FileNest AI | AI-Powered File Organizer & Storage Analyzer

FileNest AI 🌿: A Local AI-Powered File Organizer | Hacktoberfest Week 3

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Meet FileNest AI — an AI-powered file organization and storage analysis application designed to make managing files on your computer simpler and smarter.

As developers and everyday computer users, we accumulate files, duplicate documents, large downloads, and cluttered folders. Finding what is taking up storage or deciding how to organize everything can become a repetitive task.

FileNest AI brings file analysis, duplicate detection, and local AI assistance into one application.

✨ Key Features

• 📁 File and folder scanning
• 💾 Storage analysis and folder-size insights
• 🔍 Large-file detection
• ♻️ Exact duplicate detection using SHA-256 hashing
• 🤖 AI-powered file organization suggestions
• 🗂️ File management operations with user approval
• 🛡️ Protected files to help prevent accidental modifications
• ↩️ Activity history and undo support
• 📊 A dashboard for exploring file and storage insights

The goal is to reduce repetitive file-management work while keeping users in control of the actions performed on their files.

Demo

🎥 Watch the FileNest AI demo:

▶️ FileNest AI — Project Demo on YouTube

The demo showcases the application interface, its file-management workflow, and its AI-powered capabilities.

Code

💻 GitHub Repository:

https://github.com/SamruddhiPatil0078/FileNestAI

The project is open source, and I welcome feedback, suggestions, and contributions!

How I Built It

I built FileNest AI using a modern web stack combined with locally running AI.

Technology stack:

• React + Vite: Responsive frontend and interactive dashboard
• Tailwind CSS: UI styling
• Python + FastAPI: Backend APIs and file-management logic
• Ollama: Local inference runtime
• Gemma 3 1B: Open-weight language model for AI-powered assistance
• SHA-256: Exact duplicate file identification

🧠 Why Gemma 3 1B?

I chose Gemma 3 1B because I wanted to experiment with running an AI model locally rather than depending entirely on a hosted AI API.

The model provides a foundation for AI-assisted file organization while the application handles file scanning, storage analysis, duplicate detection, and file operations.

The project follows a local-first approach, with scanning restricted to a configured directory during development. File operations follow the application's access rules and user-approval workflow.

Building this project has also taught me that integrating an AI model is only one part of creating a useful AI application. The surrounding software architecture, reliable file handling, user experience, and safeguards matter just as much.

Why Does Open Innovation Matter?

Open innovation made it possible to experiment with an open-weight language model and build an AI-powered application without making a hosted, proprietary AI API the core of the experience.

Using Ollama and Gemma 3 1B gave me the opportunity to explore local inference, experiment with model integration, and learn how AI can work alongside conventional backend logic.

It also makes the project easier to explore and extend. Other developers can inspect the code, suggest improvements, experiment with different models, and contribute ideas for smarter file management.

For me, open source is not just about using existing tools. It is about learning from them, building something useful, and making that work available for others to explore.

My Agent Session

I used AI-assisted development while building and refining FileNest AI, particularly when working through integration, debugging, and feature improvements.

The source code and project demo are linked above. I haven't included a DevRelay session link because I don't have a public session URL to share.

The Lazy Payoff 🌿

Imagine spending less time manually searching folders, checking which files consume the most storage, and comparing duplicate files.

FileNest AI brings these tasks into a single workflow and uses local AI to assist with organization decisions.

Instead of making users do all the repetitive analysis themselves, the application helps surface useful information and supports user-approved file-management actions.

The goal: less time managing files, more time building things. 🚀

What's Next?

I'm interested in continuing to improve FileNest AI with better organization workflows, more useful storage insights, and a smoother user experience.

Feedback and contributions are welcome!

If you're interested in AI, local LLMs, Python, React, or open-source development, feel free to check out the repository and share your thoughts.

Thanks for checking out FileNest AI! 🌱