# Studymat > Source: > Published: 2026-10-03 08:32:42+00:00 *This is a submission for the [Hacktoberfest Weekend Challenge: Build for a Friend](https://dev.to/challenges/hacktoberfest-weekend-2026-10-01)* I built **StudyMate**, an AI study partner for a friend who is a real student. She has plenty of study material, but when she gets stuck on a topic she often doesn't know **what to do next**: re-read, watch a video, practice, or revise. Most AI tools work like this: **Ask → Get answer.** Then the student is on their own again. StudyMate works like this: **Ask → Understand → Practice → Revise → Improve** It looks at what the student asked and offers the right next actions. The options are **adaptive, not fixed.** The same app gives different next steps depending on what the student is doing. *StudyMate's chat interface. After each answer, it offers adaptive next actions based on what the student asked.* Personalized AI study partner with contextual learning actions. This version uses **Ollama Cloud** by default. The default configurable model is `gpt-oss:120b`. ``` cd backend python -m venv .venv .venv\\Scripts\\activate pip install -r requirements.txt copy .env.example .env ``` Open `backend/.env` and add your Ollama Cloud API key: ``` AI_PROVIDER=ollama_cloud OLLAMA_HOST=https://ollama.com OLLAMA_API_KEY=YOUR_KEY_HERE OLLAMA_MODEL=gpt-oss:120b ``` Then: ``` uvicorn app.main:app --reload --port 8000 ``` Check: ``` http://127.0.0.1:8000/api/health ``` In a second terminal: ``` cd frontend npm install npm run dev ``` Open the Vite URL shown in the terminal. Never commit `backend/.env`. It contains your private API key. The React frontend does… The repo has the React + Vite frontend, the FastAPI backend, and a `.env.example` with setup instructions. 📧 Contact: [chanveersinghdev@gmail.com](mailto:chanveersinghdev@gmail.com) ``` Student (question / doubt) │ ▼ React + Vite (UI) │ ▼ FastAPI backend │ ▼ Context / prompt engine │ ▼ Ollama Cloud ─► gpt-oss:120b │ ▼ Answer + adaptive actions ``` | Part | Tool | |---|---| | Frontend | React + Vite | | Backend | FastAPI | | AI | gpt-oss:120b (open-weight) via Ollama Cloud | | Storage | SQL database for user data | | Built with | Claude Code | StudyMate is designed around how people actually learn: `Discover → Understand → Practice → Revise → Exam prep` The prompt engine detects the question type and phase, then chooses which actions to show. The AI is an **open-weight model (gpt-oss:120b)** running through Ollama Cloud, not a closed proprietary API. The learning logic is separated from the provider and model: ``` StudyMate learning logic → AI provider → Model ``` The model is set in `.env`, so I can switch models or providers, or move to local inference later, without rewriting the product. That means less vendor lock-in and more control over the AI layer. The Ollama API key stays on the FastAPI backend (`.env`). The browser never sees it, and only `.env.example` is committed to GitHub. StudyMate **never calls AI-generated questions "PYQs."** They are labelled *Practice question*. Real previous-year questions will come from a verified database (year + exam + subject + chapter), so students can trust what they study. I designed the interface like a real product: dark theme, rounded chat and composer, contextual action buttons, hover animations, chat history, and a responsive layout. The biggest lesson was that a good study tool shouldn't stop at the answer. Knowing what to do next is what turns an answer into learning. StudyMate is the foundation for that, and I want to keep building it with my friend's feedback.