SlothSort: My Idea for a Lazy, AI-Powered File Organizer

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

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

My computer's Downloads folder can quickly become a mess of PDFs, screenshots, documents, installers, images, and files with confusing names. Finding a particular file often takes more time than it should.

That is why I want to build SlothSort — a local AI-powered desktop assistant that organizes files so you don't have to do it manually.

The idea is to let users scan a cluttered folder and receive suggestions for how to organize it. SlothSort would help with:

Smart categorization: Group files into categories such as documents, images, videos, code, and installers.

Suggested file names: Propose clearer names that make files easier to recognize.

Automatic folder planning: Recommend folders and locations based on file types and names.

Approval before changes: Show users a preview of the proposed changes and let them approve or reject them before anything is moved or renamed.

The goal is to reduce digital clutter without making users spend their time sorting hundreds of files themselves.

SlothSort is currently a project proposal. I plan to develop a working prototype and gradually add more intelligent organization features.

The code repository is not available yet. I plan to publish the project on GitHub as development progresses.

My plan is to combine Python file automation with a locally running open-weight language model through Ollama.

Python scans the folder and collects basic file information, such as file names, extensions, and sizes.

The system groups recognizable file types using simple rules and asks the local AI model to help classify ambiguous files or suggest more descriptive names when useful.

SlothSort presents a preview of the proposed folder structure, file names, and changes.

The user reviews and approves the changes before Python moves or renames anything.

I want to build the first version around a simple principle: the AI suggests, but the user stays in control.

Once the required software and model are installed, I want the core workflow to run locally without requiring a continuous internet connection.

File organization might seem like a small problem, but a folder can contain personal documents, screenshots, work files, and other information that users may not want to upload to an external service.

That is why I want local, open-weight AI to be central to SlothSort.

Privacy: File names and metadata can be processed locally, avoiding the need to send them to a third-party AI API.

Offline capability: Once installed, the core organization workflow could work without a continuous internet connection.

Flexibility: I can experiment with different open-weight models and choose one that performs well on the available hardware.

Lower ongoing costs: Running inference locally avoids per-request AI API charges, although the user still needs suitable hardware and must account for electricity and storage.

A closed AI API could provide similar classification features, but it could also introduce external-service dependencies and usage costs. With SlothSort, I want users to be able to run the assistant on their own machines and decide exactly which file changes to accept.

Open innovation would also let me learn from the implementation, modify the workflow, and adapt the tool to different users' needs.

There is no agent session yet because development has not started.

As I build the project, I plan to document the development process and share a session or walkthrough if it is useful.

The payoff I am aiming for is simple: a cleaner computer with less repetitive work.

Instead of manually creating folders, renaming files, and moving downloads one by one, users could review a proposed organization plan and approve the changes in a few steps.

I have not measured any time savings yet because SlothSort is still at the idea stage. After building the prototype, I want to test it on a sample folder and compare the time required for manual organization with the time required using SlothSort.

Safety is an important part of the idea, too. The tool should preview changes, avoid deleting files automatically, and give users control over when changes happen.

My first milestone is a small, functional prototype that can scan a selected test folder, suggest categories, and safely organize files after user approval.

From there, I hope to introduce smarter file-name suggestions, customizable categories, and more advanced organization rules.

This is my idea for Slow and Steady: a small, practical use of open-source AI that handles a tedious task while leaving the final decisions in the user's hands.