This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Meet LazyDesk AI 🦥 — a simple, AI-powered file organizer designed to reduce the small, repetitive tasks that create digital clutter.
We all download files, save college notes, collect code snippets, and forget to organize them later. Eventually, finding one file becomes a task of its own.
LazyDesk AI helps by scanning a selected folder, suggesting a category for each file, and organizing files into category folders.
It supports categories such as:
- 📄 Documents
- 🖼️ Images
- 🎬 Videos
- 🎵 Audio
- 💻 Code
- 📊 Spreadsheets
- 📽️ Presentations
- 📦 Archives
- 📁 Other
The best part? The user stays in control. You can review and edit the suggested categories before confirming the organization. The app also avoids overwriting files when destination filenames already exist.
The goal isn't to automate everything blindly. It's to make an everyday task simpler while keeping the final decision with the user.
Demo
The project currently runs locally on my computer.
To try it, follow the setup instructions in the repository:
The repository README includes an application screenshot. A deployed web demo is not currently available because the application uses a locally running Ollama model.
🎥 Watch the LazyDesk AI Demo: View on YouTube
The demo shows LazyDesk AI scanning sample files, suggesting categories using a local AI model, and organizing files into folders after user confirmation.
Code
🔗 GitHub repository: https://github.com/dhinesh-babu-7/LazyDesk-AI
The project is built with Python and Streamlit, with Ollama providing local AI inference.
Explore the source code, try the project, and share ideas for improvement!
How I Built It
I built LazyDesk AI using a small, practical technology stack:
- Python — file handling and application logic.
- Streamlit — the interactive user interface.
- Ollama — local inference for the AI model.
- Qwen 2.5 3B — an open-weight language model used for file-category suggestions.
The workflow is straightforward:
- The user selects a folder.
- The application scans files directly inside that folder.
- The AI receives filenames and suggests categories.
- The user reviews and edits the suggestions.
- The user confirms the plan, and the application moves files into category folders.
I also added an extension-based fallback so that familiar file types can still be categorized if the local model is unavailable or returns an unexpected answer.
One important design decision was to keep file contents out of the AI prompt. The model receives filenames rather than the contents of documents, which helps limit what is shared with the inference process.
This is an early version, and there is still room to improve its classification accuracy, interface, and testing.
Why Does Open Innovation Matter?
Open innovation made this project possible with tools I could run and experiment with on my own computer.
Using an open-weight model through Ollama gave me a way to explore AI without building the application around a paid, hosted inference API.
It also helped me learn more about how AI fits into a real application: model responses need validation, fallback logic matters, and users should be able to review automated decisions.
For a student developer, being able to experiment locally, inspect the code, and share the project publicly makes learning more accessible.
Open innovation isn't just about using free tools. It's about having the freedom to learn, modify, experiment, and build on existing work.
The Lazy Payoff
LazyDesk AI focuses on reducing the effort involved in sorting files manually.
I haven't measured a reliable time saving yet, so I won't claim a specific number. The current version demonstrates the workflow, and the next step is to test it with a larger collection of files and compare the time taken against manual organization.
I'd also like to make it useful for students who regularly download lecture notes, assignments, datasets, and programming files.
The idea behind “Slow and Steady” is simple: start with one repetitive task, make it safer and easier, and improve it step by step.
Prize Categories
I'm entering the overall Hacktoberfest Open-Source AI Challenge: Week 3 competition. LazyDesk AI uses local inference with Ollama and the open-weight Qwen 2.5 3B model. I'm not claiming a partner-specific prize category.
LazyDesk AI explores local AI inference and practical automation using open-weight models.
I'll select the applicable prize categories from the official challenge page before submitting.
Built with Python, Streamlit, Ollama, and Qwen 2.5 3B.
Less file chaos. More time for what matters. 🦥
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