🦥 StudySloth AI — Your Open-Source AI Study Buddy

#devchallenge #hf26challenge

🦥 StudySloth AI: Slow Down, Learn Smarter with Open-Source AI

What if studying felt less overwhelming and revision became easier?

As students, we often have lots of notes but limited time to revise them. Reading long notes repeatedly can be tiring, and identifying weak topics is not always easy.

That inspired me to build StudySloth AI — an AI-powered study assistant designed to turn notes into useful revision material.

💡 What I Built

StudySloth AI helps students study smarter by transforming their notes into structured learning resources.

The goal is simple: spend less time organizing study material and more time understanding it.

✨ Key Features

📝 Quick Summary: Turn lengthy notes into concise summaries.
🗂️ Flashcards: Generate flashcards for active recall and revision.
🧠 Interactive Quiz: Test your understanding with AI-generated questions.
🎯 Weak Spot Finder: Identify topics that may need more attention based on quiz performance.
🚀 Sloth Study Sprint: Bring your revision activities together in one focused study workflow.
🔎 Source Check: Help students compare generated answers with their original notes instead of blindly trusting AI output.

🛠️ How I Built It

I designed StudySloth AI around an open-source and local-first AI approach.

Planned technology stack:

Frontend: React and Vite
Backend: Python and FastAPI
AI model: Qwen2.5
Local inference: Ollama
Version control: Git and GitHub

The intended workflow is straightforward:

A student enters their notes.
The frontend sends the notes to the backend.
The backend communicates with the local AI model.
The generated summary, flashcards, or quiz is returned to the interface.
Students can use their quiz results to decide what to revise next.

Running inference locally can also reduce dependence on paid cloud AI APIs, although it requires a compatible local setup and sufficient computing resources.

🌱 Why Open-Source AI Matters

Open-source tools make it easier for developers and students to experiment, learn, and build useful applications without depending entirely on proprietary services.

By exploring open-weight models and local inference, I wanted to understand how AI-powered applications work beyond simply calling an API.

This project also gave me an opportunity to explore the connection between a frontend, a backend API, and a language model.

📚 What I Learned

Working on StudySloth AI helped me explore several important development concepts:

Connecting a React frontend to a backend API.
Structuring backend endpoints with FastAPI.
Integrating a language model into an application.
Designing practical AI features around a real student problem.
Thinking about reliability, structured responses, and grounding answers in source notes.
Organizing a project for GitHub and open-source collaboration.

One important lesson is that an AI application should not just generate impressive-looking answers. It should also help users verify those answers and understand their limitations.

🔗 Project Links

GitHub Repository: https://github.com/priyarani8281/dev3

Live Demo: https://drive.google.com/file/d/1NATNmo3SaseCFF15iIfstqTA2cOnH-Rn/view?usp=drivesdk

🚀 What's Next?

I would like to improve StudySloth AI by adding better quiz evaluation, more reliable source checking, improved error handling, and a smoother study experience.

I also want to continue learning how open-weight AI models can be used to build practical tools for students.

StudySloth AI is my step toward building AI applications that are useful, accessible, and designed around real problems.

Slow down. Learn smarter. One study sprint at a time. 🦥🌿