# LazySort AI: Let Local AI Handle the Folder Cleanup You Keep Putting Off

My Downloads folder has a talent for becoming a digital storage room. PDFs, screenshots, source files, archives and files named final_v2_revised_reallyfinal all end up living together.

The solution sounds simple: organize everything.

Then the folder stays messy for another three weeks.

For Week 3 of the Hacktoberfest Open-Source AI Challenge, themed Slow and Steady, I built LazySort AI: a local-first file organizer that uses open-source AI to suggest categories and cleaner filenames before moving anything.

The goal was simple: automate the tedious part without letting automation take over.

What does LazySort AI do?

LazySort AI lets you select a folder and scan its files. It suggests a category, such as Documents, Images, Videos, Audio, Archives or Code, and can propose a cleaner filename.

You see the suggestions in a preview. You can edit them, deselect files, and approve the changes yourself.

Nothing moves during the scan.

The application only moves selected files after explicit confirmation, and it is designed not to delete files or overwrite existing destination files.

Why use local AI?

A small organizing task does not need to become another cloud integration.

LazySort AI can use Gemma 3 through Ollama, running on the user's own computer. That means the model inference can happen locally instead of requiring a hosted AI API.

This matters when filenames or folder structures reveal personal information. A project folder, a document title or even a badly named screenshot can tell a surprisingly detailed story about its owner.

Keeping inference local reduces the need to send that information to an external AI provider. LazySort AI uses filenames and file metadata for its suggestions rather than uploading file contents for analysis.

Local inference is not a universal privacy guarantee. The application, model runtime and operating system still need to be configured and maintained responsibly.

Open-source AI should be practical

Open innovation is not only about building another chatbot.

It also means making AI available for small, everyday tasks that people can inspect, adapt and run themselves. Open models and local tools give developers room to experiment without requiring a paid API account for every prototype.

For this project, the useful part of AI is not that it can make an impressive paragraph. It is that it can reduce a repetitive task while leaving the user in charge.

The application also has a rule-based fallback. If Ollama is unavailable, file extensions can still provide basic category suggestions. The project should remain useful even when the model is not.

Safety before automation

File management is one of those areas where an incorrect automated decision can turn a small inconvenience into a terrible afternoon.

That is why LazySort AI uses a preview-and-approval workflow:

1. Scan the chosen folder.
2. Generate category and filename suggestions.
3. Review and edit the proposed changes.
4. Select the files to organize.
5. Confirm before applying the changes.

The intended workflow is non-recursive, avoids overwriting existing destination files, and does not delete files.

The suggestions are still suggestions. Users should review them and keep backups of important data.

Built with

• Python and Flask for the local web application
• HTML, CSS and JavaScript for the interface
• Ollama for running a model locally
• Gemma 3 (gemma3:4b) for AI-powered suggestions

The application is designed to run on a personal computer and does not require a hosted AI API key.

What I learned

The interesting challenge was not simply connecting an AI model to an application. It was deciding how much control the model should have.

For a file organizer, fully automatic action is not necessarily a better user experience. A fast mistake can be worse than a slow manual process.

A preview, editable suggestions and explicit approval make the tool more predictable. The rule-based fallback also helps avoid making the entire application dependent on the availability of a model.

Try it

GitHub repository: https://github.com/Yadnu14/LazySort-AI

The repository contains the application source, setup instructions, tests and MIT license.

LazySort AI is a small experiment in a simple idea: AI should handle tedious work without making users surrender control over their own files.

Sometimes the best automation is the kind that gives you your time back and then gets out of the way.

---