What I Built
I built StudySprint, a lightweight AI-powered study planner for a college friend who struggles with deciding what to study first when there are multiple subjects, deadlines, and limited time.
Users can add their subjects, topics, deadlines, difficulty, and estimated study time. StudySprint then creates a prioritized study plan.
It also includes:
- ✅ Task completion tracking
- 📊 Progress tracking
- ⏱️ Focus timer
- 💾 Local persistence
- 🤖 AI-powered prioritization
- 🔄 Fallback prioritization when AI is unavailable The goal is simple: spend less time planning and more time studying.
Demo
🔗 Live Demo: https://study-sprint-26.lovable.app/
Code
🔗 GitHub: https://github.com/Megh-Shah-08/study-sprint-26
How I Built It
Built using ReactJS, TypeScript, and Tailwind CSS.
The AI layer is designed around an open-weight model through Ollama, which receives the student's tasks and available time and generates a prioritized plan.
I also added a rule-based fallback using deadlines, difficulty, and estimated time so the app remains functional without AI.
Why Does Open Innovation Matter?
Using an open-weight model means the AI doesn't have to depend entirely on a proprietary API. It can be run locally, customized, and adapted for different use cases.
For a personal tool containing study schedules and tasks, local AI also provides a path toward keeping that information on the user's own device.
My Agent Session
Built with the help of Lovable.dev.
Prize Categories
- Open Innovation
- AI / Open-Weight AI
Built for a friend. Made to solve a real problem. 🚀
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