This is my submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Ever opened your Downloads folder to find one file, only to get lost in a sea of screenshots, PDFs, documents, and randomly named files? đ
I wanted to make that everyday frustration a little easier to handle.
Meet SlothDesk AI â an AI-powered file organization assistant that helps turn a messy workspace into a more organized one, without taking control away from the user.
Instead of manually sorting files one by one, you can scan a workspace, review AI-generated organization suggestions, approve the actions you want, and execute them. Supported file operations can also be undone.
đĻĨ The idea: let AI help with the repetitive work while you stay in control.
Key Features
- đ Scan: Inspect files in a workspace.
- đ¤ AI Suggestions: Get recommendations for organizing files.
- đ Human Approval: Review and approve or reject proposed actions before execution.
- âŠī¸ Undo Support: Reverse supported file operations.
- đ Restricted Demo: The hosted demo is designed to operate within a designated sample workspace.
The goal isn't to automate everything blindly. It's to make file organization easier while keeping important decisions in human hands.
Demo
đ Try the live application: https://slothdesk-ai.onrender.com
The project is deployed on Render, with a React frontend and a FastAPI backend.
Code
đģ Explore the source code on GitHub: https://github.com/tashu031/SlothDesk-AI
Feel free to explore the implementation, try the demo, and share suggestions or contributions!
How I Built It
SlothDesk AI combines a web interface, a Python backend, database storage, and an open-weight language model.
Tech Stack
- Frontend: React and Vite
- Backend: Python and FastAPI
- Database: SQLite
- AI Model: Gemma 2B
- Local Inference: Ollama
- Deployment: Render
- Testing: Pytest
How AI Fits Into the Workflow
The application uses AI to assist with organization suggestions. Those suggestions then go through an approval workflow before file operations are executed.
This separation is important: an AI-generated recommendation shouldn't automatically become an unrestricted operation on someone's filesystem.
I also implemented demo-mode restrictions to keep the hosted application's file operations within its designated sample workspace.
Building SlothDesk AI helped me understand that an AI application is about much more than connecting a model to a user interface. It also requires backend validation, testing, controlled file operations, and a workflow that users can trust.
Why Does Open Innovation Matter?
Open innovation makes it possible for developers to experiment with AI beyond relying entirely on closed, hosted APIs.
Using Gemma 2B with Ollama for local inference gave me the opportunity to explore how an open-weight model can fit into a practical developer tool.
Here's why that matters:
- More control: Developers can experiment with where and how inference runs.
- Local experimentation: Model inference can run on a developer's own machine when the required model and resources are available.
- Learning by building: You can explore the entire workflow, from model output to backend validation and human approval.
- Freedom to experiment: Developers can adapt the application and experiment with different models and approaches.
Open models don't automatically solve privacy, security, or deployment challenges. Those still require careful engineering. But they give developers more flexibility to learn, experiment, and build useful tools.
For me, this project was an opportunity to bring open-weight AI into an everyday problem rather than building just another chatbot.
My Agent Session
For this project, I focused on implementing and testing the application workflow, including file-operation safeguards and automated tests.
I haven't included a shareable DevRelay agent session in this submission.
The Lazy Payoff đĻĨ
The payoff I'm aiming for is less repetitive file sorting and fewer manual decisions.
Instead of deciding where every file belongs individually, users can review a set of suggestions and approve the actions that make sense.
I haven't measured a reliable time-saving figure yet, so I won't invent one. Measuring the time required for manual organization versus the AI-assisted workflow is a useful next step.
Because being lazy is fun. Building something that genuinely saves time is even better. đ
Final Thoughts
SlothDesk AI started with a simple question:
What if organizing a messy folder felt more like reviewing a helpful assistant's plan than doing all the work yourself?
Building this project gave me hands-on experience with open-weight AI, local inference, full-stack development, testing, and safer AI-assisted automation.
The application is now live, and I'm excited to keep improving it.
If you try it, I'd love to hear your feedback. What would make file organization easier for you?
â Try it: https://slothdesk-ai.onrender.com
đģ Explore the code: https://github.com/tashu031/SlothDesk-AI
Build thoughtfully. Automate carefully. Take it slow. đĻĨ
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