Lazy Tidier: I Taught an Open-Weight Model to Clean My Downloads Folder

Description and use

Lazy Tidier is a Python CLI that organizes a Downloads folder using extension rules and an open-weight LLM for ambiguous files. It is safe, supports dry‑run, undo, and respects privacy.

Problem

The author’s Downloads folder was cluttered with mixed file types and ambiguous names, making manual cleanup tedious.

Outcome

On a 19-file test set, 11 files were processed by the LLM and all categories matched manual review; dry-run completed in ~8.6s.

Workflow

• Scan folder non-recursively
• Apply extension-based rules to obvious files
• Send ambiguous PDFs, TXT, MD, DOCX to open-weight LLM with filename and up to ~500 chars
• Validate LLM JSON response, sanitize names, enforce privacy guard
• Generate dry-run plan and display table
• If --apply, write undo log and move files into category subfolders
• If --undo, reverse last apply using undo log

Evidence

Lazy Tidier is a Python CLI that sorts a messy folder using simple rules first and an open-weight model only for ambiguous files.

Source

Most of the work needs no AI at all. Rules based on file extension sort the obvious files for free.

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Only the ambiguous ones (PDFs and text notes) go to an open-weight model.

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A normal run is a dry-run: it only prints what it would do.

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--undo reverses the last run, restoring the original names too.

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The privacy guard: files whose names contain any of 14 sensitive whole words are sorted locally and never sent to the model.

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On my 19-file test folder, 11 files went to the model and the rest were sorted locally. The whole dry-run took about 8.6 seconds in one run.

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