SlothSort：讓本地 AI 整理檔案

What I Built

Meet SlothSort — a local-first AI file organizer that takes the boring work out of managing a cluttered Downloads folder.

Instead of manually opening files, renaming them, and deciding where they belong, simply upload your documents and let SlothSort suggest meaningful filenames, categories, and summaries.

It supports documents, invoices, certificates, notes, spreadsheets, code files, and more.

The best part? You stay in control. Review the AI's suggestions, edit them if needed, and approve files before they move into your organized library. Made a mistake? Undo the move.

The goal is simple: spend less time organizing files and more time doing what matters.

Demo

Code

The project includes the Flask backend, a web dashboard, local AI integration, SQLite storage, and file organization with undo support.

How I Built It

I built SlothSort using:

• Python + Flask — application backend and web interface.
• Ollama — runs the language model locally.
• Qwen 2.5 3B — the default open-weight model for generating file organization suggestions.
• SQLite — stores file metadata and action history.
• HTML, CSS, and JavaScript — powers the interactive dashboard.
• Local file-processing libraries — extract text from supported documents before analysis.

When a file is uploaded, SlothSort extracts available text, sends the relevant content to the local model, and receives a suggested filename, category, and summary. The user can review the result before the file is moved.

If Ollama is unavailable, the application has a basic rule-based fallback so the workflow can still be tested.

Why Does Open Innovation Matter?

Files can contain personal information, financial details, academic certificates, and work documents. Uploading all of them to a third-party AI service is not always desirable.

That's why I wanted to explore a local-first approach.

With Ollama and an open-weight model, SlothSort can analyze files on the user's own machine instead of relying on a hosted AI API.

Open innovation makes three things possible:

• Privacy: File contents do not need to be sent to an external AI provider.
• Control: Users can change the model, adjust prompts, and customize the organization rules.
• Accessibility: After the initial setup and model download, the core workflow can run offline without per-request AI API charges.

There is still a cost in hardware and electricity, and local models can make mistakes. That's why SlothSort includes a review-before-moving workflow instead of blindly trusting every prediction.

For me, open-source AI is not just about using a different model. It's about giving people more control over where their data goes and how their tools work.

The Lazy Payoff 🦥

Think about the files that accumulate in your Downloads folder: invoices, certificates, notes, screenshots, and documents with names like final_v2_revised.pdf.

Each file takes only a few seconds to organize, but the repeated effort adds up.

SlothSort aims to reduce that repetitive work by generating suggestions automatically and letting users approve them in batches of small, manageable actions.

I haven't measured a reliable time-saving figure yet, so I won't claim a specific number. That's something I'd like to benchmark as the project develops.