What I Built
SlothSort is a local-first AI file organization assistant that takes the boring work out of managing a cluttered Downloads folder.
Instead of manually opening files, figuring out what they contain, renaming them, and moving them into folders, SlothSort analyzes files and proposes meaningful names, categories, and destinations automatically.
It can group documents by topic and type, identify duplicate files, detect potential naming conflicts, and generate a cleanup plan for review before changes are applied. Its undo functionality makes it easy to reverse operations if something doesn't look right.
The goal is simple: spend less time managing files and more time doing the things that actually matter.
Whether you're a student buried under assignment PDFs, a developer drowning in downloads, or someone whose desktop has become a digital junk drawer, SlothSort helps you reclaim your time.
Demo
🎬 Demo video: [Add your demo video link]
🖥️ Live demo: [Add a deployed demo link, if available]
SlothSort is designed primarily as a local desktop application, so the most meaningful demo will show it organizing a real test folder on a local machine.
Code
💻 GitHub repository: [Add your GitHub repository link]
How I Built It
SlothSort is built around open-source AI and local inference.
Python: File inspection, organization logic, duplicate detection, and filesystem operations.
Ollama: Runs a compatible open-weight language model locally.
Open-weight LLM: Analyzes extracted file information and suggests descriptive filenames, categories, and destinations.
Local metadata and content extraction: Helps the model understand documents without relying on a cloud AI service.
Safety and reversibility: A preview-first workflow, conflict checks, and an operation history help keep file changes under user control.
The AI is used where semantic understanding matters, while deterministic code handles filesystem operations. This keeps the system more predictable than allowing a language model to directly manipulate files without safeguards.
The intended workflow is straightforward: scan, understand, suggest, review, organize, and undo when needed.
Why Does Open Innovation Matter?
Your files are personal. Your assignments, financial documents, work files, and private notes shouldn't need to be uploaded to a third-party server just to organize them.
Open-weight models make it possible to run SlothSort locally, giving users greater control over their data and how their AI assistant behaves.
Open innovation also makes the project adaptable. Users can experiment with different compatible models, inspect the organization logic, change categorization rules, and improve the tool for their own workflows instead of being locked into a single proprietary API.
Local inference can also eliminate per-request AI API charges, making repeated file organization practical without a recurring model-usage bill. After the model and required dependencies are installed, the core workflow can operate offline.
For me, the most important part of open-source AI is that it makes useful automation more accessible. You shouldn't need an expensive subscription or surrender control of your files to stop wasting time on repetitive digital housekeeping.
Less file management. More free time. Stay slow, stay organized. 🦥
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