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.
What if your unfinished code could become your next source of income?
Developers spend hours building projects, experimenting with technologies, and solving technical problems. But sometimes, their projects never become products.
What if the code they have already written contains the foundation for a business?
That's why I built 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 repository metadata, programming languages, dependencies, README content, file structure, and selected source files.
It then uses a locally running AI model to generate three potential product opportunities.
Each opportunity includes:
- 🎯 Target Customer: Who might benefit from the product.
- 🔴 Problem Statement: The specific problem the product could solve.
- 💡 Proposed Solution: How the product could address that problem.
- 🧩 Codebase Leverage: How existing code could help build it.
- 💰 Pricing Hypothesis: A possible monetization strategy.
- âš¡ Difficulty Rating: An estimate of implementation complexity.
- 📋 MVP Blueprint: Practical steps to build an initial version.
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 generated opportunities are starting points that developers can evaluate with real customers.
Demo
🎬 Watch the CommitGhost Demo:
The application features a dark, purple glassmorphism interface inspired by modern developer tools.
The workflow is straightforward:
- Enter a public GitHub repository URL.
- CommitGhost retrieves repository metadata and inspects relevant files.
- A locally running AI model analyzes the collected information.
- Three potential business opportunities appear in the dashboard.
- Expand implementation plans and copy business plans to explore the ideas further.
Code
💻 GitHub Repository:
The project uses a React frontend and a Python FastAPI backend.
The backend retrieves repository information from GitHub and passes relevant context to the local AI model. The frontend presents the analysis in an interactive dashboard.
How I Built It
I built CommitGhost using the following technologies:
- React + Vite: Frontend application.
- Tailwind CSS: Styling and responsive layouts.
- Lucide React: Interface icons.
- Python + FastAPI: Backend API.
- GitHub REST API: Repository metadata, language statistics, README content, and file-tree inspection.
- Ollama: Local AI inference.
- Qwen 2.5 3B: Open-weight language model.
The interface uses a dark background, translucent purple cards, subtle borders, ambient lighting, and smooth transitions.
The AI Pipeline
The central idea behind CommitGhost is to ground its recommendations in an existing codebase.
First, the backend retrieves repository metadata and inspects relevant project files. It then prepares a concise summary of the repository's architecture, technologies, and functionality.
That information becomes the context for the local language model, which generates structured business opportunities.
The backend validates the model's response before returning the results to the frontend.
A Performance Challenge
One challenge I encountered was inference speed.
My Qwen 2.5 3B model runs locally on CPU, so generating detailed recommendations takes considerably longer than retrieving repository information from GitHub.
To avoid repeating expensive inference unnecessarily, I implemented an in-memory cache. When the same repository already has a cached analysis, CommitGhost can return that result without generating everything again.
This keeps the application simple without introducing unnecessary databases or paid AI APIs.
Why Does Open Innovation Matter?
For CommitGhost, open innovation is more than a technology choice. It gives developers greater control over how AI runs and how their applications are built.
1. Local Inference
CommitGhost runs Qwen 2.5 3B through Ollama rather than relying on a paid, hosted AI inference API.
This avoids per-request charges from a hosted AI provider, although local hardware and electricity still have costs.
2. More Control Over Data
The repository information included in the model prompt can remain on the developer's machine during local inference instead of being sent to an external AI inference provider.
GitHub access still requires an internet connection, so the entire application is not completely offline.
3. Freedom to Experiment
Using a locally runnable open-weight model gives developers more control over prompts, inference settings, model selection, and future customization.
It also makes experimentation possible without depending entirely on one hosted AI provider's pricing or availability.
4. Accessibility for Small Projects
A developer can experiment with CommitGhost without purchasing a hosted AI API subscription.
The trade-off is performance. Local inference depends on available hardware, and CPU inference can be slow.
For this project, the ability to experiment locally and control inference costs makes open-weight AI a practical choice.
What I Learned
Building CommitGhost reinforced an important idea for me:
AI becomes more useful when it works with something real.
Instead of asking a model to invent startup ideas from nothing, I built a workflow that examines an existing repository and uses its technical context to explore potential business opportunities.
The next step is to put those recommendations in front of real developers, gather feedback, and discover which ideas are genuinely worth building.
CommitGhost doesn't ask AI to invent another startup idea. It looks at what you've already built and helps you discover what it could become. 👻
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