This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
Sloth Digest is a local AI agent that reads my unread Gmail overnight and hands me a morning digest, so I never have to open a cold inbox again.
Every email gets triaged by an open-weight model running entirely on my own machine (Gemma 3 4B IT, QAT-quantized, served by llama.cpp on plain CPU) into three buckets:
Who gets to be lazier? Me, every single morning. Instead of 15–30 minutes of scanning subject lines and context-switching, I read one short markdown page and only touch the 3–4 emails that actually need a human.
Here's real output from the bundled sample inbox:
## ⚡ Needs you (4)
- **Energia Lux** — Your October invoice — €84.12 due Oct 27
Electricity bill of €84.12 is due soon.
→ Pay the invoice before October 27th.
- **Marco Silva** — Re: Thursday moved to 15:00?
Plumber availability needs confirmation for Thursday.
→ Confirm 15:00 or propose an alternative time.
...
## 😴 Skip safely (1)
- **ShopMega** — 🔥 70% OFF ends tonight — don't miss out!
Promotional email about a limited-time sale.
6 emails triaged locally in 14s (~12 min of manual triage saved).
Nothing left this machine.
Live demo: https://friend-crops-sharp-episode.trycloudflare.com
It shows the sample inbox (6 fake emails — bills, a friend asking for a confirmation, a flash sale, a delivery notice, a medical appointment reminder) and runs the triage live on the local Gemma model when you hit the button. ~14 seconds on CPU, no cloud API involved anywhere.
You can reproduce the whole thing offline:
./llama-server -m gemma-3-4b-it-qat-Q4_0.gguf --port 8801
python sloth_digest.py --demo # sample inbox, zero credentials needed
A local AI agent that reads your unread Gmail overnight and hands you a morning digest — so you don't have to.
Every email is triaged into three buckets by an open-weight model running entirely on your own machine (Gemma 3 4B IT QAT via llama.cpp):
No email content ever leaves your computer. Works offline. Costs nothing per run — set it up once and forget it.
Your inbox is the most private dataset you own. A closed API means sending every message — invoices, medical appointments, personal threads — to someone else's servers. Sloth Digest runs the whole pipeline against a local OpenAI-compatible endpoint, so the "open" piece is the product:
The agent is ~200 lines of Python stdlib. The only dependencies are llama.cpp (for inference) and, for the optional web UI, FastAPI.
The architecture is deliberately boring — a sloth would approve:
unread Gmail ──► fetch (Gmail API, strictly read-only)
│
▼
┌─────────────────────┐
│ Gemma 3 4B IT (QAT) │ per email: CATEGORY / SUMMARY / ACTION
│ llama.cpp, CPU │ strict 3-line verdict, temperature 0
└─────────────────────┘
│
▼
morning digest (markdown, grouped by urgency)
Three design choices make a small 4B model reliable enough to trust with triage:
CATEGORY / SUMMARY / ACTION) at temperature 0. No essays, no JSON to break.
Local inference is not a gimmick here, it's the point: the whole pipeline — fetch, classify, summarize — runs against llama.cpp's OpenAI-compatible endpoint on 127.0.0.1. Pull the network cable and it still works.
Your inbox is probably the most private dataset you own: invoices, medical appointments, family threads, business negotiations. Triage means reading all of it — and with a closed API, "reading" means shipping every message to someone else's servers, on someone else's terms, under a privacy policy that can change.
Because Gemma's weights are open and llama.cpp is open source, the equation flips:
I ran it on my real unread inbox this morning: 8 emails triaged in ~20 seconds, and the digest correctly separated two things I genuinely needed to act on from the newsletter noise. Manual triage averages ~2 minutes per email once you include the context-switching — so the agent saves me roughly 15 minutes a day, ~90 hours a year, for a one-time setup cost and €0 in API fees.
The cron job runs at 7am. I wake up, read one page, and go back to being lazy. The sloth wins. 🦥