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

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

What I Built

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:

• Screenshot 2026-10-21 at 2.45.19 PM.png
• INV_98242_oct_final_v2.txt
• my_updated_resume_draft_FINAL(1).txt
• coffee_shop_bill_scan.txt
• `temp_fix_database_conn.py

Demo

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

Code

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

How I Built It

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

1. Open-Weight Models & 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
}


2. Multi-Provider Fallback Cascade

To guarantee maximum resilience, SlothSort implements an intelligent cascade:

• Primary: Local Ollama service (runs 100% locally with open weights).
• Secondary: Google Cloud AI / Gemini (gemini-1.5-flash) for ultra-fast cloud multimodal processing.
• Tertiary Backup: Groq (llama-3.3-70b-versatile) for sub-second open model cloud inference.
• Offline Safety Net: A built-in heuristic classifier that guarantees zero-latency execution even when offline with no models running.

3. Non-Destructive File Transactions

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.

Why Does Open Innovation Matter?

Open innovation is what makes SlothSort practical and trustworthy:

1. Absolute Privacy for Sensitive Documents: Your Downloads and Desktop folders hold your most sensitive personal data: tax forms, salary slips, medical receipts, confidential work contracts, and personal photos. Sending every single file to closed commercial APIs creates severe privacy and compliance risks. With open-weight models running on Ollama, your data never leaves your machine.
2. Zero Cost Forever: A background watcher scanning hundreds of messy files every week would quickly rack up token fees on proprietary cloud APIs. Open-source models run locally on your laptop for $0.00.
3. Offline Autonomy: Whether you are on a long flight without Wi-Fi or working in a remote cabin, open-source AI runs entirely offline.
4. Model Freedom: You aren't locked into one vendor. You can swap between Llama 3.2, Qwen 2.5, Gemma 2, or fine-tuned local models with a single setting.

The Lazy Payoff

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

With SlothSort:

• Time spent: 3 seconds (one click on "Tidy Up Now").
• Energy expended: Zero.
• Result: Receipts filed under Financial_and_Receipts, screenshots timestamped under Screenshots, resumes saved under Career_and_Resume, and code filed under Developer_Code.

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

Prize Categories

BEST USE OF GEMMA MODAL.