A lot of developers and students have skills they could turn into a side hustle. The difficult part is often deciding what to sell, how to price it, who to approach, and what the first steps should be.
For Hacktoberfest's Side Hustle challenge, I built SoloSprint AI a privacy-first assistant designed to help turn an early business idea into an actionable plan.
What is SoloSprint AI?
SoloSprint AI helps users turn their skills and ideas into a structured starting point for a side business. It is built around local AI inference rather than requiring users to send their ideas to a hosted AI service.
The goal is simple: make AI assisted planning more accessible to people who are just getting started.
See it in action
Demo video: https://youtu.be/ESk3F5Y83bQ
Source code: https://github.com/Samearth17/SoloSprint-AI
How it works
The project uses Python and FastAPI for the application backend, with Ollama providing access to a local open-weight language model.
The intended workflow is straightforward:
Describe a side hustle idea and your skills.
Generate a structured plan with practical next steps.
Use that starting point to think through your offer, pricing, and client outreach.
The application is designed to run locally, allowing users to experiment without making a third-party AI API a required part of the workflow.
Why open innovation matters
For me, open source AI is exciting because it gives developers more control over the tools they build.
Running inference locally can help keep personal ideas and early client briefs on a user's own machine. Open model ecosystems also make it easier to experiment with different models and adapt a project to different hardware, budgets, and needs.
That flexibility matters for students, independent developers, and freelancers who want to experiment without immediately taking on the costs or dependencies of a hosted AI service.
Built with
Python
FastAPI
Ollama
HTML, CSS, and JavaScript
An open-weight language model
What I learned
Building SoloSprint AI reinforced the importance of making AI useful for a specific problem rather than adding a model just for the sake of using one.
I also had to work through local environment and dependency issues while getting the application running. That is part of building software too: getting from an idea to something another person can actually run.
What's next?
I would like to continue improving the user experience, add more flexible output formats, and make it easier for other developers to contribute.
Feedback and suggestions are welcome in the GitHub repository.
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