What I Built
I built SlothSort AI, a local AI-powered file organizer that helps users organize messy folders without manually opening, understanding, renaming, and sorting every file.
SlothSort AI scans a selected folder and supports files such as PDF, DOCX, TXT, CSV, and XLSX. It reads the file contents and uses local AI to understand the documents and help classify them.
The goal is simple: let AI handle the boring file organization while you do less work. 🦥
Demo
🎥 Demo video: Coming soon
The application is currently being developed and tested locally using Streamlit and Ollama.
Code
SlothSort AI GitHub Repository
How I Built It
I built SlothSort AI using Python, Streamlit, and Ollama.
For local AI processing, I use the Llama 3.2 3B open-weight model through Ollama. The project uses different Python libraries to read different file formats:
Select Folder → Scan Files → Read Content → AI Classification → Suggest Organization → User Review → Organize Files The local AI approach allows the application to work with document content without requiring a cloud AI API for every file.
Why Does Open Innovation Matter?
Open innovation makes it possible to build an AI-powered file organizer using local, open-weight AI instead of depending completely on a closed AI API.
Documents can contain private information such as resumes, certificates, financial records, educational documents, and work files.
Using local AI provides more control over how that information is processed. It also allows developers to:
Open file → Read file → Understand it → Rename it → Create folder → Move it
SlothSort AI aims to reduce that process to:
Select folder → Scan → Let AI understand the files → Review → Organize → 🦥 Relax The project is designed around the idea that repetitive and boring file-management tasks should require as little human effort as possible.
The less time spent organizing files, the more time there is to focus on useful work—or simply do nothing. 😴🦥