Hacktoberfest Open-Source AI Challenge: Week 4 – Ghost in the Machine
Introducing RepoGhost AI, an AI-powered codebase investigation platform designed to help developers understand, explore and rediscover the hidden history of their software projects.
Every software repository has a story. Some functions are undocumented, some modules become forgotten, and important development decisions can disappear as projects evolve. Understanding an unfamiliar or older codebase can become a time-consuming challenge.
RepoGhost AI aims to solve this problem by combining Git intelligence, code analysis, semantic search and open-weight AI into one developer-focused platform.
Developers can explore repository structures, investigate commit histories, understand complex source code, discover potentially unused code and generate technical documentation through an intelligent interface.
Inspired by the Hacktoberfest Week 4 theme, Ghost in the Machine, RepoGhost AI brings forgotten parts of software projects back into focus.
The inspiration behind RepoGhost AI comes from a simple observation: software repositories contain more than just source code. They contain the history, decisions, experiments and knowledge of the developers who created them.
When developers leave a project or when a codebase grows over time, valuable knowledge can become difficult to recover.
I wanted to explore whether open-source AI could act as a digital investigator that helps developers uncover this hidden knowledge.
The Ghost in the Machine theme provided the perfect inspiration. Instead of imagining ghosts as supernatural entities, I interpreted them as forgotten functions, abandoned modules, undocumented code and hidden histories inside software repositories.
RepoGhost AI is my attempt to give those forgotten parts of software a voice again.
Explore repository structures, folders, files, programming languages and project statistics through a unified dashboard.
Investigate Git commit history, contributor activity, file modifications and code evolution to understand how a project has changed over time.
Analyze source code to identify functions, classes, complexity indicators, duplicate-code candidates and potentially unused code.
Interact with an AI assistant to ask questions about repositories, explain functions, summarize files and understand project architecture.
Build searchable project knowledge using embeddings and retrieval-based techniques to help developers find relevant code and documentation.
Generate draft README files, architecture descriptions, API documentation and developer onboarding guides.
Visualize repository activity, programming language distribution, commit trends and project health indicators.
Open-source AI is at the heart of RepoGhost AI.
The project is designed around open-weight models and local inference to explore how developers can use AI while maintaining greater control over their source code and development environment.
Planned AI Model: Google Gemma
Model Runtime: Ollama
AI Architecture: Retrieval-Augmented Generation (RAG)
Vector Database: ChromaDB
Inference Approach: Local inference
The proposed AI workflow combines repository analysis, code chunking, embeddings, semantic retrieval and local model inference.
When a developer asks a question, the application retrieves relevant code and documentation from the selected repository and provides that context to the language model.
This approach aims to generate more relevant, repository-specific explanations instead of relying only on generic programming knowledge.
The open-weight approach also provides flexibility to experiment with different models, customize the inference environment and reduce dependency on proprietary AI services.
Note: The final model integration and inference results will be documented according to the implementation actually completed.
| Component | Technology |
|---|---|
| Frontend | React + Vite |
| Language | TypeScript |
| UI Styling | Tailwind CSS |
| Backend | Python + FastAPI |
| Git Analysis | GitPython |
| Code Analysis | Python AST / Tree-sitter |
| AI Model | Gemma |
| AI Runtime | Ollama |
| Embeddings | Local embedding model |
| Vector Database | ChromaDB |
| Metadata Storage | SQLite |
| API Communication | REST API |
RepoGhost AI follows a repository intelligence pipeline.
Step 1 – Repository Selection
The developer selects a local Git repository for analysis.
Step 2 – Repository Processing
The backend examines the directory structure, source files, documentation and available Git history.
Step 3 – Code Analysis
The system identifies files, functions, classes, programming languages and other code-related information.
Step 4 – Knowledge Indexing
Relevant source-code sections and documentation are divided into meaningful chunks and converted into vector embeddings.
Step 5 – AI-Powered Retrieval
When a developer asks a question, the application retrieves relevant repository context using semantic search.
Step 6 – Local AI Inference
The retrieved context is supplied to the configured open-weight model through Ollama.
Step 7 – Intelligent Response
The application presents explanations, summaries and relevant source references through the RepoGhost AI interface.
Open innovation is important because developers should have greater control over the tools they use and the data they work with.
For RepoGhost AI, an open-weight AI approach provides several advantages:
My goal is to demonstrate how open-source AI can become a practical part of everyday software development.
Live Demo: [https://lumina-puce-ten.vercel.app/]
GitHub Repository: [https://github.com/asusvivobook176-svg/lumina]
Screenshots:
git clone YOUR_REPOSITORY_URL
cd RepoGhost-AI
cd frontend
npm install
npm run dev
Open another terminal:
cd backend
py -m venv venv
Activate the virtual environment on Windows:
venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Start the FastAPI server:
uvicorn app.main:app --reload --port 8000
Install Ollama and download the selected Gemma model according to your system's available resources.
ollama pull gemma3:1b
Start the model:
ollama run gemma3:1b
The selected model and its configuration should match the model used by the application.
RepoGhost AI has several opportunities for further development:
I would like to thank the Hacktoberfest team, DEV Community and the open-source AI ecosystem for creating opportunities to explore, experiment and build meaningful projects.
This project is an exploration of how open-source AI can help developers rediscover the knowledge hidden inside their software repositories.
Every repository has a history. Every function has a purpose, and every project contains knowledge that deserves to be understood.
With RepoGhost AI, I want to bring that hidden knowledge back to life.
Your Codebase Has a Ghost. Let AI Reveal Its History.
Project: RepoGhost AI
Theme: Hacktoberfest 2026 – Ghost in the Machine