This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
AHGV Learning — Your Personal AI Lecture Tutor 🎓
Students often spend hours watching online lectures but still struggle to understand difficult concepts, find specific explanations, and revise what they have learned.
AHGV Learning is an AI-powered interactive learning assistant that helps students learn from YouTube lectures. It brings lecture-based question answering and revision tools into a single learning workspace.
With AHGV Learning, users can:
- 🎥 Load YouTube lectures with available captions.
- 💬 Ask questions about lecture content and receive AI-generated explanations.
- ⏱️ Use transcript timestamps to connect answers with lecture material.
- 🧠 Generate multiple-choice quizzes for revision.
- 📝 Extract key points from lectures.
- 🔐 Create an account and sign in using Supabase authentication.
The project is designed for college students, self-learners, tutors, and educators. Potential customers include individual learners, coaching centres, and educational content creators who need more interactive learning tools.
The goal is simple: help students understand what they watch, not just finish watching it.
Demo
🚀 Live application: https://ahgv-learning.vercel.app
The frontend is deployed on Vercel, and the Python backend is deployed on Render.
Note: YouTube caption retrieval depends on caption availability and access restrictions. Some videos may not load successfully in the hosted environment.
Code
💻 GitHub repository: https://github.com/Shivam-Sharma2009/AHGV_PROJ
The project is built with a separate frontend and backend, making it easier to maintain and extend its learning features.
How I Built It
I built AHGV Learning using a modern web stack with AI-powered transcript processing.
Technology stack:
- Frontend: React, Vite, JavaScript, CSS
- Backend: Python, FastAPI, Uvicorn
- AI integration: Google Gemini API
- Authentication: Supabase Auth
- Frontend hosting: Vercel
- Backend hosting: Render
The application retrieves available YouTube captions and uses transcript context to support question answering. The backend includes timestamp-aware transcript retrieval and uses Gemini to generate explanations, quizzes, and key points.
The frontend communicates with the FastAPI backend through HTTP API requests, while Supabase manages authentication.
One of the main engineering challenges is making transcript retrieval reliable in a hosted environment, where external services may restrict requests from cloud-provider IP addresses.
Why Does Open Innovation Matter?
Open innovation allows developers to learn from existing technologies, share improvements, and build useful applications without reinventing every component.
For an educational project like AHGV Learning, open development can encourage contributions to transcript processing, accessibility, multilingual learning, documentation, and the overall user experience.
It also creates opportunities to experiment with alternative AI models and inference approaches as the project evolves.
My vision is to make lecture-based learning more interactive, accessible, and adaptable to different learning needs.
Project: AHGV Learning
Live demo: https://ahgv-learning.vercel.app
Source code: https://github.com/Shivam-Sharma2009/AHGV_PROJ
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