DEV Community: Arkendu Kundu

I built friendstudy ai — an open-source ai study companion for students

Arkendu Kundu — Sun, 04 Oct 2026 17:02:49 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built FriendStudy AI, an AI-powered study companion designed to help students study more effectively.

It provides students with an interactive study workspace where they can:

ask AI questions about any topic
generate study plans
practice with AI-generated quizzes
track study progress
maintain study streaks
organize their daily study goals

The idea came from a simple problem: students often have to switch between multiple tools for planning, learning, practicing, and tracking their progress.

FriendStudy AI brings these features together in one place.

Who I Built It For

I built FriendStudy AI for students who want a simple study companion that can help them understand topics, practice questions, and stay consistent with their studies.

The goal was to create something that feels less like a generic chatbot and more like a study companion that students can use as part of their daily routine.

Demo

Live Demo:

https://friendstudy-ai-hf26.vercel.app/

The AI features are powered by Qwen 2.5 3B, running through Ollama.

How It Works

The application uses:

React + JavaScript for the frontend
FastAPI + Python for the backend
Qwen 2.5 3B as the AI model
Ollama to run the open-weight model
Render for backend deployment
Vercel for frontend deployment
ngrok to connect the deployed backend with the locally running AI model

The production architecture looks like this:

React → Vercel → FastAPI → Render → ngrok → Ollama → Qwen 2.5 3B

The frontend communicates with the FastAPI backend, while the backend sends AI requests to the locally hosted Qwen model through Ollama.

Features

AI Study Companion

Students can ask questions and get AI-generated explanations using Qwen 2.5 3B.

Study Planning

FriendStudy AI can help students organize their study sessions and create structured study plans.

AI Quiz

Students can practice topics using AI-generated quiz questions.

Progress Tracking

The dashboard provides an overview of study progress, completed topics, daily goals, and study streaks.

Daily Goals

Students can set study goals and keep track of their daily progress.

What I Learned

This project helped me learn a lot about taking an AI application from local development to a deployed application.

Some of the biggest things I learned were:

connecting a React frontend with a FastAPI backend
deploying a FastAPI application on Render
deploying a Vite frontend on Vercel
working with Ollama and open-weight LLMs
connecting a deployed backend to a locally running AI model
exposing a local AI service for development using ngrok
debugging CORS and API connection issues
working with environment variables
troubleshooting deployment and build issues
connecting different services together into one working application

Challenges

The biggest challenge was getting the local Qwen model to work with the deployed application.

Initially, the frontend and backend worked separately, but the deployed backend could not directly access Ollama running on my computer.

I eventually built this architecture:

React → Vercel → FastAPI → Render → ngrok → Ollama → Qwen 2.5 3B

Getting every part of this pipeline working together required quite a bit of debugging.

I also had to troubleshoot frontend build issues, CORS configuration, environment variables, API connectivity, and the connection between the deployed backend and the local AI model.

Seeing the deployed application successfully send a request through the entire pipeline and receive a response from Qwen made the debugging worth it.

Code

The complete project is available on GitHub:

https://github.com/arkendukundu-dev/friendstudy-ai-hf26

Feel free to explore the code and try the application.

What's Next

I would like to continue improving FriendStudy AI by adding:

better personalized study plans
improved quiz generation
persistent student profiles
better progress analytics
faster AI responses
more AI-powered study tools
improved personalization based on student progress

Final Thoughts

Building FriendStudy AI was a great experience because I wasn't just building another AI chatbot.

I wanted to build something that could actually become a useful study companion for students.

This project also gave me hands-on experience with AI, React, FastAPI, deployment, open-weight models, Ollama, and connecting local AI infrastructure with a deployed web application.

Thanks for checking out FriendStudy AI!

If you try it, I'd love to hear your feedback.

I built friendstudy ai — an open-source ai study companion for students

Arkendu Kundu — Sun, 04 Oct 2026 11:11:21 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built FriendStudy AI, an AI-powered study companion designed to help students study more effectively.

It provides students with an interactive study workspace where they can:

ask AI questions about any topic
generate study plans
practice with AI-generated quizzes
track study progress
maintain study streaks
organize their daily study goals

The idea came from a simple problem: students often have to switch between multiple tools for planning, learning, practicing, and tracking their progress.

FriendStudy AI brings these features together in one place.

Who I Built It For

I built FriendStudy AI for students who want a simple study companion that can help them understand topics, practice questions, and stay consistent with their studies.

The goal was to create something that feels less like a generic chatbot and more like a study companion that students can use as part of their daily routine.

Demo

Live Demo:

https://friendstudy-ai-hf26.vercel.app/

The AI features are powered by Qwen 2.5 3B, running through Ollama.

How It Works

The application uses:

React + JavaScript for the frontend
FastAPI + Python for the backend
Qwen 2.5 3B as the AI model
Ollama to run the open-weight model
Render for backend deployment
Vercel for frontend deployment
ngrok to connect the deployed backend with the locally running AI model

The production architecture looks like this:

React → Vercel → FastAPI → Render → ngrok → Ollama → Qwen 2.5 3B

The frontend communicates with the FastAPI backend, while the backend sends AI requests to the locally hosted Qwen model through Ollama.

Features

AI Study Companion

Students can ask questions and get AI-generated explanations using Qwen 2.5 3B.

Study Planning

FriendStudy AI can help students organize their study sessions and create structured study plans.

AI Quiz

Students can practice topics using AI-generated quiz questions.

Progress Tracking

The dashboard provides an overview of study progress, completed topics, daily goals, and study streaks.

Daily Goals

Students can set study goals and keep track of their daily progress.

What I Learned

This project helped me learn a lot about taking an AI application from local development to a deployed application.

Some of the biggest things I learned were:

connecting a React frontend with a FastAPI backend
deploying a FastAPI application on Render
deploying a Vite frontend on Vercel
working with Ollama and open-weight LLMs
connecting a deployed backend to a locally running AI model
exposing a local AI service for development using ngrok
debugging CORS and API connection issues
working with environment variables
troubleshooting deployment and build issues
connecting different services together into one working application

Challenges

The biggest challenge was getting the local Qwen model to work with the deployed application.

Initially, the frontend and backend worked separately, but the deployed backend could not directly access Ollama running on my computer.

I eventually built this architecture:

React → Vercel → FastAPI → Render → ngrok → Ollama → Qwen 2.5 3B

Getting every part of this pipeline working together required quite a bit of debugging.

I also had to troubleshoot frontend build issues, CORS configuration, environment variables, API connectivity, and the connection between the deployed backend and the local AI model.

Seeing the deployed application successfully send a request through the entire pipeline and receive a response from Qwen made the debugging worth it.

Code

The complete project is available on GitHub:

https://github.com/arkendukundu-dev/friendstudy-ai-hf26

Feel free to explore the code and try the application.

What's Next

I would like to continue improving FriendStudy AI by adding:

better personalized study plans
improved quiz generation
persistent student profiles
better progress analytics
faster AI responses
more AI-powered study tools
improved personalization based on student progress

Final Thoughts

Building FriendStudy AI was a great experience because I wasn't just building another AI chatbot.

I wanted to build something that could actually become a useful study companion for students.

This project also gave me hands-on experience with AI, React, FastAPI, deployment, open-weight models, Ollama, and connecting local AI infrastructure with a deployed web application.

Thanks for checking out FriendStudy AI!

If you try it, I'd love to hear your feedback.