LearnSync – A Unified Knowledge Explorer

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This is a submission for the Algolia MCP Server Challenge

What I Built

LearnSync is a unified search engine for developers that aggregates and ranks learning content from three major platforms:

  • DEV.to articles
  • GitHub repositories
  • YouTube tutorials

Whether you’re diving into a new framework or exploring a deep-dive into Web3, LearnSync brings the best learning materials across sources into one clean, fast, searchable interface.

Demo

Live App: learnsyncsub.netlify.app

GitHub Repository: https://github.com/pulkitgovrani/LearnSync

Video Walkthrough:

How I Utilized the Algolia MCP Server

I leveraged the Algolia MCP Server to power the unified search layer across content types.

Content Ingestion Logic:

  • DEV.to: I used their public /articles endpoint, supporting filters like tag, username, page, and state to fetch fresh and relevant articles.

Example structure: 

  const url = 'https://dev.to/api/articles?tag=react&page=1&per_page=20&state=fresh';
  • GitHub: I utilized the https://api.github.com/search/repositories endpoint to fetch trending repositories around a topic, sorted by stars and updated date. 
  const url = `https://api.github.com/search/repositories?q=react&sort=stars&order=desc&page=1`;
  • YouTube: I pulled topic-based tutorials from the youtube/v3/search endpoint using the YouTube Data API, filtering for recent and relevant videos. 
  const url = `https://www.googleapis.com/youtube/v3/search?part=snippet&q=react tutorial&type=video`;

All fetched content was normalized to a common schema and indexed into a single Algolia index (learnsync). The frontend uses this index to perform instant searches with filtering and relevance ranking powered by Algolia.

Key Takeaways

  • Normalizing article, repo, and video data into a unified format was challenging but crucial.
  • Algolia made building a lightning-fast search UI incredibly easy and scalable.
  • Writing a backend that ingests, formats, and syncs data from three APIs taught me a lot about rate limiting, data structure design, and batching.
  • The project evolved from a basic search bar to a continuously updated, API-powered learning tool.

Team

Built solo by @pulkitgovrani

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