SlothSort AI: Let Open-Source AI Handle Your Digital Clutter

Slow down. Sort smarter. Keep your originals safe. Ever opened your Downloads folder and found yourself staring at hundreds of files, promising you'll organize them someday? I decided to build something to make that task easier. Meet SlothSort AI, a local-first file organization assistant built for the Hacktoberfest Open-Source AI Challenge: Slow and Steady. 💡 What Is SlothSort AI? SlothSort AI helps users declutter their digital workspace without manually sorting every file. It combines a dark-themed dashboard, automatic file categorization, organization statistics, and AI-generated suggestions in one simple application. ✨ What Can It Do? 📂 Scan a selected folder and categorize files by extension. 📊 Display file counts and category statistics. 🧠 Generate organization advice using a local AI model. 👀 Preview and adjust categories before organizing. 🛡️ Copy files into category folders while preserving originals. 🌙 Provide a clean, forest-inspired dark interface. The idea is simple: spend less time managing digital clutter and more time doing what matters. 🧠 The Open-Source AI Behind the Project SlothSort AI uses Qwen 2.5 3B through Ollama for local inference. The application is built with Python and Streamlit, with Requests connecting the interface to the local Ollama API. Instead of depending on a hosted AI API for every request, the application can use a model running on the user's own computer. 🌱 Why Open Innovation Matters For this project, openness isn't just a technical choice. It changes how the tool can be used and improved. More control over personal data File organization can involve sensitive filenames. Using a local model allows AI suggestions to be generated without sending filenames to a cloud AI API. The current application is designed around local inference. Less dependence on paid AI services After installation, local inference does not require a recurring cloud AI API charge. Users still need suitable hardware and storage, and electricity and other costs may apply. Freedom to experiment An open-weight model gives developers the opportunity to experiment with model configurations and compatible alternatives. SlothSort AI can evolve as its model integration improves. More accessible experimentation The project uses familiar Python tools and a small language model, making it a practical starting point for learning about local AI applications. Offline potential Once the model and required dependencies are installed, core AI inference can run without internet access, as long as the local services are available. That's the benefit of building with open AI: you can run it, learn from it, adapt it, and improve it. 🛡️ Safety Comes First Automation should reduce effort without creating unnecessary risk. SlothSort AI previews its proposed organization before asking for confirmation. It copies files into category folders rather than moving or deleting the originals, and it avoids overwriting existing destination files by generating alternative filenames. Its current categorization is extension-based, while the AI provides additional advice based on filenames. It does not claim to understand the actual contents of every file. 🛠️ Built With Python Streamlit Ollama Qwen 2.5 3B Requests 🚀 What's Next? I want to explore custom categories, better duplicate detection, copy-history tracking, undo support, and additional local models. The goal is to keep making digital organization simpler without sacrificing user control. 💚 Final Thoughts SlothSort AI began with a familiar problem: file clutter and the never-ending promise to organize it later. By combining local AI with a safety-first workflow, I wanted to create a tool that rewards doing less manually while giving users more control over their digital workspace. Open innovation makes that possible by giving developers the freedom to build beyond hosted services and create tools that people can adapt to their needs. Sometimes the smartest way to get organized is to move at a sloth's pace. 🦥 Project repository: https://github.com/vanshikamishra1909-cmd/SlothSortAI Built for the Hacktoberfest Open-Source AI Challenge — Slow and Steady.