Clutter Sloth: The Lazy, Local-First Downloads Sorter for International Sloth Day 🦥

A developer built Clutter Sloth, an open-source, local-first Downloads folder organizer that classifies files using open-weight vision models such as Qwen3.5-4B-GGUF and Gemma 3 served locally through Unsloth Desktop, keeping all data on the user's machine. The tool waits a settle period before evaluating files, never deletes anything, keeps sensitive categories like ID and Personal preview-only, and uses a trust ladder that requires five consecutive approvals before auto-moving files. A sample digest reports 43 files sorted over seven days with a 6.6% correction rate and roughly six-minute scans on a 4 GB VRAM GTX 1650.

This project is submitted to the Hacktoberfest Open-Source AI Challenge: Week 3 "Slow and Steady" in celebration of International Sloth Day October 20 . We all have it: a Downloads folder overflowing with cryptic filenames like statement final 3 .pdf , Screenshot 2026-10-18 1423.png , and IMG 9210.jpeg . We intend to organize them, but real life gets in the way. Aggressive cleanup scripts are terrifying—they rename things unpredictably or dump sensitive identity documents into public clouds. October 20 is International Sloth Day , and sloths have life figured out: conserve energy, move deliberately, and never panic. Meet Clutter Sloth : a slow, careful, local-only Downloads folder organizer powered by an open-weight vision model running on your own machine. It files things away quietly while your computer is idle, never deletes anything, learns from your corrections, and makes every move reversible. Sloths do not rush. Clutter Sloth is built around deliberate restraint: settle seconds default: 2 minutes before they are evaluated. never auto ID & Personal are permanently preview-only. sloth undo reverts the last batch with zero risk of overwriting restored files. sloth digest Running sloth digest provides genuine metrics of time saved: Clutter Sloth digest, last 7 days Files sorted: 43 28 auto-moved, 14 approved, 1 corrected Moves undid: 0 Correction rate on files reviewed: 6.6% Average scan duration: ~6 minutes on a GTX 1650 4 GB VRAM Instead of spending 30 minutes manually renaming bank receipts, invoices, and research papers, Clutter Sloth handles it quietly in the background. Your Downloads folder holds your most private life: boarding passes, tax returns, resumes, and medical bills. Sending these files to proprietary cloud APIs is an unnecessary privacy hazard. With open-weight models like Qwen3.5-4B-GGUF or Gemma 3 served locally via Unsloth Desktop , complete visual and document understanding runs on modest hardware even a 4 GB VRAM GPU with zero bytes leaving your machine . php flowchart TD A "Downloads Folder" -- |"Settle Delay and CPU < 60%"| B "sloth.engine Candidates " B -- C "sloth.extract PIL / pypdf / pypdfium2 " C -- D "sloth.classify" D <-- |"Dynamic In-Context Corrections"| E "sloth.db SQLite " D <-- |"Local OpenAI API"| F "Unsloth Desktop Open-Weight Model " D -- G{"Trust Ladder"} G -- |"never auto OR Streak < 5"| H "Queue in Proposals Preview Mode " G -- |"Streak = 5"| I "Autonomous Move to Sorted/Category/" H -- J "sloth review Interactive Verification " J -- |"Approve"| K "Category Streak Incremented" J -- |"Correct"| L "Streak Reset + Exemplar Stored" I -- M "sloth undo Batch Reversal " M -- |"Reversion"| N "Restored Safely without Overwrite" 1. Install Clutter Sloth git clone https://github.com/i-soumya18/clutter-sloth.git cd clutter-sloth pip install -e . 2. Initialize configuration sloth init 3. Verify connection to local Unsloth Desktop sloth doctor 4. Run an initial scan sloth scan --now sloth review