"SlothBrief: A Local AI Assistant That Reads Your Documents for You"

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title: "SlothBrief: A Local AI Assistant That Reads Your Documents for You"
published: false

tags: devchallenge, hf26challenge, opensource, python

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

What I Built

Meet SlothBrief 🦥, a local AI assistant designed to make working with documents less tedious.

Instead of opening multiple files and manually searching for important information, SlothBrief lets users load a folder of PDF, TXT, and Markdown files, generate a concise summary, and ask questions about the loaded documents.

The goal is simple: let AI handle the repetitive first pass so people can spend more time on the work that matters.

Demo

Coming soon: I'll add a short screen recording showing SlothBrief scanning sample documents, generating a brief, and answering a question.

Code

GitHub repository:

How I Built It

SlothBrief is built with:

• Python for the application logic.
• Streamlit for the user interface.
• Ollama for running an AI model locally.
• Llama 3.2 3B as the example open-weight model.
• pypdf for extracting text from PDF files.

The workflow is straightforward:

1. Select a local folder containing supported documents.
2. Extract text from the files.
3. Pass the relevant document text to the locally running model.
4. Generate a short brief or answer a question using the supplied documents.
5. Download the brief as a Markdown file.

The project is designed to run without a hosted LLM API. The initial model download requires internet access, but inference can run offline once the model is installed locally.

Why Does Open Innovation Matter?

Documents can contain private notes, plans, and information that users may not want to send to a third-party AI service.

By using an open-weight model with local inference, SlothBrief gives users more control over where their document content is processed.

Open innovation also makes the project easier to inspect, modify, and extend. Users can experiment with different compatible models, customize the workflow, and build additional capabilities without depending entirely on a closed AI API.

There is also a cost advantage: local inference avoids per-request hosted LLM API charges, although it still uses the computer's processing power, memory, and electricity.

For me, the important part is having the freedom to experiment and build around the model instead of treating AI as a black box.

My Agent Session

I'll add a DevRelay session link here if I capture one during development.

The Lazy Payoff

SlothBrief aims to reduce the repetitive work of opening documents, identifying key points, and writing an initial summary.

Instead of manually reviewing every file first, users can start with a compact brief and ask follow-up questions.

I'll compare the time required to review the same set of documents manually versus using SlothBrief and share the results after testing.

The idea is not to avoid reviewing important information. It's to make the first pass easier.

Prize Categories