Ghost in the Codebase: Bringing Forgotten Code Back to Life with Open-Source AI | Hacktoberfest 2026

Mukesh Kumar — Thu, 29 Oct 2026 17:13:15 +0000

Ghost in the Codebase: Bringing Forgotten Code Back to Life

Submission for the Hacktoberfest Open-Source AI Challenge — Week 4: Ghost in the Machine

What I Built

Ghost in the Codebase is a local-first AI assistant designed to explore unfamiliar repositories and help developers understand the stories hidden inside their code.

Every repository has a history. Some are well-documented, while others contain confusing functions, forgotten modules, missing documentation, and architectural mysteries.

Inspired by Hacktoberfest Week 4's Ghost in the Machine theme, I wanted to make exploring unfamiliar codebases interactive, useful, and a little spooky.

The application helps developers:

Explore Haunted Repositories: Upload a ZIP archive and explore its file structure.
Resurrect Forgotten Code: Analyze important files, functions, dependencies, and project architecture.
Ask the Ghost: Ask questions about the codebase and receive explanations grounded in source files.
Uncover Hidden Passageways: Identify TODOs, missing documentation, complex functions, and potential maintenance issues.
Find a Path Forward: Generate contribution suggestions and a roadmap for understanding the project.
Hear the Repository's Story: Turn technical findings into an engaging narrative about how the software works.

The goal is simple: make unfamiliar code less intimidating and help developers contribute with confidence.

Demo

Live Demo: https://ghost-in-the-codebase.vercel.app

GitHub Repository: https://github.com/TheMukeshDev/Ghost-in-the-Codebase

The hosted demo runs the frontend. The Flask + Ollama analysis backend is designed to run locally, so the full analysis and AI features work on your own machine.

How I Built It

Ghost in the Codebase uses a modular full-stack architecture combining traditional static analysis with open-weight AI.

Technology stack

React, TypeScript, Vite, and Tailwind CSS for the frontend.
Python and Flask for backend APIs and repository analysis.
Ollama for running open-weight AI models locally.
Qwen2.5:3B as the default local model, subject to hardware compatibility.
Python AST and static analysis for extracting code structures and identifying potential issues.
SQLite for application metadata and session history.

The workflow follows five steps:

Import a repository.
Safely index and analyze its files.
Extract code structures, dependencies, and relevant relationships.
Retrieve relevant source code and use local AI to generate contextual explanations.
Present findings, source references, and contribution suggestions through an interactive interface.

The AI is not the sole source of truth. Deterministic analysis handles verifiable tasks, while AI helps explain the findings. The application is designed to preserve useful browsing and analysis features when Ollama is unavailable.

Why Does Open Innovation Matter?

Understanding a codebase should not always require sending its source code to a third-party AI service.

Open-weight models and open-source tools offer several advantages:

Privacy and control: Local inference can keep source code and prompts on the user's machine.
Model freedom: Developers can experiment with compatible models instead of depending on a single closed API.
Offline potential: After downloading the necessary software and model files, supported workflows can operate without an internet connection.
Cost control: Local inference avoids per-request charges from hosted AI APIs, although hardware and electricity still have costs.
Transparency: Developers can inspect, modify, test, and extend the analysis pipeline.

Open innovation also allows the community to add language analyzers, improve retrieval, experiment with different models, and develop new ways to understand complex repositories.

Local inference is not automatically more accurate or faster than a closed model. Its key advantage is giving developers more control over their code and tools.

My Agent Session

This session covers installing and wiring up DevRelay, repairing a corrupted frontend build, deploying the app, and publishing the source.

Session: Ghost in the Codebase: repairing a corrupted frontend, deploying, and publishing

Ghost in the Codebase: repairing a corrupted frontend, deploying, and publishing

You

Install DevRelay, connect my MLH/DEV account, then help me finish my Hacktoberfest Week 4 'Ghost in the Machine' submission.

Agent

Installed DevRelay 0.1.27 on Windows, registered the gateway as an MCP server in OpenCode, GitHub Copilot CLI, and VS Code, and installed the 9 DevRelay agent skills. Then completed the MLH OAuth sign-in and verified the DEV profile link.

▸ shell iwr -useb https://devrelay.com/install.ps1 | iex

Input

$ iwr -useb https://devrelay.com/install.ps1 | iex

Output

DevRelay 0.1.27 installed; skills synced to ~/.claude, ~/.agents, ~/.copilot; MCP wired into OpenCode and Copilot.

▸ shell devrelay --doctor

Input

$ devrelay --doctor

Output

All checks passed. MLH and DEV accounts connected.

You

The article still has blank Live Demo, GitHub Repository, and Agent Session placeholders. Fill them with real values.

Agent

I found the frontend source had been corrupted by earlier fix scripts: every template literal had lost its backticks and interpolations, and several JSX className attributes were left unquoted. I repaired the four broken API calls in Dashboard.tsx, rebuilt the tab navigation with active-state styling, fixed the post-import redirect, removed an unused import, and produced a clean production build.

