SlothSort AI: Let Gemma Clean Your Messy Files So You Don't Have To

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

What I Built

SlothSort AI is a local-first AI file organizer that turns the repetitive job of cleaning up a messy folder into a simple review-and-apply workflow.

Instead of manually sorting files one by one, SlothSort lets local Gemma generate an organization plan that the user can review before anything changes.

The workflow is:

1. Select a folder.
2. Scan the visible files.
3. Let local Gemma generate an organization plan.
4. Review the proposed changes.
5. Apply the organization only after confirmation.

SlothSort does not read file contents. It uses metadata such as filenames, extensions, file sizes, and modification times to create the organization plan.

Demo

Watch the full SlothSort AI demo

The demo shows the complete workflow from creating a fake folder and scanning its files to generating, reviewing, and applying the AI organization plan.

Code

View the SlothSort AI source code on GitHub

How I Built It

SlothSort AI is built with:

• Python
• FastAPI
• Pydantic
• Vanilla HTML, CSS, and JavaScript
• Ollama
• Gemma 4 E4B

The architecture is intentionally lightweight:

User
  ↓
SlothSort UI
  ↓
FastAPI
  ↓
Ollama
  ↓
Gemma 4 E4B
  ↓
Structured JSON plan
  ↓
Pydantic validation
  ↓
Review
  ↓
User confirmation
  ↓
Filesystem changes

The backend scans the selected folder and collects metadata from visible files. That metadata is sent to Gemma through Ollama running locally.

Gemma returns a structured organization plan, which is validated with Pydantic before being presented to the user.

Nothing is changed during scanning or planning. Files are only moved after the user reviews the proposed changes and explicitly confirms the operation.

The backend also rechecks the scan before applying changes, keeps destinations inside the selected root folder, avoids intentional overwrites, and never deletes files.

Why Does Open Innovation Matter?

SlothSort uses Gemma locally through Ollama instead of depending on a proprietary hosted AI API.

This means the application can perform its AI planning locally without requiring a cloud AI account or API key. File contents are never read, and only file metadata required for organization is provided to the local model.

Open-weight models make this kind of local-first application possible while giving developers and users more control over the AI component and where their data is processed.

The Lazy Payoff

Organizing a messy folder is exactly the kind of repetitive task that is easy to keep postponing.

SlothSort turns:

Look at file → Decide → Rename → Move → Repeat

into:

Select folder → Scan → Review AI plan → Confirm

The goal is simple: let local AI handle the repetitive first pass while keeping the user in control of the final changes.

Safety

• File contents are never opened or sent to the model.
• Only file metadata is used for AI planning.
• Scanning does not modify files.
• AI-generated plans are validated before they can be applied.
• Applying changes requires explicit user confirmation.
• The backend rechecks files before applying a plan.
• Destination paths must remain inside the selected root folder.
• Existing destination names receive a numbered suffix.
• Files are never intentionally deleted.

Prize Categories

• Overall
• Best Use of Gemma

AI Disclosure

AI coding tools were used during implementation, debugging, and refinement of the project.

The project concept, product direction, feature decisions, testing, final review, and submission were human-led.

Team

Solo submission.