This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
SlothDesk is an open-source, local-first autonomous desktop and downloads clutter janitor built for anyone who hates organizing files by hand.
Every single week, our Desktop and Downloads folders become digital landfills of:
Screenshot 2026-10-24 at 11.23.41 PM.png`` statement (2).pdf``Uber_trip_receipt_e83912.pdf`` customers_dump_2026_raw.csv``docker-compose.staging.yml`` node-v22.1.0-x64.msi
Manually inspecting, renaming, and filing hundreds of files is the ultimate procrastination trap.
Instead of making you drag, click, and rename files:
(1) with extracted vendor names, dates, and semantic slugs.
The Open-Source Local AI Janitor for Chaotic Desktops and Downloads.
Channel your inner sloth. Open-source local inference sorts, renames, and files your digital clutter so you don't have to.
Every developer and knowledge worker's Desktop and Downloads folders devolve into a chaotic digital landfill:
Manually organizing hundreds of files is the ultimate procrastination chore.
SlothDesk automates this entirely on your machine. Using open-source natural language processing and semantic vector classification (scikit-learn TF-IDF semantic vector anchors + heuristic entity and date extractors), SlothDesk automatically:
`chase_bank_statement_2026-10.pdf`, `uber_receipt_2026-10-24.pdf`, `aws_screenshot_2026-10-24.png`).
**Repository**: [https://github.com/titas-mahato/slothdesk](https://github.com/titas-mahato/slothdesk)
The repository is structured with a modular local inference architecture:
app/classifier.py (scikit-learn TF-IDF semantic vector anchors + local keyword extractor)app/renamer.py (Entity, date, and vendor extraction engine)app/janitor.py (Virtual desktop simulation + safe real filesystem operations with conflict resolution)app/daemon.py (Watchdog-powered background daemon with patient file settling)main.py & templates/index.html (FastAPI + responsive dark-canopy editorial dashboard)scikit-learn TF-IDF vectorization mapped against domain anchor spaces). It computes cosine similarity distances across document categories in under 15 milliseconds per file with $< 200\text{MB}$ RAM overhead.YYYY-MM-DD, Month YYYY), recognized tech/service vendors ( Chase, Uber, AWS, Stripe, GitHub), and file roles to synthesize clean, normalized filenames. watchdog library. When a new file is detected in Downloads, the daemon waits patiently for a quiet window to ensure the browser has finished writing .crdownload or temporary files before taking action.Desktop folders contain your most confidential documents: bank statements, tax returns (W-2/1099), medical receipts, payslips, and private source code. Handing these files over to third-party commercial LLM clouds creates unacceptable privacy risks. Open-source local AI ensures your private files never touch a server you don't control.
During testing this week, SlothDesk was set loose on a real Downloads folder containing 42 untidy files accumulated over three weeks: