SlothSort AI: Let AI Do the Sorting. You Do the Chilling.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Meet SlothSort AI — a simple, AI-powered file organizer designed for people who want to spend less time organizing files and more time doing what matters.
We all know the feeling: a Downloads folder full of documents, images, videos, code files, and random files we promise to organize later. Manually sorting everything is repetitive, boring, and easy to procrastinate on.
SlothSort AI makes that process easier. Choose a folder, let the app analyze filenames, review the suggested categories, and organize the files into folders such as Documents, Images, Videos, Audio, Code, Archives, and Other.
The goal is simple: less manual work, less digital clutter, and more time for the things you actually enjoy.
Who gets to be a little lazier? Students, developers, creators, and anyone whose folders have become a digital mess.
Demo
Try SlothSort AI live:
https://devanshguptacu-create.github.io/SlothSort-AI/
The project is designed to run in a compatible desktop browser. Folder access requires browser support and your permission. AI-powered classification also depends on a working Hugging Face Inference connection.
Code
GitHub Repository:
https://github.com/devanshguptacu-create/SlothSort-AI
The repository contains the HTML, CSS, and JavaScript used to build the project.
If you explore the code, you can see how the interface, file handling, AI classification, and category-based organization work together.
How I Built It
I built SlothSort AI using HTML, CSS, and JavaScript, keeping the project lightweight and accessible without requiring a separate Python backend or a local model installation.
The main pieces are:
- HTML: Structures the dashboard and file-organizing interface.
- CSS: Creates a modern, sloth-inspired interface with a dark visual theme.
- JavaScript: Handles folder selection, file scanning, classification, and organization.
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Open-weight AI model: The project uses Hugging Face Inference with
facebook/bart-large-mnlifor zero-shot text classification of filenames into relevant categories. - Browser File System Access API: Allows supported browsers to access a folder after the user grants permission.
The AI connection is an important part of the project: instead of relying only on fixed file extensions, the app can use a language model's classification capabilities to suggest categories from filenames. Extension-based categorization can serve as a fallback when AI inference is unavailable.
The hosted inference approach keeps the project easy to try without installing a large model locally. It does, however, depend on network access, provider availability, and a valid Hugging Face access token where required.
Why Does Open Innovation Matter?
Open innovation makes experimentation more accessible.
By building around an open-weight model, I could explore AI-powered classification without training a model from scratch or building an entire inference infrastructure myself. Hugging Face also makes it easier to experiment with models and connect them to practical projects.
For a student developer, this matters. It lowers the barrier to learning, prototyping, and building useful tools with AI.
It also gives developers more flexibility to inspect the model's documentation, experiment with alternatives, and adapt the implementation as their needs grow.
SlothSort AI represents my belief that AI should not only solve complicated problems. It should also eliminate small, repetitive frustrations from everyday life.
Open innovation helps make that kind of experimentation possible for more people.
My Agent Session
I haven't included an agent session link in this submission. This section is optional, so I'll add one if I have a shareable DevRelay session available.
The Lazy Payoff
The intended payoff is straightforward: fewer manual sorting decisions and less repetitive file management.
Instead of creating folders and moving every file individually, users can review AI-suggested categories and organize files in batches.
I haven't measured a verified time saving yet, so I won't invent a number. The next step is to test SlothSort AI on a real, messy folder and compare the time taken with manual organization.
The philosophy: automate the boring parts, keep the human in control, and save your energy for something better.
Always review the suggested categories before moving important files, and test with copies of files first.
Prize Categories
My primary focus is the Slow and Steady challenge: using open-source AI to reduce repetitive effort and make everyday work easier.
I'll select any additional partner prize categories only if the project meets their published eligibility requirements.
Built with curiosity, powered by open innovation, and inspired by the art of doing less repetitive work.
If you like the idea, check out the repository, try the demo, and share your feedback!
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