AHGV Local AI Tutor: A Privacy-Focused AI Study Assistant
AHGV Local AI Tutor: A Privacy-Focused AI Study Assistant 馃帗 This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle . What I Built AHGV Local AI Tutor is an AI-powered study assistant designed to help students learn from their own study materials instead of repeated
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 馃捀
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
AHGV Local AI Tutor is an AI-powered study assistant designed to help students learn from their own study materials instead of repeatedly searching through lengthy documents.
Students can provide PDF, Markdown, and text files, or paste their notes directly. The application helps turn those materials into useful learning resources.
The intended users are college students, independent learners, and tutors who need a practical way to organize study material and prepare for examinations.
The long-term goal is to make useful AI-assisted learning accessible without requiring students to maintain expensive hardware or purchase multiple study tools.
馃寪 Project: https://ahgv-learning.vercel.app
The companion project is available online. The AHGV Local AI Tutor repository contains the code for the study-assistant implementation.
馃捇 GitHub Repository: https://github.com/Shivam-Sharma2009/AHGV_PROJ
The project uses a React and Vite frontend with a Python FastAPI backend. Supporting technologies include Pydantic, HTTPX, PyPDF, and pytest for validation and testing.
I am building this as a practical learning project, with an emphasis on keeping the architecture lightweight and making the code understandable and extensible.
I built AHGV Local AI Tutor around a simple workflow:
The frontend uses React 19 and Vite. The backend uses FastAPI and Uvicorn, with Pydantic for data validation, HTTPX for HTTP communication, and PyPDF for PDF processing.
My goal is to support open-weight AI models while keeping the application practical on limited hardware. Because my laptop has limited storage, running a local model through Ollama has been a constraint. I am exploring lightweight deployment options rather than requiring every student to download a large model.
The next step is to make the open-weight inference path fully reproducible and document the model and deployment requirements clearly.
Open innovation makes AI development more accessible to students and independent developers who do not have access to expensive GPUs or large cloud budgets.
For a project like AHGV Local AI Tutor, open-weight models can provide greater control over model selection, deployment, and customization. They