CommitGhost 👻 — Turn Your Existing Code Into Your Next Business With Local AI

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

What I Built

CommitGhost — Your Unfinished Code. Your Next Business.

Developers spend hours building projects, but sometimes their code never becomes a product. What if the code they have already written contains the foundation for a business?

That's the idea behind CommitGhost.

CommitGhost is an AI-powered developer tool that analyzes a public GitHub repository and discovers three potential business opportunities based on its existing functionality.

Instead of generating random startup ideas from a prompt, CommitGhost examines the repository's README, programming languages, dependencies, file structure, and selected source files.

It then uses a locally running AI model to generate three potential opportunities, each with:

• A target customer and the problem they face.
• A proposed solution.
• An explanation of how the existing code could help.
• A suggested pricing model.
• An implementation difficulty rating.
• A practical MVP implementation plan.

The goal is simple: help developers discover ways to turn existing code into something people might actually pay for.

Pricing recommendations are hypotheses, not validated market research. The opportunities are starting points that developers can evaluate and test with real customers.

Demo

GitHub Repository:

https://github.com/naveensri4518/Ghost-Commit

Demo Video:

Watch the CommitGhost Demo

The application currently runs locally. It uses a dark, purple glassmorphism interface inspired by modern developer tools.

The workflow is straightforward:

1. Enter a public GitHub repository URL.
2. CommitGhost retrieves repository metadata and inspects relevant files.
3. A local AI model analyzes the collected information.
4. Three potential business opportunities appear in the dashboard.
5. Copy an opportunity's business plan to explore it further.

Code

Source code: https://github.com/naveensri4518/Ghost-Commit

The project uses a React frontend and a Python FastAPI backend.

The backend retrieves real repository information from GitHub and sends a concise summary to the locally running AI model. The frontend presents the results in an interactive dashboard.

How I Built It

I built CommitGhost using:

• React + Vite for the frontend.
• Tailwind CSS for styling.
• Lucide React for icons.
• Python + FastAPI for the backend.
• GitHub REST API for repository metadata, language statistics, README content, and file-tree inspection.
• Ollama for local model inference.
• Qwen 2.5 3B as the open-weight language model.

The interface uses a dark background, translucent purple cards, subtle borders, ambient lighting, and smooth transitions.

The most important part is the AI pipeline.

CommitGhost doesn't simply ask a model to invent three businesses. It first collects information about an actual repository and uses that information as context for generating opportunities.

The backend also validates the model's structured response before returning the results to the frontend.

I encountered a performance challenge during development: my local model runs on CPU, so generating detailed recommendations takes considerably longer than fetching repository data.

I addressed repeated analysis delays by introducing an in-memory cache. When a repository already has a cached result, the application can return that result without running inference again.

This keeps the architecture small and avoids introducing unnecessary databases, paid AI APIs, or complicated agent frameworks.

Why Does Open Innovation Matter?

For CommitGhost, open innovation isn't just a technology choice. It changes how the application can be run and customized.

1. Local inference

CommitGhost uses Ollama to run Qwen 2.5 3B locally rather than sending prompts to a paid, hosted AI API.

This avoids per-request charges from a hosted AI provider, although local hardware and electricity still have costs.

2. More control over data

Because inference happens locally, repository information included in the model prompt can stay on the developer's machine instead of being sent to a hosted AI provider.

GitHub access still requires an internet connection, and this does not mean the entire application is offline.

3. Freedom to experiment

Using a locally runnable open-weight model gives developers more control over model selection, prompts, inference settings, and future customization.

The model can be changed without redesigning the entire application.

4. Accessibility for small projects

A developer can experiment with CommitGhost without needing a paid AI API subscription.

The trade-off is performance: local inference depends on available hardware, and CPU inference can be slow.

For a small developer tool, that trade-off is worth exploring because it offers more control over operating costs and how AI is used.

Prize Categories

[Add the applicable partner prize categories listed on the official challenge page. Remove this section if no partner category applies.]