DEV Community: Trupti Khot

LazyStudentAI: Study Smarter, Not Harder with AI

Trupti Khot — Fri, 23 Oct 2026 17:16:13 +0000

🦥 LazyStudentAI: Study Smarter, Not Harder with AI | Hacktoberfest 2026

Hey everyone! 👋

I'm excited to share LazyStudentAI, an AI-powered study assistant project I’m building to make learning and exam preparation easier for students.

As students, we often have lots of notes but limited time to revise everything. I wanted to explore how AI could help turn study material into organized learning resources.

That’s how LazyStudentAI came to life! 🚀

This project is part of my journey into open source and the Hacktoberfest 2026 — Week 3: Slow and Steady challenge.

💡 What I Built

LazyStudentAI helps students transform study material into useful revision resources.

The goal is simple: spend less time organizing notes and more time understanding concepts.

✨ Key Features

📝 Study Material Input: Work with study material through the project’s input workflow.
📚 AI-Powered Summaries: Create concise revision material from notes.
🎓 Viva Questions and Answers: Prepare for practical examinations and viva sessions.
🗂️ Flashcards: Review important concepts using flashcards.
✅ Revision Checklist: Organize revision into manageable tasks.

Note: The exact features available depend on the current implementation in the notebook.

📸 LazyStudentAI in Action

Here are screenshots of the project and its study resources.

📝 Study Material Input

✅ Revision Checklist

🎓 Viva Questions and Answers

🗂️ Interactive Flashcards

🛠️ How I Built It

I used Python and Google Colab as part of my development workflow.

The project is organized around a notebook, where the study-assistant workflow can be explored and improved.

My focus was on exploring how AI can help students create structured learning resources from their study material.

💻 Explore the Code

🔗 GitHub Repository:

https://github.com/tk1981215/LazyStudentAI

📓 Project Notebook:

https://github.com/tk1981215/LazyStudentAI/blob/main/LazyStudentAi.ipynb

You can explore the notebook, review the implementation, and suggest improvements.

🌍 Why Open Source?

Open source gives developers and students an opportunity to learn by building real projects, sharing ideas, and collaborating with others.

As a student learning AI and programming, I see this project as a chance to improve my skills while building something that could be useful to other learners.

I would love feedback, bug reports, and ideas for new features!

🔮 What's Next?

Some improvements I would like to explore include:

Better support for different study-material formats.
More customizable summaries.
Improved flashcards and revision planning.
A cleaner, more user-friendly interface.
Additional testing and documentation.

These are future goals, not claims that every feature is already implemented.

🦥 The Lazy Payoff

The idea behind LazyStudentAI isn't to avoid studying. It's to make studying more organized.

Instead of spending all your time preparing revision material, you can focus more on understanding, practicing, and remembering what you learn.

Study smarter, not harder! 💡

🤝 Join the Journey

If you're interested in AI, Python, education technology, or open source, check out the repository and share your thoughts.

⭐ Star the repository if you find the idea interesting.

🐛 Report issues or suggest improvements.

💡 Share ideas for features that could help students.

Thanks for reading, and happy building! 🚀

Tags: #hacktoberfest #ai #python #opensource

🛍️ DukaanAI: Building an AI Marketing Assistant for Local Shops with Open-Weight AI

Trupti Khot — Sat, 17 Oct 2026 17:51:10 +0000

What I Built

🛍️ DukaanAI — AI Marketing Assistant for Local Shops

Small shop owners often struggle to create attractive advertisements for their products. They may not have the time, marketing skills, or budget to hire someone to write promotional content.

DukaanAI is a simple AI-powered project that helps local shop owners generate product advertisements quickly.

For example, a shop owner can ask it to create an advertisement for a cotton kurti priced at ₹599 in Marathi.

The goal is to make AI-powered marketing more accessible to small Indian businesses.

Key Features

Generate product advertisements from a simple text prompt.
Create promotional content for different products and prices.
Support multilingual marketing prompts, including English, Hindi, and Marathi.
Help small businesses create content for WhatsApp and social media.
Keep the project simple and easy for beginner developers to understand and extend.

The initial version focuses on generating advertisements. More features can be added as the project develops.

Who would pay for it?

Local shop owners, home-based businesses, small retailers, and online sellers could benefit from a tool like DukaanAI. A future business model could offer free basic generations and paid bulk advertisement creation.

Demo

The project currently runs as a simple Python application.

Demo video: Watch Demo Video

Live demo: Not deployed yet.

Code

GitHub repository: [Add your GitHub repository link here]

The project uses Python and the Groq API to generate marketing content with an open-weight AI model.

How I Built It

I built DukaanAI using Python and the Groq API.

The basic workflow is:

The user enters a product and a request for an advertisement.
Python sends the prompt to the AI model through the API.
The model generates promotional content.
The application displays the generated advertisement in the terminal.

Technology Stack

Python: Application logic.
Groq API: Access to hosted model inference.
Open-weight model: openai/gpt-oss-20b.
Groq Python SDK: Connects Python to the model API.

I chose a simple implementation so that I could focus on solving a real problem rather than building unnecessary complexity.

Why Does Open Innovation Matter?

