This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
GigPilot AI is a local-first assistant designed to help freelancers turn client messages into structured project plans.
Freelancers often receive rough requirements through WhatsApp messages, emails, or informal conversations. Before starting the actual work, they need to understand the scope, identify missing information, estimate a timeline, prepare a quotation, and respond professionally.
GigPilot AI brings these steps into one workspace. It generates a project summary, suggested deliverables, clarifying questions, potential scope risks, an estimated timeline, a quotation draft, and a client reply.
The goal is to make project planning easier for independent freelancers, designers, developers, and other people offering services online.
Demo
The application runs locally on my machine and uses Ollama for local AI inference.
GitHub repository: https://github.com/uditsharma45/gigpilot-ai
A publicly hosted demo is not available yet. The repository includes the code and setup instructions for running the project locally.
Code
Source code: https://github.com/uditsharma45/gigpilot-ai
The project contains a React and Vite frontend, a Python FastAPI backend, and integration with Ollama for local model inference.
How I Built It
I built GigPilot AI using React, Vite, JavaScript, CSS, Python, FastAPI, and Ollama.
The AI layer uses the open-weight Qwen 2.5 1.5B model, with Gemma 3 1B also configured as an available model.
The frontend provides a workspace where a freelancer can enter a client brief, choose a quotation currency, and request a project plan. The backend sends the brief to the local Ollama service, validates the structured JSON response, and returns the generated project information to the interface.
I tested the backend through FastAPI's interactive documentation. The generation endpoint successfully returned a structured response for a sample clinic website brief.
One important design decision was to keep inference local rather than requiring a hosted AI API. This makes the project useful for experimentation on a personal machine and helps keep client messages local when using the local inference workflow.
The project is still under development, and generated quotations and plans should be reviewed before being sent to clients.
Why Does Open Innovation Matter?
Open innovation makes it possible for developers to experiment with AI without depending entirely on closed, hosted APIs.
Using Ollama and open-weight models gave me the opportunity to run inference locally, inspect how the application communicates with the model, and build a complete workflow around structured AI output.
It also makes the project more approachable for developers who want to learn about AI integration without making a hosted API the only way to run their application.
For a tool that handles client briefs, local inference also offers a practical privacy advantage: messages can remain on the user's machine during local model processing.
Prize Categories
This project is submitted for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle. It focuses on building a practical freelance tool using open-weight AI models and local inference.
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