This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built a Privacy-First Price Comparator—a local AI tool that helps users find the best deals without sacrificing their personal data.
Most popular price-tracking extensions operate on a data theft model: they give you discounts, but they harvest your browsing history, shopping habits, and personal profiles to sell to advertisers. Even worse, some platforms use your search history to dynamically inflate prices.
This tool is designed for privacy-conscious shoppers, freelance procurement agents, and businesses who want to compare prices across e-commerce sites securely. They would pay a small one-time fee or subscription for a tool that runs entirely on their own machine, guaranteeing their data never leaves their laptop.
How I Built It
Instead of relying on centralized APIs that log user queries, this project uses local inference.
The Engine: It uses an open-weight model (like Google's Gemma) running locally.
The Process: The user feeds a product URL into the tool. The tool scrapes the raw HTML of the product pages (using standard Python scraping libraries).
The AI: The local AI model is prompted to parse the messy HTML, extract the exact price, shipping costs, and product specs, and output a clean JSON comparison—all without any data ever pinging an external server.
Why Does Open Innovation Matter?
Open innovation is the only way this product can exist.
If I built this using a closed API (like OpenAI or Anthropic), I would just be trading one privacy nightmare for another—sending my users' entire shopping intent and raw webpage data to a massive tech corporation.
By using open-weight models running locally, open innovation allows for absolute data sovereignty. It proves that we don't have to surrender our privacy just to get a good deal on the internet.
Prize Categories
- Open Source AI Side Hustle
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