The Sloth Inbox: I Built an AI That Dreads My Inbox So I Don't Have To 🦥

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

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

Tomorrow is International Sloth Day, and I have decided the sloths are right about everything.

A sloth's whole philosophy is: move slowly, conserve energy, and let the world come to you. My inbox is the opposite of that philosophy. It arrives overnight, all urgency and exclamation points, and demands I become a different animal — a twitchy meerkat, scanning the horizon at 7 AM for bills, permission slips, and "FINAL NOTICE" subject lines.

So I built The Sloth Inbox: a local overnight triager that reads the pile while I sleep and hands me a two-to-three-line morning brief. That's it. That's the whole product. It does the dreading so I don't have to.

The inbox did the overnight shift. You just woke up. 14 messages arrived overnight: 4 need you, 2 newsletters filed for later, 2 promos quarantined, 2 receipts archived, 2 social pings, 2 low-priority drift. Worth your energy: Payment due: your electric bill; Permission slip needed for field trip; Reminder: dentist appointment tomorrow at 9. Sloth take: 4 of 14 need you; the rest can marinate. Handle the few, ignore the many.

Three lines. I know exactly what deserves my energy and exactly what can marinate. The sloth take at the end is generated by a local open-weight model (more on that below) — or, when no model is installed, by a clearly-labeled mock, because a sloth would never pretend to be something it isn't.

No deployed link — this one is proudly, deliberately local-first, which is the entire point (see "Why Does Open Innovation Matter?" below). Here's a real run against a synthetic 14-message overnight inbox:

============================================================ 🦥 THE SLOTH INBOX — morning brief ============================================================ The inbox did the overnight shift. You just woke up. 14 messages arrived overnight: 4 need you, 2 newsletters filed for later, 2 promos quarantined, 2 receipts archived, 2 social pings, 2 low-priority drift.

Worth your energy: Payment due: your electric bill; Permission slip needed for field trip; Reminder: dentist appointment tomorrow at 9. Sloth take [mock:mock-canned-take-v1]: 4 of 14 need you; the rest can marinate. Handle the few, ignore the many. [mock take] ------------------------------------------------------------ adapter: mock:mock-canned-take-v1 ledger: out/ledger.json (14 messages classified) brief: out/brief.json Enter fullscreen mode Exit fullscreen mode Alongside the brief it writes a full audit ledger — every message, its bucket, its confidence score, and the exact reasons it landed there:

{ "message_id": "m01", "category": "needs-you", "confidence": 1.0, "reasons": [ "subject hit: 'payment due' (+10) -> needs-you", "subject hit: 'bill' (+6) -> needs-you" ], "sender": "billing@citypower.example", "subject": "Payment due: your electric bill" } Enter fullscreen mode Exit fullscreen mode No black box. If it files your electric bill under "low-priority drift," you can see exactly which rule betrayed you.

The project is pure Python, standard library only — no dependencies to install, no API keys, no accounts:

Repo: https://github.com/midnightmichigan/sloth-inbox (public, MIT — the complete project above, 15/15 checks green)

The architecture has one non-negotiable principle: the model never sees your email.

Triage — the part that actually touches private messages — is the deterministic classifier. It's fast, explainable, testable, and it runs anywhere Python runs, including a laptop with the Wi-Fi off. The open-weight model has exactly one job: read the aggregate stats (counts per bucket, nothing more) and write the one-line "sloth take" at the end of the brief. Stats in, one sentence out. There is no prompt on earth that can leak an email the model was never given.

adapter = MockModelAdapter () # tests, offline, CI adapter = OllamaAdapter ( model = " gemma3 " ) # the real thing, localhost only Enter fullscreen mode Exit fullscreen mode OllamaAdapter speaks to localhost:11434 with the standard /api/generate endpoint — stdlib urllib, no SDK needed. If Ollama isn't running, it raises a clear error and the brief falls back to a labeled template take; classification is never affected. I developed and tested the full pipeline against the mock adapter (15/15 green, including a graceful-degradation test with no server present); the Gemma path is implemented and waiting for a machine with Ollama installed.

This project only works because the AI is open. A closed API would break it in four places:

Closed AI would make this a SaaS signup form. Open AI makes it a script you own.

I ran it this week on a synthetic 14-message overnight inbox — bills, a school permission slip, newsletters, flash sales, shipping confirmations, social pings, and one forwarded sloth video from an uncle. Triage took well under a second; the brief is three lines; the ledger explains every decision. The manual equivalent is opening 14 messages, skimming each, and mentally sorting them before coffee — roughly ten minutes of meerkat-mode every morning, or about 60 hours a year of pre-coffee triage. The Sloth Inbox compresses that to the time it takes to read three lines. Energy conserved. Leaves eaten slowly. This is the way.