Project Info
🧠 Learnr – Learn Smarter, Not Harder
Experience the future of education with AI-powered interactive learning.
Visualize concepts, take quizzes, and master any subject with personalized guidance.
🚀 Overview
Learnr is an intelligent education platform built using the MERN stack and enhanced by Python-powered AI video generation.
It helps learners master topics through multiple learning styles — interactive text, adaptive quizzes, and visual explanations — powered by cutting-edge AI tools like Groq and Manim.
Our mission is simple: make learning faster, deeper, and more intuitive.
✨ Features
🧩 Interactive Learning
Engage with dynamically generated explanations, examples, and guided exercises tailored to your knowledge level.
🧠 Smart Quizzes
Instantly test your understanding with AI-generated quizzes that adapt in real time based on your progress and performance.
🎥 Visual Concepts
Visualize abstract ideas with automatically generated Manim videos — rendered using Groq for ultra-fast computation.
🗣️ Multi-Style Learning
Choose your preferred learning mode — visual, theoretical, interactive, or quiz-based — and switch seamlessly at any time.
🏗️ Tech Stack
| Layer | Technology | Description |
|---|---|---|
| Frontend | React, Vite, TailwindCSS | Responsive and dynamic UI for lessons, videos, and quizzes |
| Backend | Node.js, Express | REST API, authentication, and quiz logic |
| Database | MongoDB Atlas | Stores user data, progress, and content metadata |
| AI / Video Engine | Python, Groq, Manim | Generates AI-driven educational videos |
| Integration | REST + WebSocket | Real-time session updates and data streaming |
🧮 System Architecture
Analysis
View
Metric
- 33
- 18
- 2
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- ExpressIn code
- FirebaseIn code
- HTMLIn code
- JavaScriptIn code
- MongoDBIn code
- PythonIn code
- ReactIn code
- TypeScriptIn code
- Node.jsClaimed
9 of 10 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
211 KB
Source files
53
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
DevoutSomething/CalHacks2025
66 files · 629 KB · @ f83ebec
Structure
Interface
30 files · 45%Screens, components and styles rendered to the user.
Application logic
14 files · 21%Domain rules, services and shared utilities.
Data & schema
1 file · 2%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript33%
- JavaScript27%
- CSS19%
- Markdown18%
- Python2%
- HTML0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
frontend/package.json
npm · 24- @excalidraw/excalidraw
- dotenv
- firebase
- katex
- react
- react-dom
- react-katex
- react-markdown
- react-router-dom
- rehype-katex
- remark-gfm
- remark-math
- +12 more
Backend/package.json
npm · 9- cors
- dotenv
- express
- expresss
- ffmpeg-static
- groq-sdk
- mongoose
- multer
- nodemon
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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