SlothSort 🦥: Your AI Organizes the Mess. You Stay in Control.

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

What if your AI could organize your files without requiring you to hand your files over to a cloud AI service?

Meet SlothSort — a privacy-first Windows desktop application that uses local AI to propose file organization, while keeping the final decision in your hands.

Built for the Hacktoberfest Open-Source AI Challenge — Week 3: Slow and Steady.

Downloads pile up. Screenshots multiply. Documents disappear into folders named New Folder, New Folder (2), and final_final_REAL.

Manually sorting everything is tedious. Sending file information to a cloud AI service isn't always a trade-off users want to make.

I wanted to explore a different approach:

What if AI handled the tedious proposing, but never took control away from the user?

In this demo, you'll see the complete journey:

This isn't just a chatbot recommending folders. It's a desktop application that connects AI suggestions to actual file operations—with a deliberate human approval step.

SlothSort combines a modern desktop stack with a locally running open-weight model.

The inference workflow uses a local Ollama endpoint at 127.0.0.1:11434. After the model and runtime are installed, the core AI workflow does not require a cloud AI API.

AI can make mistakes. A plausible filename or category doesn't automatically mean a file should be moved.

SlothSort separates suggestion from execution:

The Electron renderer runs with contextIsolation enabled and nodeIntegration disabled. Privileged filesystem operations stay in the main process behind a narrow, typed IPC interface.

I also designed the workflow without a file-deletion operation. The application focuses on moving and renaming files, with the user remaining in control.

For a file organizer, privacy isn't an abstract feature. Files can contain personal documents, work material, screenshots, and information users simply don't want sent to an external AI service.

Using an open-weight model locally makes a different design possible.

With Ollama and Gemma installed, SlothSort can generate suggestions locally instead of depending on a hosted AI inference API.

The source code is available for developers to examine how the AI workflow, approval process, and filesystem operations are implemented.

Local inference is only one part of the design. Review, explicit approval, operation validation, and undo help prevent AI suggestions from becoming unchecked filesystem changes.

The combination of open-source tooling and an open-weight model gives other developers a foundation they can inspect, reproduce, adapt, and improve.

Open innovation matters here because it lets us explore not only what AI can automate, but also how to build that automation with more user control.

The goal isn't to make people stop caring about their files. It's to make organization require less repetitive decision-making.

Instead of choosing a destination and filename for every file from scratch, users can review AI-generated proposals and approve the changes they actually want.

I have put the project through automated testing and packaged Windows release validation. The repository also documents the architecture, setup instructions, testing evidence, and known limitations.

I won't claim a made-up percentage of time saved. A reliable time-saving figure would require a repeatable comparison against manual organization. For now, the measurable product goal is straightforward: reduce repetitive sorting work without removing the user's control over their files.

The following screenshots are tracked in the repository and come from the packaged Windows validation workflow.

Local AI suggestions for safer file organization. SlothSort is a Windows desktop application that scans a selected folder, proposes categories, filenames, and destinations through local Ollama inference, and waits for your approval before moving anything.

15-second overview: SlothSort turns a messy folder into a reviewable queue of file-organization proposals. The model runs through Ollama on the local machine, proposed moves are validated in the main process, and approved operations are recorded so supported moves can be undone.

Demo status: The concise 1080p demo recording is prepared locally at artifacts/day-2-audit/voiceover/SlothSort-v0.1.1-demo-video.mp4, but it is not currently a tracked repository or public release asset. The README therefore uses the tracked screenshots below instead of linking an untracked binary.

Sorting files is easy to postpone and difficult to do safely in bulk. A plain AI-generated…