SlothPlan: the to-do list that does less

A developer built SlothPlan, a command-line to-do triage tool that sorts a messy task list into DO TODAY (capped at three items), DEFER, and DELETE buckets, each with a one-line reason. The tool runs entirely locally, using deterministic rule-based logic or an optional open-weight model with no account, API key, or network access. In the recorded run, the deterministic mode surfaced three time-sensitive obligations, while the 0.5B model incorrectly deferred an electricity bill with the reason "no deadline, start next month.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady https://dev.to/challenges/hacktoberfest-week3-2026-10-19 My to-do list has 47 items. Yours probably does too. And here's the dirty secret of productivity culture: the list isn't the work. Triaging the list is the work — the staring, the re-ordering, the guilt-math of "is learning Spanish more important than calling the dentist?" I have spent entire Sunday mornings reorganizing tasks instead of doing them, which is just procrastination wearing a planner's clothes. Every to-do app on the planet answers this with more features: priorities, tags, Eisenhower matrices, AI scheduling assistants that need your calendar, your email, your soul, and a $12/month subscription. They make the triage heavier . Nobody built the app that does the triage for you and then — this is the important part — tells you to do less. So for "Slow and Steady" I built exactly that: a tool that rewards laziness by doing the one piece of work you were avoiding deciding what matters , capping your day at three things, and granting you written permission to delete the rest. SlothPlan — a command-line tool where you dump a messy to-do list and get back three buckets: DO TODAY max 3, non-negotiable , DEFER officially not today's problem , and DELETE permission granted, let it go . Each item gets a one-line reason. The judgment comes from a local open-weight model; the guarantees come from deterministic code. python3 -m slothplan triage -f my-messy-todos.txt The twist, same as my Week 2 entry: the model, the inference engine, and all the code run on your own machine. No account. No API key. No subscription. Your to-do list — which is, let's be honest, a map of your anxieties — never touches a network. I fed it a 12-item Tuesday dump. Here is what the recorded run produced full transcript in the repo's demo-output.txt — nothing cherry-picked : === SLOTHPLAN triage === method: rule-based fallback --no-llm was passed; no model was run DO TODAY max 3 — your only decisions today: 1. Pay electricity bill, due Friday or they cut the power - looks time-sensitive — it names a deadline, payment, or an urgent event 2. Reply to Priya about the wedding RSVP — deadline is tomorrow - looks time-sensitive — it names a deadline, payment, or an urgent event 3. Submit the Hacktoberfest Week 3 entry README tonight - looks time-sensitive — it names a deadline, payment, or an urgent event DEFER — officially not today's problem: 4. Call dentist to reschedule the appointment - looks time-sensitive — it names a deadline, payment, or an urgent event moved from today: the 3-item cap keeps your day lazy 12. Call mom back — she called twice today - looks time-sensitive — it names a deadline, payment, or an urgent event moved from today: the 3-item cap keeps your day lazy DELETE — permission granted, let it go: 10. Delete that crypto spam email from "DOUBLE YOUR COINS" - matches a junk pattern — nothing of value is lost 11. Reorganize my Netflix watchlist - matches a junk pattern — nothing of value is lost That's the deterministic mode --no-llm , and it's the mode I'd actually use daily: three real obligations surfaced, the dentist and mom politely bounced by the cap with an explanation, and the crypto spam and the Netflix-watchlist reorganization sentenced to death. Twelve decisions I didn't have to make. Now the confession, because the transcript has two runs and I'm not hiding the first one: when I let the 0.5B model judge all 12 items itself, it took "rewards laziness" as a personal mission. It deferred the electricity bill with the reason "no deadline, start next month" — for Deliberately boring architecture, because boring ships: comments and bullet/checkbox markers are stripped. My sample file's header comment got triaged as a task in the first build — fixed, and there's a test for it now. slothplan/llm.py : one verdict per task from slothplan/triage.py : every verdict is parsed and validated. Max 3 in "today" — overflow is moved to defer This tool cannot exist on a closed API, for two reasons — one practical, one philosophical. Practically: SlothPlan's entire design is a bet that a small model is enough. A 0.5B model is honest about what it is — opinionated, sometimes wrong, occasionally deleting your electricity bill — and the whole architecture deterministic guardrails, labeled fallbacks, a --no-llm mode is built around that honesty. You can only build that way if you can run the model locally, iterate on prompts for free at 3 a.m., and ship something whose marginal cost per triage is exactly zero. A closed API charges per token, which means every design decision bends toward fewer, bigger calls — the opposite of this architecture. And a to-do list is personal enough that "send it to someone's cloud" was never on the table. Philosophically: the open-weights ecosystem is what lets a student with a $0 budget and a borrowed VM build something in a day that would have been a research project five years ago. Qwen2.5's weights are public under Apache 2.0. llama.cpp is MIT. My code is MIT. Anyone can audit every layer, swap in a bigger GGUF with one flag, or fork the whole thing. That composability — weights, engine, and app all inspectable and replaceable — is open innovation working exactly as advertised. The moat isn't the model; it's the trust that comes from everything being auditable. I'd rather tell you than have you discover them: --model ; a bigger model would judge better. no reason given — the model often emits just the bucket word despite being shown examples. .txt input, CLI only. Bonus-points honesty: I have no first user yet — this was built for the challenge this week, starting Monday morning. The demo above is real output from the actual tool, and the limitations section is real too. Repo: https://github.com/swarnnim/slothplan https://github.com/swarnnim/slothplan ./scripts/download-llamacpp.sh ./scripts/download-model.sh python3 -m slothplan triage -f sample todos/messy.txt If you also have a 47-item list you're avoiding: what's the one task on it you'd secretly love an app to delete for you? That's the roadmap — a bigger delete bucket.