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Application using LLM models to explain pattern matching algorithms and their performance metrics. This project integrates the RAG (Retrieval-Augmented Generation) technique and vector embeddings to improve the model's reponses.

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unixisking/algoteach-ai

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AlgoTeach AI

AlgoTeach AI is an interactive educational platform that leverages AI to explain algorithmic concepts, complexity analysis, and pattern-matching techniques. It is built using Next.js and integrates LLM models to provide in-depth explanations and visualizations.

Features

  • AI-Powered Explanations: Uses LLM models to break down complex algorithmic concepts.
  • Pattern Matching Analysis: Demonstrates how different algorithms perform on various inputs.
  • Performance Metrics Visualization: Provides insights into time and space complexity.
  • Interactive Playground: Users can test algorithms and analyze their performance in real time.

Installation

To run the project locally, follow these steps:

Prerequisites

  • Node.js (>= 18)
  • npm or yarn

Steps

# Clone the repository
git clone https://github.com/unixisking/algoteach-ai.git
cd algoteach-ai

# Install dependencies
npm install # or yarn install

# Start the development server
npm run dev # or yarn dev

The application will be available at http://localhost:3000.

Technologies Used

  • Next.js – React framework for server-side rendering and static site generation.
  • TypeScript – Ensures type safety.
  • LLM Integration – AI-powered explanations.
  • ShadCN/UI – Component styling.
  • Tailwind CSS – Utility-first styling framework.

Contributing

Contributions are welcome! If you’d like to improve AlgoTeach AI, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries, reach out via GitHub issues or email [[email protected]].

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Application using LLM models to explain pattern matching algorithms and their performance metrics. This project integrates the RAG (Retrieval-Augmented Generation) technique and vector embeddings to improve the model's reponses.

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