This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Meet LazyNotes - a local-first AI study assistant that helps students turn lengthy study materials into concise, revision-ready notes.
Students often spend hours reading lengthy PDFs, identifying important concepts, writing summaries, and preparing revision questions. LazyNotes aims to simplify that workflow by letting students upload their study materials and generate structured learning resources using AI running locally on their own machine.
With LazyNotes, students can work toward generating:
- Smart summaries - understand lengthy study materials faster.
- Key concepts - identify the most important topics to revise.
- Definitions - collect important terms in one place.
- Revision questions - turn study material into active-recall practice.
- Downloadable notes - keep generated study resources for later revision.
The goal is simple: spend less time organizing notes and more time actually learning.
Demo
Live demo: Running locally on my machine; a public deployment or video demo is not available yet.
The project uses a local backend and an Ollama-hosted model, so the complete experience is intended to run on the user's computer.
Code
GitHub Repository: MrHardik/LazyNotes
Explore the source code, follow the development, and contribute ideas or improvements. Contributions that improve the interface, document processing, AI-generated notes, or documentation are welcome.
How I Built It
LazyNotes is built around the idea that useful AI study tools shouldn't require every document to be sent to a third-party cloud API.
Tech stack
- React + Vite - frontend and user interface.
- Python + FastAPI - backend API.
- Ollama - runs the AI model locally.
- Qwen 2.5 3B - the intended lightweight open-weight language model.
- PyMuPDF - extracts text from PDF documents.
- Requests - communicates with Ollama's local API.
How it works
- A student uploads a PDF or text file.
- The FastAPI backend extracts the document's text.
- The backend sends the relevant text to the locally running model through Ollama.
- The model generates structured study material.
- The frontend presents the results for review and revision.
The application is designed to avoid relying on paid, cloud-hosted LLM APIs for inference. This makes local experimentation more accessible and gives users greater control over their study documents.
Why Does Open Innovation Matter?
Open innovation made the central idea behind LazyNotes possible: building an AI-powered learning tool without depending on a proprietary inference API.
Open-weight models and tools such as Ollama let developers experiment with AI on personal hardware. Frameworks such as React and FastAPI make it practical to connect that local intelligence to a usable application.
This matters especially for students. Not everyone can afford recurring API costs, and study materials may contain personal notes, assignments, or other documents users would prefer to keep on their own devices.
A local-first approach can offer three important advantages:
- Accessibility: no per-request cloud inference bill.
- Privacy and control: documents can be processed locally instead of being sent to an external AI provider, provided the application is configured to keep processing local.
- Experimentation: developers can inspect, modify, and improve the application and choose the model that suits their hardware.
Open innovation isn't just about making technology available. It's about giving more people the ability to build on it.
My Agent Session
I used AI-assisted development to help build and refine the project, while connecting the frontend, backend, document-processing workflow, and local model.
Agent session recording: not linked yet.
The Lazy Payoff
The biggest potential payoff is reducing the repetitive work between opening a textbook PDF and getting ready to revise.
Instead of manually extracting definitions, outlining chapters, and drafting practice questions, a student can use generated notes as a starting point and spend more time checking understanding, solving problems, and revising.
I still need to measure the time saved across real study sessions, so I won't claim a specific percentage or number of hours yet. The aim is to make the first step of revision faster without removing the student's role in understanding and verifying the material.
Prize Categories
Open-Source AI: LazyNotes is designed around an open-weight model running locally through Ollama.
I'll finalize the applicable partner categories according to the official challenge rules before submitting.
LazyNotes is built on a simple belief: AI should help students learn more effectively without making useful tools expensive or complicated to access.
Less time formatting notes. More time understanding concepts.
If you're interested in local AI, open-source development, or tools that make student life easier, check out the repository and share your feedback!
Top comments (0)