Study Buddy: a retro AI study companion built for a friend 🎓

A developer built Study Buddy, a retro-styled AI study companion that combines note upload, question answering, quiz generation, and study planning in a single interface. The application pairs a React and Vite frontend with a Java Spring Boot backend, using Hugging Face Inference Providers for AI assistance, Apache PDFBox for PDF text extraction, and browser local storage instead of a database. The developer kept the Hugging Face API token server-side so the frontend never exposes it, and cautions that generated answers and quizzes should still be checked against original notes.

Hacktoberfest Weekend Challenge: Build for a Friend Submission 🤝 This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend What I Built Study Buddy is a retro-style AI study companion designed to make studying a little less overwhelming. I wanted to build something useful for a friend who has to go through lengthy notes, prepare for exams, and figure out what to revise next. Instead of switching between different tools for reading notes, making quizzes, and planning study sessions, Study Buddy brings these tasks together in one place. You can upload study material, ask questions about your notes, generate practice quizzes, create a study plan, and keep track of your progress. The goal is to spend less time organizing study material and more time actually learning it. I built the interface with a nostalgic retro aesthetic because studying doesn't have to feel like staring at another boring productivity dashboard. I also wanted the project to be accessible and easy to try, without requiring users to create an account or set up a database just to get started. Demo Add your deployed Study Buddy URL here A short demo video will show the main workflow: uploading notes, asking a question, generating a quiz, and planning a study session. Code View Study Buddy on GitHub Replace the link above with your actual public repository before publishing. How I Built It Study Buddy combines a Java backend with a modern React frontend. Frontend: React and Vite, with a retro-inspired interface designed to make studying feel approachable. Backend: Java and Spring Boot, providing REST APIs for the study features. AI: Hugging Face Inference Providers, using an open-weight language model for AI-powered study assistance. Document processing: Apache PDFBox for extracting text from PDF study materials. Storage: Browser local storage for keeping supported study data and progress without requiring a traditional database. Deployment: Docker and Render for making the application accessible online. I kept the application modular so that the frontend handles the study experience while Spring Boot manages document processing and AI requests. One important design decision was to keep the Hugging Face API token on the backend rather than exposing it in the browser. The frontend communicates with Spring Boot, and the backend makes authenticated requests to the model provider. The AI features are intended to help learners understand and practise their material. Generated answers and quizzes should still be checked against the original notes, because language models can make mistakes. Why Does Open Innovation Matter? I wanted to build something beyond a basic chatbot. Open-weight models and accessible inference tools made it possible to experiment with AI-powered learning features without training a language model from scratch. Using a model through a configurable backend makes it easier to experiment with alternatives as model availability, performance, and project requirements change. Students should be able to experiment with AI features without needing a complicated setup or a paid subscription to multiple productivity tools. Study Buddy brings several study workflows together in one interface. By sharing the code, I hope other developers can explore the implementation, suggest improvements, add features, and adapt the project to their own learning workflows. For me, open innovation is not just about making code public. It is about making experimentation more accessible and giving people the freedom to understand, adapt, and improve the tools they use. What I Learned Building Study Buddy helped me connect several parts of full-stack development: React, Spring Boot, document processing, API integration, environment configuration, and deployment. It also reminded me that adding AI to an application is only one part of the challenge. The user experience, reliable API integration, clear limitations, and useful everyday workflows matter just as much. What's Next? I'd like to improve Study Buddy with more personalized revision recommendations, better tracking of weak topics, and additional ways to practise concepts from uploaded material. The long-term goal is simple: make it easier for students to turn their notes into a practical learning routine. Prize Categories Open-Source AI / Best Use of AI Study Buddy uses a language model through Hugging Face Inference Providers to support its study workflows. Rather than building a model from scratch, I focused on designing an application around AI: connecting it to study material, integrating it into a full-stack system, and making the results useful through a simple interface. This project reflects what I find exciting about open innovation: developers can build practical applications around existing AI models, experiment with different options, and share their work with the community. Thanks for checking out Study Buddy 🎓💻