🦥 Sloth Mode: A Local AI Assistant That Does the Boring Work for You

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Meet Sloth Mode — a local-first AI document assistant designed for people who want to spend less time dealing with information and more time doing things they actually enjoy.

We all have documents, meeting notes, and random text files that take time to read and organize. Sloth Mode helps reduce that effort by turning lengthy text documents into concise, useful summaries.

With Sloth Mode, you can:

📄 Select a text document from your local documents folder.
🧠 Generate an AI-powered summary with important points, decisions, and action items.
🖥️ Use a simple browser-based interface built with Streamlit.
⬇️ Download the generated summary for later use.
The goal is simple: let AI handle the repetitive reading so you don't have to.

Code

GitHub repository: [https://github.com/priyeshdabre07/sloth-mode]

The project is built with Python and is designed to be easy to run and modify.

Repository contents:

ui.py — Streamlit user interface.
app.py — command-line document summarizer.
documents/ — sample text documents for testing.

How I Built It

I built Sloth Mode using a small, local AI stack:

• Python — application logic and document processing.
• Streamlit — browser-based user interface.
• Ollama — local model runtime and inference API.
• Qwen 2.5 3B — the open-weight language model that generates summaries. The application reads text files from a local folder, sends their contents to the Ollama service running on my Mac, and displays the model's response in the browser.

The workflow is straightforward:

1. Choose a text document.
2. Send its contents to the locally running model.
3. Generate a concise summary with important points and action items.
4. Display and download the result.

Why Does Open Innovation Matter?

For Sloth Mode, open-weight AI makes local, private-by-design document assistance possible without depending on a proprietary cloud AI API.

Privacy and control: Documents can be processed on my own computer instead of being uploaded to an external AI provider.

Offline potential: Once the model and software dependencies are installed, the core inference workflow can run without an internet connection.

No per-request API fees: Local inference avoids usage-based charges from a hosted model API, although it still uses electricity and computer resources.

Freedom to experiment: I can change the model, modify prompts, adjust the summarization workflow, and extend the application without being locked into one proprietary AI endpoint.