This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
🦥 StudySloth AI is a local AI-powered study assistant that converts study notes into summaries, key points, definitions, flashcards, and quizzes.
It helps students spend less time preparing revision materials and more time learning.
Demo
StudySloth AI runs locally using Streamlit and Ollama.
Add screenshots or a demo video here.
Code
GitHub repository: https://github.com/Kshitij342/StudySloth-AI
How I Built It
I used Python, Streamlit, Ollama, and the Llama 3.2 1B open-weight model. The app supports TXT, PDF, and DOCX study notes and generates revision materials through Ollama's local API.
Why Does Open Innovation Matter?
Local inference provides greater control over user data and avoids requiring a paid cloud AI API. After the dependencies and model have been downloaded, the application can run offline when its local components are available.
Local models can still make mistakes, and performance depends on the computer's hardware.
My Agent Session
I used Google Antigravity to help develop and test the application. I have not included a saved DevRelay session.
The Lazy Payoff
StudySloth reduces repetitive work when preparing revision materials. I have not measured a specific time saving.
Prize Categories
No partner-specific category selected yet.
Built for Hacktoberfest 2026 Week 3: Slow and Steady. 🦥
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