This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Lazy Student AI is a 100% offline, privacy-first desktop application designed to do the heavy lifting of studying for you. You simply drop a boring, hour-long lecture video (MP4), audio file (MP3/WAV), or a lengthy PDF document into the app, and it automatically extracts the text, analyzes it, and generates bite-sized bullet-point summaries and interactive flashcards.

It saves countless hours of re-watching or re-reading materials. Who gets to be a little lazier? Every student who dreads going through massive lecture transcripts. Just upload the file, grab a coffee, and come back to ready-made study notes!

Demo

Here is the look of our modern landing page where users can download the setup file:

And here is the magic in action—a complete summary and interactive flashcards generated from a lecture:

Code

How I Built It

I wanted a seamless, industry-standard architecture that runs entirely on a local machine. Here is the tech stack and how the open-source AI fits in:

The Brain (Open-Source AI): I used Google's Gemma 2B model via Ollama (ollama run gemma:2b). It is incredibly fast, lightweight, and perfect for running locally on a laptop to generate accurate summaries and flashcard Q&A pairs. For audio and video processing, I integrated the local OpenAI Whisper CLI along with FFmpeg to extract and transcribe speech to text.

The Backend: A robust Spring Boot 3 application using Java 17 and SQLite for local data storage. It uses Apache PDFBox to parse PDFs and directly executes local shell commands to trigger Whisper and Ollama.

The Frontend: A sleek, modern UI built with React, Vite, Tailwind CSS v4, and Framer Motion for smooth, sloth-approved animations.

The Packaging: Instead of forcing users to run npm or mvn commands, I bundled the frontend into the Spring Boot static resources, compiled it into a .jar, and wrapped it into a standalone .exe installer using Launch4j and Inno Setup.

Why Does Open Innovation Matter?

For a student tool, open innovation is everything.

Zero Cost: Students are usually broke. Relying on closed, paid APIs (like OpenAI's GPT-4) would mean charging a subscription fee. By utilizing open-weight models like Gemma, the tool costs $0 to run forever.

100% Offline & Private: University lectures and personal notes shouldn't be sent to a corporate server. Running inference locally means the app works even in a library with terrible Wi-Fi, and user data never leaves their laptop.

Flexibility: Building on open tools allowed me to seamlessly pipe FFmpeg outputs into Whisper, and pipe that text directly into Gemma, entirely orchestrated by my own Spring Boot backend.

The Lazy Payoff

his app is the ultimate cheat code for laziness. Instead of spending 2 hours watching an "Object-Oriented Programming" lecture and pausing every 5 minutes to take notes, I just feed the .mp4 file to the app. In a few minutes, I get exactly what I need to know in 10 bullet points and flip-cards to memorize definitions. I literally used it this week to summarize a massive PDF assignment guideline while I watched an episode of Supernatural.

Prize Categories

Best Use of Gemma - The core intelligence of this project relies entirely on Google's open-weight Gemma model running locally via Ollama to perform the complex NLP task of summarizing and structuring flashcards from massive walls of transcribed text.