▸ edit Repair template literals in frontend/src/pages/Dashboard.tsx and Import.tsx

Input

Repair template literals in frontend/src/pages/Dashboard.tsx and Import.tsx

Output

Replaced api.get(/repositories//tree) with api.get(`/repositories/${id}/tree`) and equivalent fixes for report, ask, delete, and the dashboard redirect.

▸ shell npm run build

Input

$ npm run build

Output

vite v5.4.21 built 1425 modules in 5.87s; dist/index.html plus CSS and JS assets emitted.

Agent

Deployed the fixed frontend to a new Vercel project without touching the existing linked project, and published the source to a new public GitHub repository. The live demo renders the Ghost in the Codebase landing page; the Flask/Ollama analysis backend remains local-first as designed.

▸ shell vercel deploy --prod --yes --project ghost-in-the-codebase --create-if-missin...

Input

$ vercel deploy --prod --yes --project ghost-in-the-codebase --create-if-missing --verify

Output

Production ready at https://ghost-in-the-codebase.vercel.app (title verified, HTTP 200).

▸ shell git push -u origin master

Input

$ git push -u origin master

Output

Pushed to https://github.com/TheMukeshDev/Ghost-in-the-Codebase.

The Spirit of It

There is something unsettling about opening a repository that nobody has touched in years.

The files are still there. The functions still execute. The architecture still holds together. But the people who originally built it may have moved on, and the reasoning behind their decisions may be lost.

Ghost in the Codebase is my attempt to give those projects a voice again.

Instead of treating unfamiliar code as a wall of syntax, the project turns it into something developers can explore, question, and understand.

Because sometimes, the ghost in the machine is simply an undocumented function waiting for someone to understand it.

Challenge Details

Challenge: Hacktoberfest Open-Source AI Challenge — Week 4: Ghost in the Machine

Submission deadline: October 31, 2026.

Theme: Build something spooky or inspired by remembrance using open-weight models, open-source AI frameworks, or local inference.

This project explores the theme by bringing forgotten code back to life with open-source AI, helping developers understand the software and preserve the knowledge hidden inside repositories.

Built for Hacktoberfest 2026 — Open-Source AI Challenge, Week 4.

Every codebase has a story. Let's bring it back to life.

The "Agentic Era": A Student’s Perspective

Mukesh Kumar — Sun, 26 Apr 2026 16:48:39 +0000

As a student, I’ve spent the last year building bots simple, linear code that does exactly what I tell it to. But watching Google Cloud NEXT '26, it finally clicked: we are moving from building bots to architecting agents.

The keynote wasn't just a product launch; it was a blueprint for the "Agentic Enterprise." The industry is no longer interested in isolated AI tools; it is moving toward a unified, multi-layered stack where infrastructure, data, security, and logic work as one autonomous system.

What I Saw: The Marathon Blueprint
The most impactful part was the Marathon Simulation demo. It moved past the "Hello World" phase of AI and showed real, high-stakes engineering. It taught me that reliable agents need separation of concerns:

The Planner: Defines the strategy (the "Thinker").

The Evaluator: Judges the plan against real-world constraints (the "Judge").

The Simulator: Runs thousands of iterations to test for failure (the "Worker").

This architecture is the new gold standard for my own projects. I am already planning to apply this "Plan-Evaluate-Simulate" pattern to my previous work like DriveLegal and EcoDrop to make them truly autonomous rather than just reactive.

What I Did: Putting the Stack to Work
I didn't just watch the keynote—I dove into the open-source repository provided by the Google Cloud team. Getting the marathon simulation environment running locally was my biggest "level-up" moment this week.

My Challenge: I hit a few roadblocks configuring the EventCompactionConfig for the simulator, but using the Gemini Cloud Assist features within my IDE, I was able to perform a natural-language investigation, find the root cause, and apply a fix.

My Takeaway: Seeing how the Wiz + Gemini integration works firsthand—specifically how the "Green Agent" suggests fixes for security risks—changed my mindset. Security isn't an "add-on" anymore; it’s part of the Secure-by-Design loop that every student developer needs to master right now.

Why This Matters for Us (The "Next-Gen" Builder)
The tools announced—the Agent Development Kit (ADK) for logic, Model Context Protocol **(MCP) for universal connectivity, and the **Knowledge Catalog for grounding AI in our real-world data—are the new foundations of our field.

For students like me, these tools solve the biggest problem:** context fragmentation**. We aren't just writing scripts; we’re learning to manage agent identity and observability. We’re learning how to build production-grade systems, not just academic experiments.

DEV Community: Mukesh Kumar

Ghost in the Codebase: Bringing Forgotten Code Back to Life with Open-Source AI | Hacktoberfest 2026

Ghost in the Codebase: Bringing Forgotten Code Back to Life

What I Built

Demo

How I Built It

Why Does Open Innovation Matter?

My Agent Session

The Spirit of It

Challenge Details

The "Agentic Era": A Student’s Perspective