This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
Meet Bizcuits - an AI-assisted business workspace built for small sellers who are trying to grow their side hustles without getting buried under the little things that come with running a business.
Running a small business means juggling products, records, numbers, and decisions, often without having a whole team to help. I wanted to build something that brings business management and AI assistance together in one place, making it easier for sellers to understand their business and spend more time growing it.
Bizcuits combines a React frontend with a Python backend, data analysis using Pandas, and a PostgreSQL-ready database setup. The goal is to make useful business tools more accessible to people who are building something of their own.
I didn't want AI to be just another chatbot sitting beside a dashboard. I wanted it to be part of a workspace designed around an actual problem: running a small business.
Demo
Live application: https://bizcuits.onrender.com
The application is deployed on Render, with Neon PostgreSQL configured as its hosted database.
Code
GitHub repository: https://github.com/mehroofsaba/Bizcuits
The project includes the React + Vite frontend, Python backend, dependency files, and Render deployment configuration.
Feel free to explore the repository, check out the implementation, and contribute ideas or improvements!
How I Built It
Bizcuits is built with:
- React + Vite for the frontend.
- Python + Flask for the backend.
- Pandas for data analysis.
- PostgreSQL for hosted database storage, using Neon.
- Gemma 3 27B as the configured AI model, with a backend setup designed to support hosted inference through Google AI Studio or local inference through Ollama.
- Render for deployment.
One of the most exciting parts of building this project was taking it beyond my local machine. I had to work through Git, environment variables, deployment configuration, and database connectivity to get Bizcuits online.
I also learned to work with PostgreSQL during deployment. Before this project, I hadn't worked with it, so setting up Neon and connecting it to Render became part of the learning experience.
The result is a deployed application that I can continue improving, testing, and developing into something useful for real small-business owners.
Why Does Open Innovation Matter?
For a project like Bizcuits, open innovation means having more freedom over how AI fits into the application.
Using an open-weight model such as Gemma gives developers more flexibility than building around a single closed model provider. With a compatible inference setup, a model can be run locally through tools such as Ollama, giving developers more control over their deployment environment and data flow. Hosted inference can also make experimentation easier without requiring everyone to run a large model on their own hardware.
That flexibility matters when building tools for small businesses. I want Bizcuits to be adaptable rather than permanently tied to one particular AI service.
Open-source projects also make it possible for other developers to inspect the implementation, suggest improvements, and build on what already exists. A project becomes more valuable when it can grow beyond the person who started it.
For me, that's what makes open innovation exciting: you get to learn by building, share what you learn, and give other people something they can improve.
Prize Categories
Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle.
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