Sloth Brief — Open-Source AI for Lazy Automation

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

What I Built:

Sloth Brief — a deliberately lazy local agent that turns the messy stuff you keep putting off into a calm morning briefing. You drop group-chat exports, brain-dump notes, half-written emails, or any .txt/.md files into an inbox/ folder before bed (or whenever).

A small open-weight model running entirely on your laptop reads everything and produces a short Markdown file that contains:
Exactly two sentences that capture what actually matters
The three tiniest next actions (ideally < 2 minutes each)
A “safe to ignore until later” list:-
One low-energy win for the day:
The whole point is that the computer does the cognitive work so you don’t have to. You wake up (or finish your coffee) and already know the minimum viable next steps. No scrolling, no decision fatigue, no “I’ll deal with it later.”It is designed for a normal laptop, works fully offline once the model is downloaded, and never sends your private chats or notes anywhere.

Demo

Since Sloth Brief is designed as a local-first, privacy-focused agent (where private notes and chat logs never leave your device), there is no live web server required.

Source Code & Local Setup: Tam-ato/sloth-briefing

How to Run locally in 30 Seconds:

1. Pull the model via Ollama:

bash
ollama pull gemma4:31b-cloud   # or local gemma4:12b / llama3.2:3b 

Code
Key pieces:

src/sloth_brief.py – the agent (context gathering, prompt, local LLM call, optional watcher)

inbox/ – drop files here

out/ – generated briefs land here

examples/ – sample group-chat export + brain-dump notes

How I Built It
The core is a local open-weight model served by Ollama. I deliberately kept the architecture simple:

A few lines of code walk the inbox/ folder, newest files first, and concatenate contents up to ~1,100 characters.

A single system prompt tells the model exactly the Markdown shape it must produce.

The official Ollama Python library talks to the local server (localhost:11434).

Why Open Innovation Matters
Privacy by default: Personal notes stay strictly on your local machine.

Works completely offline: Once weights are downloaded, the tool works on a plane or without an internet connection.

Full control of behaviour: You can swap models, rewrite system prompts, or fine-tune without asking permission.

My Agent Session
Note: Developed locally using Ollama and Python CLI.

Building Process & Tradeoffs
Theme Selection (Slow and Steady): Built Sloth Brief to solve notification fatigue and info overload by turning scattered group chat exports and notes into a calm morning digest.

Local Model Focus: Chose gemma4:31b-cloud as default (with seamless offline fallback to gemma4:12b) via Ollama to keep sensitive daily notes completely private.

Context Truncation: Designed custom file-reading logic in Python to cap inputs at ~1,100 characters so local models run rapidly.

Structured Framing: Enforced strict Markdown formatting: 2-sentence context, 3 micro-actions (< 2 min), a safe-to-ignore list, and 1 quick win.

The Lazy Payoff
I used it myself while writing this post. Dropping two days of scattered notes produced a clean, actionable 30-second read.

Prize Categories
Best Use of Gemma: The project is designed around small open-weight models and defaults to gemma4:31b-cloud.