QuietSort: Let AI Clean Up Your Messy Downloads Folder 🦥
What I Built
I built QuietSort, a local-first AI assistant that organizes messy folders without making me sort every file manually.
My Downloads folder used to be a graveyard of random PDFs, screenshots, ZIP files, documents, and mysteriously named files. QuietSort scans a folder, understands what each file is likely to be, and suggests a cleaner filename and destination.
It can group files into categories like Documents, Study Materials, Images, Code, and Archives. Before anything moves, I get a preview of the proposed changes and can approve or reject them.
The idea is simple: let AI handle the boring decisions while keeping the human in control.
Less dragging and dropping. Less decision fatigue. More time for things that actually matter.
Demo
[Insert your deployed demo link or a short video showing QuietSort organizing a test folder.]
The demo should show the app scanning a messy folder, generating suggestions, previewing the changes, and organizing the files after approval.
Code
[Insert your actual GitHub repository link.]
The project is intended to be open source, so anyone can inspect the implementation, experiment with the model, customize the categories, and improve the workflow.
How I Built It
QuietSort is designed around local inference with an open-weight AI model, rather than sending file information to a hosted AI API.
The proposed stack is:
- Python and FastAPI for the backend.
- Gemma through Ollama for local AI-powered file classification and naming suggestions.
- HTML, CSS, and JavaScript for a simple interface.
- SQLite or a local JSON log to track approved actions and support undo functionality.
The workflow is straightforward:
- The user selects a folder to organize.
- The application scans the files and gathers relevant metadata.
- The model suggests a category, filename, destination, and short explanation.
- The user reviews the proposed changes.
- The application moves approved files and records the actions.
I designed the workflow to prioritize safety: files should never be moved or renamed without user approval, and the application should maintain an action log to help reverse changes.
Why Does Open Innovation Matter?
For this project, privacy is not an optional feature. It is part of the design.
File names and documents can reveal personal information, academic work, financial details, and private projects. A local-first approach lets users keep that information on their own machine instead of automatically sending it to an external AI service.
Open-weight models also give developers more control. I can experiment with different models, adjust prompts, inspect the surrounding code, and customize how the assistant makes decisions.
Once the model has been downloaded and the local runtime is configured, the core workflow can operate without an internet connection.
For me, open innovation means being able to build a useful tool without giving up control over the data, the model, or the way the application works.
My Agent Session
[Insert your actual DevRelay agent-session link, if available.]
The session can help show how the project evolved, what implementation decisions were made, and how the workflow was tested.
The Lazy Payoff
The goal is to turn a repetitive folder-cleanup task into a quick review-and-approve workflow.
Instead of manually opening files, deciding where each belongs, renaming it, and moving it, users can review AI-generated suggestions in batches.
I plan to measure the time required to organize the same set of files manually versus using QuietSort. Once I have tested it, I’ll report the actual results here rather than guessing at the time saved.
The real payoff is not avoiding work entirely. It is spending less attention on small, repetitive tasks.
Prize Categories
- Best Use of Gemma — only if Gemma is genuinely integrated into the final working project.
Top comments (0)