SlothSort: A Local AI Assistant for the Downloads Folder
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built SlothSort helps tidy a folder without the tedious drag-and-drop routine. It looks at filenames, asks a local AI model to suggest categories and clearer names, and presents the proposed moves for
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
SlothSort helps tidy a folder without the tedious drag-and-drop routine. It looks at filenames, asks a local AI model to suggest categories and clearer names, and presents the proposed moves for review. Files are only moved after the user confirms the plan.
The project currently runs locally. Install the dependencies and follow the Ollama setup in the README. There is no public demo yet.
The interface uses Streamlit, and the app asks a local Ollama server to run the open-weight qwen2.5:3b model. The model sees filenames only and returns a category plus a suggested name. The Python app validates that response, sanitizes names, checks for conflicts, and requires explicit approval before moving anything.
Filenames can contain personal or work-related details. Local inference keeps them on the user's machine rather than sending them to a cloud API. After the one-time model download, there is no per-file inference fee. The prompt, category list, and model choice are all open to inspection and change.
I do not have a shareable DevRelay session link for this project.
SlothSort turns a pile of filenames into a proposed folder structure in one step, while keeping the final decision with the user. Add a real time-saved estimate here after trying it on a folder and timing the manual alternative.