SlothSort: The Lazy, Fully Local AI Daemon That Tidies Your Desktop While You Sleep

What I Built Most developers have a ~/Downloads or ~/Desktop directory that resembles a digital landfill: cryptic file names like statement_final(1).pdf, unreadable screenshots, random code snippets, and invoices lost in the abyss. Sorting through digital clutter manually requires cognitive bandwidth that nobody wants to spend after a long day of coding.

In honor of International Sloth Day and the "Slow and Steady" theme, I built SlothSort — a background daemon that rewards total digital laziness.

SlothSort watches specified directories, reads the actual contents of cluttered documents and screenshots, deduces what they are, automatically gives them semantic names, and safely organizes them into clean folder hierarchies (e.g., Documents/Finance/2026/October/).

It takes zero clicks, zero manual sorting, and requires zero effort from the user. You let your machine idle, make a coffee, and let the background agent tidy up.

Demo Repository & Docs: https://github.com/sujalzombie01/signi-board

Here is a quick look at SlothSort running quietly in the terminal:

The background daemon with configurable debounce and throttling controls.

Extraction pipelines for PDFs, text files, and images.

Strict Pydantic JSON schemas that enforce clean file names and target paths.

A local SQLite ledger to inspect, rollback, or undo any file move with one command.

How I Built It SlothSort is designed entirely around open-source AI and local runtime tools:

Llama-3.2-3B-Instruct (Q4_K_M quantized): Handles content classification, metadata extraction, and path resolution via strict structured JSON output.

Moondream2 / Llama-3.2-11B-Vision: Parses screenshots, diagrams, and image receipts when text extraction yields no raw text.

Powered by Ollama and llama.cpp, leveraging Apple Silicon Metal / CUDA acceleration without making a single outbound network call.

Built with Python using watchdog to monitor OS file-system hooks.

Document extraction handled via pypdf and pytesseract.

Structured output validation enforced with pydantic.

[ Messy Folder: ~/Downloads ] │ ▼ (watchdog event) [ Content Extractor ] ──▶ (PDF / OCR / Text) │ ▼ [ Local LLM via Ollama ] ──▶ (Llama-3.2-3B: Classify & Formulate Path) │ ▼ [ File Engine ] ──▶ Rename, Move to ~/Organized, Append to local history Why Does Open Innovation Matter? Absolute Privacy for Sensitive Personal Data: The files we put off sorting are almost always our most private: tax forms, salary stubs, medical receipts, and lease agreements. Feeding those files to closed third-party cloud APIs requires trusting a remote server with sensitive unencrypted data. Running local, open weights guarantees an air-gapped workflow where zero bytes leave the machine.

Zero Marginal Cost for Passive Background Tasks: Paying per-token charges to a closed cloud provider makes you hesitate to clean up large backlogs or leave a daemon idling. Because open-weight models run on local compute, running SlothSort on thousands of archived files costs $0.00.

Portability and Modularity: With open-weight standards (GGUF, Ollama formats), users aren't locked into a specific vendor. If a lighter 1B model or a domain-specific model is released next month, swapping the core engine requires changing a single environment variable: SLOTHSORT_MODEL=qwen2.5:3b.

The Lazy Payoff Before SlothSort, my ~/Downloads directory had accumulated over 380 unfiled documents, screenshots, and receipts dating back several months.

Time saved: Approximately 2.5 hours of manual folder browsing, previewing, renaming, and filing.

Lazy metric: Cleaned 384 files with exactly 1 terminal command and zero follow-up maintenance.

System overhead: Because it runs low-priority local quantization in bursts, CPU usage remained under 12% on idle cycles and never spiked fan noise.