What I Built
I built StudyBuddy, a local AI-powered study and interview practice partner for a college friend.
The idea is simple: instead of depending on a general-purpose AI assistant, StudyBuddy provides focused practice for common student needs:
🎓 Viva Practice — practice questions for college viva preparation
💼 Interview Practice — practice technical interview questions
📚 Explain a Topic — get simple explanations of difficult concepts
The goal was to create something practical that a student can use while preparing for exams, vivas, and interviews.
Demo
Live Demo:
https://studybuddy-na8cmqqrybaagqjpzpea8d.streamlit.app/
Note: The live Streamlit deployment provides the application interface, while the AI inference in the project is designed to run locally through Ollama.
Code
GitHub Repository:
https://github.com/akshat0042005/StudyBuddy
How I Built It
StudyBuddy is built using:
Python
Streamlit for the user interface
Ollama for local AI inference
Qwen2.5 1.5B Instruct, an open-weight model
Local HTTP communication between the Streamlit application and Ollama
The basic flow is:
User
↓
StudyBuddy UI
↓
Streamlit Application
↓
Ollama
↓
Qwen2.5 1.5B Instruct
↓
AI Response
The local setup means the application does not need to send study conversations to a third-party hosted AI service.
Why Does Open Innovation Matter?
Open innovation in AI gives developers the ability to experiment with models and tools without depending entirely on closed platforms.
For a project like StudyBuddy, open-weight models make it possible to:
Run AI locally
Experiment with different models
Understand how the AI application works
Build privacy-focused applications
Modify and extend the project for specific use cases
For students and independent developers, this makes AI experimentation more accessible and gives them more control over the technology they build with.
What I Learned
While building StudyBuddy, I learned more about:
Integrating an AI model into a Python application
Running an open-weight model locally with Ollama
Building interactive interfaces with Streamlit
Designing a small AI application around a real user problem
Handling errors and unavailable local AI services
Using an AI coding agent as part of the development workflow
DevRelay Agent Session
I also used DevRelay during development to review and improve the project.
Agent Session:
https://dev.to/agent_sessions/studybuddy-windows-and-ollama-review-for-build-for-a-friend-yhqv9f
Built For a Friend ❤️
I wanted this project to solve a problem that is actually relevant to students — having a simple practice partner available whenever they want to prepare for a viva, interview, or difficult topic.
That's what motivated me to build StudyBuddy for this challenge.
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