Some code deserves a second life. 💚
While exploring GitHub, I sometimes come across interesting open-source projects that haven't been updated for years.
Some have good ideas, but their dependencies are old, documentation is incomplete, or the codebase is difficult to understand.
And when you're new to a repository, even figuring out where to start can take a lot of time.
So when I saw this week's Ghost in the Machine challenge, I got an idea.
What if we could bring these forgotten repositories back to life?
That's how GhostCommit started. 👻
👻 What is GhostCommit?
GhostCommit is an AI-powered developer tool that helps you explore, understand, and improve old or abandoned GitHub repositories.
You can import a repository, explore its source code, check potential issues, and use AI to understand what needs attention.
I wanted to build something more useful than just a chatbot where you paste code and ask questions.
The idea was to create a small developer workspace where you can actually investigate a project.
And because it's Halloween week, I thought a ghost investigating dead code would be a fun theme. 😂🎃
🔗 Live Demo: https://ghostcommit-8io2.onrender.com/
🔗 Github Repo: https://github.com/dev-yashpawar/GhostCommit
⚡ How Does It Work?
The workflow is pretty simple.
1. Import a Repository
You start by pasting a public GitHub repository URL.
GhostCommit imports the repository and collects information about its files, dependencies, and commit history.
It also supports importing approved local Git repositories.
2. Repository Autopsy 🔍
This is my favourite part.
Once the repository is imported, GhostCommit starts its investigation.
It checks things like:
- Project structure and source files
- Programming languages
- Dependencies
- Repository activity
- Missing or incomplete configurations
- Potential maintenance issues
I also added a file explorer and code viewer so developers can inspect the actual source files.
The interesting part is that the application doesn't depend only on AI for everything.
It first collects evidence using normal code-analysis tools.
3. AI Investigation 🧠
After collecting the repository information, GhostCommit uses Gemma, an open-weight AI model, to help explain the project.
The model receives selected repository context and helps identify possible problems and improvements.
For example, it can help explain an unfamiliar codebase or suggest areas that may need modernization.
One thing I tried to be careful about is not treating every AI suggestion as a confirmed issue.
Just because a dependency looks old doesn't automatically mean it's vulnerable.
So the application separates scan evidence from things that still need verification.
4. Resurrection Plan ⚡
Finding problems is only the first step.
GhostCommit also has a resurrection workflow for turning findings into possible repair tasks.
The idea is to help developers understand which improvements to work on first.
5. Patch Review 💻
I also built a workflow around generating and reviewing suggested code changes.
Instead of blindly changing the original repository, the goal is to let developers inspect proposed patches and approve changes themselves.
I believe AI should help developers make better decisions, not take away their control.
🛠️ Tech Stack
Here's what I used to build GhostCommit.
Frontend: React, TypeScript, Tailwind CSS, Monaco Editor
Backend: Node.js, Express, Git, Tree-sitter
Database: SQLite
AI: Gemma, Google Cloud inference, and Ollama support
Deployment: Render
I used Tree-sitter for source-code parsing and Git for working with repository history and files.
Gemma handles the AI-powered understanding and explanation part.
And Render makes the application accessible through a public URL.
🌍 Why Open-Weight AI?
For me, this was one of the most interesting parts of the challenge.
Usually, when we build an AI application, we connect an API and depend on that service.
But with open-weight models, we have more flexibility.
We can choose where the model runs, experiment with different models, and change how the application processes information.
GhostCommit supports Gemma through Google Cloud, and its Ollama integration provides another option for local inference.
The public demo currently uses Google Cloud, which means selected source-code information is sent to that provider during AI analysis.
I don't want to claim everything is offline when it isn't.
But the local approach is important to the project because developers may not want to send private source code to an external AI service.
With a suitable model and local setup, AI inference can happen on the developer's own machine.
And that's something I want to improve further.
For this project, open-weight AI isn't just an extra feature. It's what makes the code-understanding workflow possible while allowing flexibility in how inference is deployed.
🎃 Why GhostCommit?
The challenge theme was Ghost in the Machine.
There were so many interesting ideas I could have built, like a haunted terminal or a ghost chatbot.
But I wanted something connected to software development.
Then I thought about abandoned repositories.
They aren't really dead. Their code is still there, and sometimes their ideas are still useful.
They just need someone to understand them and continue the work.
That's where the name GhostCommit came from.
And I designed the interface around this idea.
A minimal black-and-green dashboard, glass effects, a little ghost, a pumpkin, and an AI investigation called Repository Autopsy.
I wanted it to feel like a real developer tool, with just enough Halloween atmosphere.
🧩 What I Learned
One thing I learned while building this is that getting a response from an LLM is easy, but making that response useful is much harder.
At first, the idea sounded simple.
Import a repository, send the code to AI, and ask what needs fixing.
But real repositories contain many files, different configurations, and sometimes code that is difficult to understand even for developers.
Sending everything directly to a model isn't a good solution.
So I worked on collecting relevant information first and then using that as context for the AI.
I also spent time thinking about security.
Importing somebody else's repository doesn't mean we should automatically execute its code.
And if AI suggests changes, the developer should review them before applying anything.
Another thing was the UI.
I probably spent more time than expected on the black-and-green design. 😂
But I wanted GhostCommit to feel different from a normal AI dashboard.
🚧 What's Next?
GhostCommit is still an early project, and there's a lot I want to improve.
My next goals are better analysis of large repositories, more accurate dependency checks, safer automated testing, and improved patch generation.
I also want to experiment more with running coding models locally.
Eventually, I would like GhostCommit to help open-source contributors understand unfamiliar projects and start contributing more easily.
💚 Final Thoughts
I built GhostCommit because I liked the idea of giving forgotten projects another chance.
Not every abandoned repository needs to be revived, but some still contain useful code, good ideas, or something worth learning from.
And I think AI can make exploring those projects a little easier.
This project is my small experiment with open-weight AI, developer tools, and a bit of Halloween creativity.
There is still more work to do, but I enjoyed building it and learned a lot along the way.
Some code deserves a second life. 👻
🔗 Try GhostCommit: https://ghostcommit-8io2.onrender.com/
💻 Source Code: https://github.com/dev-yashpawar/GhostCommit
I'd love to hear what other developers think.
Have you ever found an abandoned GitHub project that you wished someone would maintain again?
Happy Hacktoberfest! 🎃💚
Top comments (1)
This is why I think the interface matters as much as the model.