🦥 SlothSummarizer: A Local, Offline AI Tool for Lazy Mornings

What I Built

I built SlothSummarizer, a local, offline, open-source AI CLI tool that reads your messy group chat exports or meeting notes and hands you a strict two-line summary plus a maximum of three action items.

Who gets to be lazier? Anyone who has ever woken up to 400+ unread messages in a group chat, only to find out the only important thing was "Meeting moved to 3 PM." Instead of scrolling for 15 minutes, you run this tool locally and get the gist in seconds.

Demo

Here is an example of the terminal output when feeding it a messy chat log:

🦥 SlothSummarizer is waking up slowly...

--- 🌅 Your Lazy Morning Summary ---
The group discussed moving the Wednesday meeting to 3 PM and agreed on ordering pizza for the office. 
Action items:
- Confirm the 3 PM meeting room booking.
- Collect dietary restrictions for the pizza order by Tuesday EOD.
------------------------------------

Code

The core logic is a simple Python script that calls Ollama's local API. Here's the summarization function:

import ollama

def summarize_chat(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        chat_text = f.read()[-4000:]

    prompt = f"""You are SlothSummarizer, a lazy but efficient AI assistant. 
    Read the following chat log. Provide exactly:
    1. A two-line summary of what happened.
    2. A bulleted list of up to 3 action items (if any).

    Chat log:
    {chat_text}
    """

    response = ollama.chat(model='llama3.2', messages=[
        {'role': 'user', 'content': prompt},
    ])

    print(response['message']['content'])

How I Built It

I built this using Python and Ollama, running the llama3.2 open-weight model entirely via local inference.

The script reads a text file, truncates it to fit safely within the local context window, and passes it to the locally hosted llama3.2 model with a strict system prompt demanding extreme brevity. No cloud APIs, no external dependencies beyond the local Ollama daemon.

Open innovation is the only reason this tool is viable for its intended purpose:

Absolute Privacy: Group chats and personal notes are sensitive. Running an open-weight model locally means my data never leaves my machine or goes to a third-party server.

Zero Cost & No Rate Limits: Closed APIs charge per token. Summarizing large, messy text files frequently would incur costs or hit rate limits. With open-source local inference, it costs nothing to run, encouraging true "set it and forget it" laziness.

Offline Reliability: It works on a laptop with no internet connection, perfectly embodying the "Slow and Steady" theme.

While this is a straightforward local script rather than a multi-step agent harness, the development process involved iteratively refining the prompt in Ollama to ensure the model strictly adhered to the "two-line summary + max 3 bullets" rule, resisting the urge to be overly verbose.

I tested this on a real 300-message WhatsApp export from a project group. Reading it manually would have taken ~12 minutes. Running SlothSummarizer took 8 seconds of local inference time and saved me from reading 250+ messages of "lol", "k", and off-topic tangents. I now run it every morning on my overnight chat exports.

Local-first AI / Privacy-focused AI (Open-weight models running locally)