HustleMate AI——本地發票助理

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

What I Built

I built HustleMate AI — a Local Invoice Assistant, a privacy-focused tool designed to help freelancers and small business owners organize their invoices more easily.

Freelancers often spend valuable time manually reading invoices, extracting payment details, tracking due dates, and organizing financial records. HustleMate AI aims to simplify this process by turning invoice text into structured information.

With HustleMate AI, users can:

• Extract important invoice details, including vendor name, invoice number, invoice date, due date, and total amount.
• Generate an AI-powered summary and suggested next action.
• Save organized invoice records in their browser.
• Export records as a CSV spreadsheet.

The project is designed for freelancers, independent designers, consultants, tutors, and small businesses that want a simpler way to manage invoice information.

My goal is to make practical AI tools accessible to people who cannot afford expensive subscription-based services.

Demo

Code

The project uses HTML, CSS, and JavaScript for the frontend, with a Python backend that connects to a locally running AI model through Ollama.

The main components are:

• index.html — Website structure and invoice dashboard.
• style.css — Responsive styling and user interface.
• script.js — Frontend interactions, record management, and CSV export.
• app.py — Backend API for local AI inference.
• requirements.txt — Python dependencies.

How I Built It

I built HustleMate AI around the idea that AI-powered productivity tools should be useful, affordable, and privacy-conscious.

The frontend is built using HTML, CSS, and JavaScript. A lightweight Python Flask backend receives invoice text and sends it to a locally running model through Ollama.

The project is designed to use Qwen2.5 3B, an open-weight language model, for extracting structured information from invoice text.

The workflow is straightforward:

1. The user pastes invoice or receipt text into the application.
2. The backend sends the text to the local model.
3. The model returns structured invoice information in JSON format.
4. The application displays the extracted details and suggested next action.
5. The user can save the record in the browser and export saved records to CSV.

The local AI integration is an important part of the project. The application is designed to avoid sending invoice text to a third-party hosted AI API.

Why Does Open Innovation Matter?

Open innovation is central to HustleMate AI because privacy and affordability are important for freelancers and small businesses.

1. Privacy and data control

Invoices can contain sensitive business information. Running inference locally helps users retain control over their data instead of relying on an external AI provider.

2. Lower running costs

After downloading the model and installing the required software, users can perform inference locally without paying per-request fees to a hosted AI API. Hardware, electricity, and setup still have costs.

3. Freedom to experiment

Using an open-weight model makes it possible to experiment with different models, adjust prompts, and potentially customize the system for particular industries or invoice formats.

4. Less vendor lock-in

The project does not depend on a single hosted AI provider's API pricing or usage limits. The model can be changed as requirements evolve.

5. Potential for offline use

Once the required software and model are installed, invoice analysis can work without an internet connection, provided the local services are running.

For me, open innovation is about making useful AI more accessible while giving users greater control over their information and tools.

Prize Categories

Primary theme: Side Hustle — practical AI for freelancers and small businesses.

HustleMate AI explores how open-weight models can support an affordable productivity tool with potential commercial value.

I will add any specific partner prize categories once I confirm which categories apply to my project.