This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
https://github.com/keerthisree-25/side-hustle
SideHustle AI: A Privacy-First Open-Source AI Assistant for Freelancers
What I Built
SideHustle AI is an open-source, privacy-focused business assistant built for freelancers, independent creators, tutors, and small business owners.
Managing a side hustle involves more than doing the actual work. People also need to organize receipts, track expenses, create invoices, monitor payments, and understand their finances. SideHustle AI brings these tasks together in one application.
Key features
AI Receipt Scanner: Extracts information from PDF invoices and receipt images using text extraction, OCR, and local AI models.
Expense Tracker: Organizes income and expenses with searchable records, categories, and CSV import/export.
Invoice Generator: Creates professional PDF invoices with customizable business details, line items, and tax calculations.
Payment Reminders: Generates polite email drafts for following up on outstanding invoices.
AI Business Assistant: Answers questions about spending and business finances using locally stored data and verifiable financial context.
Financial Reports: Generates monthly profit-and-loss summaries and exports reports as PDF or CSV.
Duplicate Detection: Identifies duplicate uploads using SHA-256 hashes and matching transaction details.
Who would pay for it?
Freelancers, consultants, designers, developers, and small business owners who want simpler financial management could benefit from this tool.
The core application is free and open source under the MIT license. A possible future business model is a paid Pro tier for advanced features such as bulk receipt processing and multiple business profiles.
The goal is simple: help people spend less time managing paperwork and more time growing their businesses.
Demo
Live demo: [Add your deployed application URL]
Video walkthrough: [Add your demo video URL, if available]
Until a hosted demo is available, you can run SideHustle AI locally using the instructions in the GitHub repository.
Code
The complete source code is available on GitHub:
Repository: https://github.com/keerthisreebandlamudi/sidehustle
The project is released under the MIT License.
Technology stack:
Frontend: React, TypeScript, Vite, Tailwind CSS
Backend: Python, FastAPI, Pydantic
Database: SQLite and SQLAlchemy
Open-source AI: Ollama with supported models such as Qwen 2.5, Gemma 2, and Llama 3.2
Document processing: PyPDF, Tesseract OCR, and Pillow
PDF generation: ReportLab
How I Built It
I built SideHustle AI using a local-first architecture that combines traditional software engineering with open-weight AI models.
- Local document processing
The application extracts text from digital PDFs and uses OCR for scanned documents and images. When Ollama is available, a local language model helps structure the extracted information into useful invoice fields.
- Reliable financial calculations
I deliberately kept financial calculations in Python rather than relying on an AI model to calculate totals. Invoice amounts, taxes, expenses, and profit figures are computed by application code to reduce the risk of incorrect financial answers.
- AI with verified financial context
The business assistant retrieves relevant information from the local SQLite database and uses that context to answer questions about spending and outstanding invoices.
When the local model is unavailable, deterministic parsing and fallback logic help maintain core functionality.
- A complete web application
The React frontend communicates with a FastAPI backend. Users can manage transactions, generate invoices, review financial summaries, and interact with the assistant through a single interface.
- Testing and reliability
The repository includes automated tests for financial calculations, invoice totals, duplicate detection, PDF generation, and data retrieval.
Why Does Open Innovation Matter?
Financial information is sensitive. Receipts and invoices can reveal client relationships, business expenses, and personal information.
Open innovation made it possible to build an AI-powered financial assistant without making a proprietary cloud AI service a mandatory part of the architecture.
Privacy: Local inference through Ollama allows supported AI processing to happen on the user's machine rather than requiring financial documents to be sent to an external AI API.
Lower operating costs: Local models avoid per-request cloud AI inference charges, although users still need suitable hardware and may incur electricity and setup costs.
Freedom to experiment: Open-weight models can be swapped or evaluated without redesigning the entire application around one proprietary provider.
Transparency and customization: Developers can inspect the source code, adapt the application to their workflows, and contribute improvements.
Resilience: Combining local AI with deterministic fallback logic helps the application continue performing supported tasks even when the AI runtime is unavailable.
For me, open innovation is about making useful AI more accessible while giving developers and users greater control over their tools and data.
My Agent Session
I used an AI coding assistant during development to help with implementation, debugging, and testing.
Agent session: Add your DevRelay session link or the appropriate challenge-supported session embed if you have one.
Prize Categories
Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle
Open-source AI and open-weight models
Privacy-first and local-first software
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