personalized, and AI-powered GitHub discovery platform tailored to each developer.
What it does SmartRepos is a modern web platform that helps developers discover trending GitHub repositories with personalized recommendations and AI-generated insights. It enables users to:
Browse daily, weekly, and monthly trending repositories
Receive recommendations based on their programming background and goals
Explore detailed views of repositories, including enhanced AI descriptions, technical breakdowns, and step-by-step learning guides
Chat with an AI assistant trained on repository context
Bookmark repositories for future reference
The platform offers a smooth onboarding quiz, personalized scoring for relevance, repository-level chat, and in-depth AI-powered analysis through Repomix and GPT-4o Mini integration.
How we built it The application is built with the Modelence framework, using a modular architecture and a clean separation of frontend and backend logic.
Frontend:
React 18 with TypeScript for structured component development
Tailwind CSS for responsive, utility-first styling
Framer Motion for smooth animations
React Query for data fetching and caching
React Router for client-side navigation
Marked and Tailwind Typography for rendering formatted README content
Backend:
Modelence for scalable, modular backend design
Node.js runtime with MongoDB for data persistence
GitHub API and Cheerio for trending data scraping
GPT-4o Mini for AI-powered descriptions and interactive chat
Repomix for full-context repository analysis
Custom modules for user preferences, recommendation scoring, repository data, and AI caching
Challenges we ran into Ensuring GitHub trending scraping was reliable across different timeframes and formats
Designing a personalized recommendation algorithm that feels meaningful and accurate
Caching AI outputs efficiently while balancing real-time updates and API cost
Structuring AI prompts to yield actionable, readable outputs across diverse repositories
Maintaining a clean and responsive UI while handling multiple data states and views
Accomplishments that we're proud of Fully integrated AI repository analysis that delivers contextual and educational insights
A recommendation system that adapts to user preferences across multiple dimensions
Seamless onboarding experience that directly influences personalized suggestions
Repository-specific chat that feels grounded in real technical details
A polished, animated, and fully responsive UI with thoughtful UX flows
What we learned How to orchestrate multiple AI services in a real-world application using intelligent caching, prompt engineering, and UI integration
Best practices for modular backend architecture using the Modelence framework
Techniques for scraping, parsing, and enriching GitHub data in a maintainable way
Designing recommendation algorithms that balance interpretability and usefulness
Building user onboarding systems that meaningfully influence app functionality
What's next for SmartRepos Add collaborative features such as shared collections or team recommendations
Integrate more LLM providers and experiment with on-device models for chat
Build a leaderboard or reputation system for community-rated repositories
Incorporate issue tracking and pull request summaries into the AI insights
Launch a public API for external tools to access personalized GitHub discovery
Log in or sign up for Devpost to join the conversation.