📚 GetMyEBook-Web: Making Digital Reading More Accessible

vasanth k — Tue, 20 Oct 2026 09:07:37 +0000

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

🚀 What I Built

GetMyEBook-Web is a web-based eBook platform designed to make digital reading more accessible and convenient.

For this project, I am expanding the platform with a new AI-powered feature called Chat with Book AI, which uses Retrieval-Augmented Generation (RAG) to help readers interact with book content conversationally.

Instead of manually searching through hundreds of pages, readers will be able to ask questions about a book and receive answers grounded in the relevant passages retrieved from its content.

✨ Key Features

📚 Digital Library: Browse and discover eBooks in one place.
🔎 Book Discovery: Find books and explore available reading resources.
🤖 Chat with Book AI: Ask questions about a book using natural language.
🧠 RAG-Based Retrieval: Retrieve relevant book passages to provide context for AI-generated answers.
📖 Context-Aware Answers: Help readers understand concepts, summarize sections, and find relevant information.
⏱️ Save Reading Time: Reduce the effort required to locate specific information in lengthy books.

The goal is not to replace reading, but to make reading, learning, and exploring books more interactive.

🎬 Demo

🌐 Live Website: https://getmyebook.in/

The existing website is available here. The Chat with Book AI feature is currently being developed and should be demonstrated once its implementation is ready.

💻 Source Code

🔗 GitHub Repository: https://github.com/Vasanth2005kk/GetMyEBook-Web

Explore the repository to follow the project's development and future improvements.

🛠️ How I Built It

The project combines a digital library with an AI-powered question-answering workflow.

The planned Chat with Book AI feature uses Retrieval-Augmented Generation (RAG) to connect a language model with the content of a selected eBook.

🧠 How the RAG System Works

Book Processing: Extract readable text from the selected eBook.
Text Chunking: Divide the extracted text into smaller, meaningful chunks.
Embedding Generation: Convert chunks into vector representations using an embedding model.
Vector Storage: Store the embeddings in a vector database or vector index.
Question Processing: Convert the reader's question into an embedding.
Relevant Retrieval: Retrieve the book passages most relevant to the question.
Answer Generation: Provide the retrieved passages as context to a language model, which generates an answer based on the available content.

🔄 RAG Architecture

Reader → Select a Book → Ask a Question → Retrieve Relevant Passages → Generate an Answer → Display the Response

This approach can make answers more relevant to the selected book and reduce unsupported responses when retrieval and prompting are implemented carefully.

The specific language model, embedding model, vector database, and application framework will be documented according to the actual implementation.

🌍 Why Does Open Innovation Matter?

Open innovation allows developers to build on shared knowledge, experiment with different technologies, and create solutions that benefit the wider community.

For Chat with Book AI, open-source AI models and frameworks can provide greater flexibility in choosing how language generation, embedding creation, and retrieval are implemented.

Potential benefits include:

Transparency: Understand how the retrieval and answer-generation pipeline works.
Flexibility: Experiment with different models and retrieval strategies.
Learning: Explore practical applications of RAG, embeddings, and vector search.
Community Collaboration: Invite contributors to improve document processing, retrieval quality, and user experience.
Accessibility: Work toward making AI-assisted reading available to more learners and readers.

The project is an opportunity to explore how open-source AI can make existing digital resources more interactive and useful.

🤖 My Agent Session

I will share my DevRelay agent session here if I save one during the development process.

This can document the implementation steps, debugging process, and decisions made while building the RAG pipeline.

⏱️ The Lazy Payoff

Imagine studying a long technical book and needing to understand one specific concept.

Instead of repeatedly searching through chapters, a reader could ask Chat with Book AI a direct question and receive an answer based on relevant passages from the book.

This could reduce time spent locating information, simplify revision, and help readers explore unfamiliar topics.

The actual time saved and answer quality will need to be evaluated after implementation and testing.

🏆 Prize Categories

I will select the applicable partner prize categories after confirming the challenge requirements and the open-source AI technologies used in the completed implementation.

🚀 What's Next?

My next steps are to:

Implement the complete RAG pipeline.
Integrate an appropriate language model and embedding model.
Add reliable book-text extraction and chunking.
Build semantic retrieval for book-specific questions.
Improve answer grounding and provide source-page or passage references where possible.
Test the system with different books and questions.
Document the architecture so other developers can contribute.

🙌 Final Thoughts

GetMyEBook-Web started as a digital reading platform. With Chat with Book AI, I want to explore how Retrieval-Augmented Generation can make reading more interactive and help people learn directly from their books.

This project is part of my journey in web development, AI engineering, and open-source contribution.

I look forward to learning, building, experimenting, and sharing improvements with the developer community.

Thanks to the Hacktoberfest and DEV Community teams for encouraging developers to build and contribute to open source!

SharNova – Secure, Temporary File Sharing

vasanth k — Wed, 14 Oct 2026 05:23:59 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I built SharNova, a secure and simple web application that allows users to share notes and files without creating an account.

SharNova generates a unique six-digit access code for every shared item. Users can protect their content with a password and set an expiration time, after which the data is automatically deleted.

The application helps students, developers, freelancers, and teams share information quickly and conveniently.

Target users: Students, developers, freelancers, and small businesses.

Potential business model: Offer a free basic version and explore premium features such as larger file uploads, extended expiration periods, and additional security options.

Demo

Live Demo: [https://github.com/Vasanth2005kk/ShareNova/blob/main/Screencast%20from%202026-10-14%2010-41-49.webm]

Code

GitHub Repository: [https://github.com/Vasanth2005kk/ShareNova]

How I Built It

SharNova is designed to make temporary file sharing simple, convenient, and secure.

Key features include:

Share notes and files without registration.
Generate unique six-digit access codes.
Protect shared content with passwords.
Set expiration times for shared content.
Automatically delete expired data.

Technology Stack:

Frontend: React 19, Vite 6, JavaScript
Styling: Tailwind CSS v4
Animations: Framer Motion
Routing: React Router v6
Backend: Python 3.12, FastAPI
Database: SQLite (default) / PostgreSQL (optional)
ORM: SQLAlchemy 2 (Async)
Database Migrations: Alembic
Validation: Pydantic v2
Security: bcrypt password hashing, rate limiting, and MIME type validation
File Storage: Local filesystem
Background Tasks: APScheduler

Open-Source AI: SharNova currently focuses on secure, temporary file sharing. No open-weight AI model has been integrated into the application yet.

Why Does Open Innovation Matter?

Open innovation helps developers build better solutions by learning from existing projects, sharing knowledge, and collaborating with the open-source community.

For SharNova, open-source technologies provide flexibility and opportunities for customization and community contributions. Integrating open-weight AI models could also enable new features while providing greater control over deployment and data handling.

I believe open innovation makes technology more accessible and encourages developers to create practical solutions to real-world problems.

DEV Community: vasanth k