# StudyBuddy AI

> Source: <https://dev.to/adityameshram-dev/studybuddy-ai-55ak>
> Published: 2026-10-03 07:30:04+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 **StudyBuddy AI** a student-focused AI study assistant that runs on a local model.

I'm a Computer Technology diploma student, and my study routine is messy. Notes in one place, a search engine in another tab, an AI chat tool somewhere else, and random resources scattered in between. I wanted a single place where I can ask a question, get an explanation, and keep going without jumping around.

I built it for myself and my friends and classmates who face similar study challenges. I wanted to create something practical that could make studying a little easier, instead of building another project just for my portfolio.

What it does:

It's not a huge platform. It's a practical tool made by a student, for students.

[Explore StudyBuddy AI Repository](https://github.com/adityameshram-dev/studybuddy-ai)

The repository includes setup instructions for running the project locally. I also documented the Windows setup, since that's the environment I use to develop and test the project.

I started with a simple idea: a clean web app with an AI assistant behind it. The stack is intentionally simple.

**Backend**

**Frontend**

**AI layer**

The flow is simple. The frontend sends a request to the FastAPI backend, the backend passes it to Ollama running the Gemma model on the same machine, and the answer comes back to the browser.

**Challenges along the way**

Building around local inference taught me things a hosted API hides from you:

For this project, open innovation made the whole idea possible.

Local models aren't automatically better than hosted APIs. Hosted services can be faster or offer stronger capabilities for certain tasks, while local inference depends on your own hardware.

For me, building StudyBuddy AI with an open-weight model was a chance to learn by doing, understand local inference, and build something without depending on a paid API. That's what made this approach meaningful for my project.