What I Built
Meet SlothDesk 🦥 — a document organizer that does the boring work for you.
Students and developers accumulate PDFs, notes, reports, and text files every day. Organizing them manually means opening files, figuring out what they contain, renaming them, and moving them into the right folders. I wanted to make that process easier.
SlothDesk uses AI to analyze documents and suggest descriptive filenames, categories, tags, and short summaries. You review the suggestions, select the files you approve, and download the organized result as a ZIP.
It supports TXT, Markdown, PDF, DOCX, CSV, LOG, and JSON files.
The goal is simple: spend less time managing files and more time doing actual work.
Demo
Live application: https://slothdesk-ai.streamlit.app/
The deployed version lets users upload documents, analyze them with AI, review suggested changes, and download the organized output.
Code
GitHub repository: https://github.com/ShamikDas-Dev/SlothDesk
The project is built with Python and Streamlit, with a reusable document-processing module and an AI-powered analysis workflow.
How I Built It
I built SlothDesk with Python, Streamlit, and open-weight AI models.
The project has two approaches to AI inference:
- Local mode: Ollama with Qwen2.5 1.5B. This lets users analyze documents locally without sending their contents to a cloud AI API, once the model is downloaded.
- Cloud mode: Groq API with OpenAI GPT-OSS 20B, an open-weight model served through Groq.
The application extracts text from supported documents and asks the model to return structured metadata, including a category, a descriptive filename, tags, and a summary.
The workflow is intentionally straightforward:
- Upload documents or select a local folder.
- Let the AI analyze their contents.
- Review and edit the suggested categories and filenames.
- Select the files you want to organize.
- Generate organized copies and download the resulting archive.
I also added safeguards: the application does not automatically delete or move original files, and users can review suggestions before applying them.
Why Does Open Innovation Matter?
I wanted SlothDesk to be more than another interface built around a closed AI API.
Using an open-weight model gives the project more flexibility. I can experiment with different models, change the inference setup, and potentially self-host the model instead of depending on a particular proprietary model API.
Open innovation also lets the same application serve different needs.
Someone who prioritizes privacy can use the local Ollama version, keeping document analysis on their own computer. Someone who prefers a hosted application can use the Groq version without having to run a model locally.
There is a trade-off: the hosted version sends extracted document text to Groq for inference, so it should not be treated as an offline or fully local solution. The local version is the better option when keeping documents on the user's own machine is important.
For me, that flexibility is the main value of open-weight AI. The model is not just a hidden dependency; it is a component I can replace, run differently, and build around.
The Lazy Payoff
The real payoff is removing repetitive decisions from everyday file management.
Instead of manually opening every document, choosing a category, inventing a filename, and sorting everything yourself, SlothDesk prepares suggestions in one workflow. You only need to review the results and approve the changes.
I have tested the application workflow, but I have not yet benchmarked the exact time saved across a large collection of files. I would rather measure that properly than invent a number.
My idea of being productive is not doing every small task faster. Sometimes, it is eliminating the task altogether.
Conclusion
SlothDesk is my attempt to make AI useful for an ordinary, repetitive problem.
It combines document understanding with a practical organizing workflow, gives users control over the final changes, and offers both local and hosted inference options.
The sloth has the right idea: conserve your energy for things that matter.
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