Sloth Digest

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

What I Built

SlothDigest turns your noisy group chats into two lines.

Line one says what happened. Line two says what to do today.

You paste your chats and press one button. Gemma runs on your own computer and writes the digest. A Python version runs from a scheduler so the digest is ready when you wake up.

Demo

Live site: https://sloth-digest-tqen.onrender.com

The screenshots show a real run on my laptop. The live page talks to Ollama on your own computer. It cannot run a model by itself.

The page finds Ollama and lists your models. It picks Gemma first. A skeleton loader shows while the model works. You can cancel any time. One button wipes everything.

Code

https://github.com/triixdev/Sloth-Digest

How I Built It

Model: Gemma 3 (gemma3:4b) served by Ollama.

Site: plain HTML and CSS and JavaScript. No framework and no build step.

Each chat gets a one sentence summary. A second call turns the summaries into the digest.

Privacy is enforced in the browser. A content security policy lets the page talk to localhost only. A request to any other server is blocked.

Safety is built in. Chat text goes to the model as data. The prompt tells the model to ignore commands hidden inside a chat. Output is shown as plain text so it can never run as code.

The command line script does the same job on a folder of .txt files. It can send timing and token counts to Sentry. It never sends message text.

The repo includes a privacy policy and terms of service. Real chats stay out of git.

Why Does Open Innovation Matter?

Chats hold names and plans and work secrets. They should stay on my machine.

Gemma is open so I run it locally. Nothing is uploaded. There is no key and no bill. It works with the network unplugged.

I can swap the model from a dropdown. A closed API would see every message and charge for each one.

The Lazy Payoff

My three sample chats are tiny. Reading them takes about 20 seconds. SlothDigest took 14 seconds on my laptop.

The saving shows up with real volume. A busy day has about 200 messages. At 12 words each that is 2,400 words. Reading speed is about 240 words per minute. That is 10 minutes of reading.

With a scheduler the digest is ready before I wake up. I read two lines instead.

Prize Categories

Best Use of Gemma
Best Use of Render