khissBkr Helping you sort your books locally.

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

What I Built

Khissbooker is an AI-powered desktop application that automatically organizes personal media libraries of Anime, Donghua, Manhua, Manhwa, Manfra, Light Novels, and Web Novels.

The Problem: Fans in regions without official licensing, or those preserving media when fan sites get taken down, often end up with thousands of disorganized files. Manually tagging and sorting them is impossible. Cloud-based solutions are expensive, invade privacy, and often refuse to process content due to corp…Translation Groups** who need to organize massive archives

• Collectors with libraries of 1,000+ downloaded files
• Book Clubs & Communities who want to share organized catalogs
• Fans in restricted regions who rely on personal archives when official sites are geo-blocked

The Side Hustle Business Model: Freemium. Free tier = unlimited local processing with Llama 3.2. Pro tier ($5 one-time) = Cloud processing with Gemma for low-end laptops, plus MongoDB cloud sync across devices.

Demo

khissbooker.tech

HeroOfMyLife / khissbooker

How I Built It

Khissbooker is built around open-weight AI models at its core:

• Local Inference (Free Tier): Uses Llama 3.2 1B via Ollama for completely offline, private tagging. The model runs directly on the user's laptop — no internet required, no API costs.
• Cloud Inference (Pro Tier): Uses Google's Gemma 2 2B via Google AI Studio for higher accuracy when users opt into the paid tier.
• Text Extraction Engine: Custom Python module using pypdf and ebooklib to read .pdf and .epub files, extracting the first few pages for AI analysis.
• Database: MongoDB Atlas (Free M0 Tier) stores metadata for optional cross-device library syncing.
• UI: Built with CustomTkinter for a modern, dark-mode desktop experience.
• Development: Accelerated with GitHub Copilot for rapid UI prototyping and database schema generation.

The app follows a hybrid architecture: it always tries the local model first (preserving privacy and keeping costs at $0), and only falls back to the cloud API if the local model struggles or the user explicitly chooses "Pro Mode."

Why Does Open Innovation Matter?

Open innovation is the entire reason Khissbooker can exist as a viable side hustle. Here's why a closed API approach would fail:

1. Privacy & Legal Safety: Personal media archives are sensitive. Users will not upload thousands of downloaded novels to a corporate cloud API. Open-weight models run locally, so raw text and images never leave the user's machine.

2. Cost Prohibitive at Scale: Scanning a library of 5,000 files via OpenAI's API would cost $50–$150+ in API fees. That destr…odel, serving users who are otherwise locked out of organized library tools.

3. No Vendor Lock-in: If Google or OpenAI changes their pricing tomorrow, a closed-API business dies. With open-weight models, Khissbooker can swap models freely — today it's Llama 3.2, tomorrow it could be any new open model that's better.

In short: A closed API would make Khissbooker too expensive, too invasive, and too restricted to serve its target audience. Open innovation made this side hustle possible.

Prize Categories

I'm entering the following categories:

• 🏆 Main Prize ($250) — Side Hustle: Best open-source AI side hustle project
• Best Use of Gemma ($200) — Gemma 2 2B via Google AI Studio for cloud-based tagging (Pro Tier)
• Best Use of MongoDB Atlas ($100) — MongoDB Atlas Free M0 Tier for cross-device library metadata sync
• Best Use of a .tech Domain ($100) — Registered khissbooker.tech via Hacktoberfest promo