SlothSort 🦥 Zero-Effort Local File Tidier Powered by Open-Source AI

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

October 20, is International Sloth Day. Sloths have life figured out: move at your own pace, conserve your energy, and avoid unnecessary effort.

Yet every developer and knowledge worker's laptop is plagued by the same chaotic mess: a Downloads or Desktop folder jammed with files like:

{% embed https://youtu.be/gHe1qCK4zpg %}

{% embed https://github.com/diwakarsingh07/SlothSort-hacktoberfest-challenge-3.git %}

SlothSort is designed around open-source AI and local inference:

At the core of SlothSort is the ability to run inference locally via Ollama using compact, open-weight models such as Llama 3.2 (3B), Qwen 2.5 (3B / 7B), or Mistral-7B. These models are fast, run smoothly on consumer laptop hardware (CPUs or modest GPUs), and offer structured JSON mode output to enforce consistent schemas: json { "new_name": "Invoice_2026-10_ACME_Cloud_Services.txt", "target_folder": "Financial_and_Receipts", "reasoning": "Identified recurring cloud compute invoice details and total amount due.", "confidence": 0.95 }

To guarantee maximum resilience, SlothSort implements an intelligent cascade:

Because laziness shouldn't mean losing files, every move operation creates a transaction record in.slothsort_history.json. If a user ever wants their clutter back, a single click restores every file to its original name and path.

Open innovation is what makes SlothSort practical and trustworthy:

Manually reviewing and organizing a cluttered 50-file downloads folder typically takes 20 to 30 minutes of tedious clicking, opening, renaming, and dragging.

Channel your inner sloth, save your energy, and let open-source AI do the chores! 🦥