SlothSort: sort your mess while you sleep with a 3B model on a 4.5 W laptop

A developer known as Mystical89 released slothsort, an MIT-licensed Python CLI that sorts messy folders by combining extension-based rules with a local 3B open-weight model served through Ollama to rename files whose names reveal nothing about their contents. The tool processed a 14-file demo folder in 10.6 seconds with a warm model and logs every move so a single --undo command reverses a run. The design keeps the model out of the decision to move a file, letting it only propose names, after an early version that batched eight classifications into one JSON array returned three results and mismatched names between files.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady https://dev.to/challenges/hacktoberfest-week3-2026-10-19 slothsort — a CLI that sorts your mess while you sleep. Rules move files into type folders. A 3B open-weight model, running locally through Ollama, does the part rules can't: it reads the documents whose names tell you nothing and gives them names a human would recognize. The problem is a specific kind of procrastination. scan 001.pdf . report final v2 1 .docx . Screenshot from 2026-10-18 14-22-31.png . You know you should sort them. You also know that sorting them means opening each one to find out what it is — which is exactly the twenty minutes you never have. So the folder grows. slothsort flips the order: you run it before bed, and the machine does the opening. In the morning the scan is acme-energy-bill.pdf , the report is q3-2026-report.docx , and everything sits in a folder that makes sense. Who gets to be a little lazier: anyone with a Downloads folder. Which is everyone. bash $ python3 slothsort.py demo/messy slothsort · /home/mystical/projects/slothsort/demo/messy 14 files, 4.0 KB Documents/ contract 2026.pdf rules notes.md rules receipt 2026-10-03.txt rules report final v2 1 .docx → q3-2026-report.docx model scan 001.pdf → acme-energy-bill.pdf model Spreadsheets/ data.csv rules Images/ IMG 2041.jpg rules photo 2026-09-30.jpg rules Screenshot from 2026-10-18 14-22-31.png → screenshot-from-2026-10-18-14-22-31.png tidy Audio/ song.mp3 rules Archives/ archive old.tar.gz rules backup.zip rules Installers/ Qoder-linux-amd64.deb rules Code/ main.py rules Plan: 14 files → 7 folders, 3 renames. Dry run — nothing moved. Use --apply. --apply moved all 14 files in 10.6 s with a warm model. Then: bash $ python3 slothsort.py demo/messy --undo Undone 14 moves from run 20261020-213040. The demo folder is generated by demo/make demo.py — an invoice PDF, a contract, a docx report, screenshots, archives, a .deb — so you can reproduce the whole thing in one command. github.com/Mystical89/slothsort https://github.com/Mystical89/slothsort — MIT, Python 3.10+, no dependencies, 22 tests. php slothsort.py the CLI: scan - rules - model renames - move + undo log test slothsort.py 22 tests, no network and no model required demo/make demo.py builds the messy demo folder The split of labor. Type folders are a solved problem: an extension is not a guess, so rules do it, instantly and offline. The model is reserved for the one job that actually needs reading. That split is also a safety feature — the model never decides whether a file moves, only what it is called. The failure that shaped the design. My first version asked the model to classify eight files at once, in a JSON array with ids. On a 3B model it returned three of eight, then mixed the names between files — the invoice got the receipt's name, the receipt got the scan's. The fix was to shrink the task until it fit the model: one file, one short answer, no ids to mismatch. The prompt now asks for a kebab-case name only; the tool sanitizes it, keeps the extension, and rejects anything that looks like prose. Reading files without dependencies. pdftotext for PDFs optional , zipfile + XML for .docx / .pptx / .xlsx , plain reads for text. The first ~200 characters are enough for the model to know what a document is. Safety. Dry run by default. Nothing is ever deleted. Name collisions become file 2 .pdf . Every run writes a JSON log to .slothsort/runs/ , and --undo reverses the last one. If the model endpoint is down, the tool falls back to rules and still works. The hardware. This runs on an Intel Core m5-6Y57 — two cores, 7.2 GB of RAM, no usable GPU. A cold run takes about a minute because the 3B model has to load; a warm run takes ten seconds. That is the "slow" in Slow and Steady, and it is the right trade for a nightly job. Three reasons, and the first one is why the tool exists at all. The model reads your documents. That is the product. Sending scans of invoices and contracts to a hosted API is a non-starter for something that is supposed to touch everything in your Downloads folder. With a local endpoint the files never leave the laptop. Privacy here isn't a feature bullet — it's the precondition. Zero cost per run. A 3B model on a 4.5 W laptop costs electricity. A hosted API costs money per document, and the tool only makes sense if you can point it at every folder without thinking about the bill. The model is a flag, not a decision. --model qwen2.5:7b when the hardware improves; --base-url for LM Studio, llama.cpp or vLLM. No code changes, no vendor, no lock-in. And it works offline — on a train, on a plane, or in a country where the API is blocked. Next: a systemd user timer so it runs at 3 a.m. on its own, and a --review mode that asks before each rename. Slow and steady.