Open innovation makes powerful AI models more accessible to independent developers and small businesses.

For DukaanAI, using an open-weight model means the underlying model weights are available under their applicable license, allowing developers to explore customization and alternative inference arrangements.

Using hosted inference through Groq also makes it possible to experiment without downloading or running a model locally.

This approach has trade-offs: API access may have usage limits or costs, and product information sent to a hosted service is processed by that provider.

In the future, DukaanAI could support local inference or other compatible providers, giving users more flexibility and reducing dependence on a single service.

I believe open AI technology can help developers build affordable, practical tools for people who might otherwise have limited access to AI.

My First User

This project is still in its initial stage. I have not yet confirmed a real-world user or received payment.

My next goal is to let a local shop owner try DukaanAI, collect feedback, and improve the generated advertisements based on their needs.

Prize Categories

Potential category: Best Use of a qualifying open-weight AI model.

I will confirm the applicable partner category against the official challenge rules before entering it.

Thanks to the Hacktoberfest and DEV communities for encouraging developers to build useful projects with open AI technology!

🌿 AI Nature Explorer: Go Outside, Explore Nature, and Let AI Guide You

Trupti Khot — Sat, 10 Oct 2026 17:16:17 +0000

What I Built

I built AI Nature Explorer, a web application designed to encourage people to explore nature instead of spending all their time on screens.

Users can upload a nature photo and use AI to identify what appears in the image. The idea is to make outdoor exploration more interesting for students, beginners, and anyone curious about the natural world. After exploring, users can try a small outdoor mission and learn to notice more of the nature around them.

My goal is simple: Go outside. Explore nature. Let AI guide you.

Demo

🌿 Live Website: https://ai-nature-explorer.vercel.app

Note: The AI backend is still a work in progress, so image identification may not work consistently in the live demo.

Code

💻 GitHub Repository: https://github.com/tk1981215/ai-nature-explorer

How I Built It

I built the frontend using React and Vite, and the backend using Python and FastAPI. The project uses a Hugging Face image-classification model to explore how AI can recognize nature-related images.

I connected the frontend to the backend and deployed the frontend on Vercel. I also attempted to deploy the backend on Render, where I encountered memory limitations while loading the AI model.

Building this project taught me about frontend-backend integration, AI model inference, deployment, and debugging real-world issues.

Why Does Open Innovation Matter?

Open-source AI makes it possible for students and independent developers to experiment with machine-learning models, learn how they work, and build applications without creating every component from scratch.

Using an openly available model gave me an opportunity to explore image classification and understand how AI can be integrated into a practical application. It also gives developers more flexibility to experiment with different models and adapt their projects as they learn.

For me, open innovation means that learning and building with AI can be accessible to people who are just starting their journey.

I am still improving the project, especially the backend deployment, and I want to continue exploring how open-source AI can help people connect with the world outside their screens.

My Agent Session

I did not use DevRelay for this project, so I do not have an agent session to share.

Prize Categories

I am submitting for the overall Hacktoberfest Open-Source AI Challenge: Week 1 — Touch Grass.

AI Nature Explorer 🌱 — Explore the Outdoors, One Photo at a Time

Trupti Khot — Sat, 10 Oct 2026 16:32:49 +0000

What if technology could encourage us to spend more time outdoors instead of staying in front of a screen?

That idea inspired me to build AI Nature Explorer 🌱

💡 What is it?

AI Nature Explorer is a web application designed to help people explore nature. Users can upload a photo of a plant, flower, tree, or another part of nature, with the goal of identifying the image and receiving an outdoor mission.

🛠️ Tech Stack

React
Vite
Python
FastAPI
Hugging Face Transformers
Vercel
Render

🌍 What I learned

Building this project helped me practise frontend and backend integration, connect a React application to a Python API, deploy a frontend, and troubleshoot deployment and memory-limit issues.

The AI backend still needs deployment improvements, so the live identification feature is a work in progress.

🔗 Project Links

Live website: https://ai-nature-explorer.vercel.app
GitHub repository: https://github.com/tk1981215/ai-nature-explorer

This project is a reminder that technology doesn't always have to keep us indoors. It can inspire us to step outside, observe our surroundings, and reconnect with nature. 🌿

Built to help you Touch Grass!

MLH #BuildInPublic #WebDevelopment #AI #React #Python #OpenSource #StudentDeveloper

First Contributions: learn how to contribute to open source projects

Trupti Khot — Mon, 05 Oct 2026 16:49:12 +0000

I followed the hands-on tutorial in the Readme of first contributions and made my first pull request to the same repo.

firstcontributions / first-contributions

🚀✨ Help beginners to contribute to open source projects

Read this in other languages.

First Contributions

This project aims to simplify and guide the way beginners make their first contribution. If you are looking to make your first contribution, follow the steps below.

If you're not comfortable with command line, here are tutorials using GUI tools.

If you don't have git on your machine, install it.

Fork this repository

Fork this repository by clicking on the fork button on the top of this page. This will create a copy of this repository in your account.

Clone the repository

Now clone the forked repository to your machine. Go to your GitHub account, open the forked repository, click on the code button, then on SSH tab and then click the copy url to clipboard icon.

Open a terminal and run the following git command:

git clone "url you just copied"

where "url you just copied" (without the…

View on GitHub

I Built StudyBuddy AI to Help My Friend Study Smarter with Open-Source AI

Trupti Khot — Fri, 02 Oct 2026 18:55:45 +0000

📚 Why I Built StudyBuddy AI

College students often struggle with difficult technical topics and exam preparation.

A friend of mine faced this problem while studying for college exams. Sometimes a topic is difficult to understand from textbooks or lecture notes, and before an exam there is very little time to revise everything.

For the Hacktoberfest 2026 Build for a Friend challenge, I decided to build a small AI-powered study assistant to help with this problem.

Meet StudyBuddy AI 🤖📚

💡 What is StudyBuddy AI?

StudyBuddy AI is a web-based study assistant that helps college students:

📖 Understand difficult topics in simple language
🎯 Find important exam questions
✍️ Practice short-answer questions
📝 Practice MCQs
⚡ Quickly revise important points

The main idea is:

Understand → Practice → Revise

🎯 The Problem

Many college students face two common problems:

Difficult technical topics can be hard to understand quickly.
Before exams, students need a simple way to practice important questions and revise.

I wanted to create one simple application that could help with both.

🤖 How StudyBuddy AI Works

The basic flow is:

Student

↓

Enter Topic

↓

React Frontend

↓

FastAPI Backend

↓

Qwen2.5-0.5B-Instruct

↓

Study Material

For example, a student can enter:

DBMS

and StudyBuddy AI can provide study material for that topic.

✨ Features

📖 Explain Topic

The application provides a simple explanation of a college topic.

It focuses on:

Definition
Simple explanation
Real-world example
Key points

📝 Exam Preparation

The application also provides:

Important questions
Short-answer questions
MCQs
Quick revision points

This helps students move from understanding a topic to practicing and revising it.

🌍 Open-Source AI

Open-source AI is at the core of this project.

StudyBuddy AI uses:

Qwen/Qwen2.5-0.5B-Instruct

The model is integrated using the Hugging Face Transformers library and runs through the Python backend.

This project helped me understand how an openly available AI model can become part of a practical application.

🛠️ Tech Stack

Frontend

React
Vite
JavaScript
CSS

Backend

Python
FastAPI
Uvicorn
Pydantic

AI

Qwen2.5-0.5B-Instruct
Hugging Face Transformers
PyTorch

Tools

Git
GitHub

📸 Screenshots

🏠 StudyBuddy AI Home

📖 Topic Explanation

📝 Exam Preparation

💻 Source Code

The complete project is available on GitHub:

https://github.com/tk1981215/StudyBuddy-AI

The repository contains the React frontend, FastAPI backend, AI integration, and screenshots.

👥 Built for a Friend

The idea for this project came from a real student problem.

A friend was struggling with understanding college topics and preparing efficiently for exams.

That made me think:

What if there was a simple study companion that could explain a topic and help with revision?

That question became StudyBuddy AI.

🌱 What I Learned

Building this project helped me learn how to:

Build a React frontend
Create APIs using FastAPI
Connect a frontend with a Python backend
Integrate an AI model using Transformers
Work with an open-source AI model locally
Handle API requests and CORS
Use Git and GitHub for project management
Turn a real problem into a working application

🌍 Why Open Innovation Matters

Open-source AI gives developers and students an opportunity to experiment, learn, and build useful applications using openly available technology.

For me, working with an open-source model made it possible to take a simple idea and turn it into a working project.

It also gave me practical experience with how AI can be integrated into a real application.

🚀 Future Improvements

Some improvements I would like to add in the future are:

PDF and notes upload
Generate questions from uploaded notes
Interactive AI study chat
Personalized quizzes
Study progress tracking
Multiple language support
Voice-based learning

The current version focuses on the core idea of helping students understand and prepare.

🏆 Hacktoberfest 2026

I built StudyBuddy AI for the Hacktoberfest 2026 – Build for a Friend challenge.

The challenge inspired me to use open-source AI to solve a real problem for a friend.

The problem:

Difficulty understanding college topics and preparing for exams.

My solution:

StudyBuddy AI. 📚🤖

❤️ Final Thoughts

This project gave me the opportunity to combine:

React + FastAPI + Open-Source AI

More importantly, it reminded me that a useful project starts with a real problem.

I'm excited to keep improving StudyBuddy AI and continue learning through open-source development.

StudyBuddy AI — Study Smarter. Understand Better.

My First Step into Open Source

Trupti Khot — Thu, 01 Oct 2026 17:05:31 +0000

Today I started my open-source journey!

I'm a B.Tech CSE (AI/ML) student, currently learning DSA, software development, AI/ML, and backend development.

I've always wanted to contribute to real-world projects, but getting started with open source felt confusing at first.

Today I took the first step:

Created my DEV Community profile
Joined Hacktoberfest 2026
Introduced myself to the DEV Community
Started exploring open-source projects

My goal is to learn by contributing, understand how real projects are built, and become a better software engineer.

One small contribution at a time.

If you're also starting your open-source journey, let's learn together!