# slothnotes: let a tiny local model read your notes so you don't have to > Source: > Published: 2026-10-25 14:43:12+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge: Week 3](https://dev.to/challenges/hacktoberfest-week3-2026-10-19): Slow and Steady.* I have a folder full of notes I never reread. Standups. A grocery list. An email to my landlord. Book club plans. slothnotes is a small Python CLI that reads that folder for me. You point it at a folder of `.md` or `.txt` files. It prints one summary line per note. If you want, it also writes a `digest.md` you can skim in about ten seconds. It runs a small open-weight model (Qwen2.5-0.5B-Instruct, Q4_K_M GGUF, about 490 MB) on the CPU through llama.cpp. After the model download, it needs no API key and no network. The effort it saves is the boring part: opening every file to remember what was in it. One command does that. This is a real run on four sample notes: standup, groceries, landlord, book club. On an 8-core CPU with no GPU, it summarized all four in about 3 seconds. One honest limit: the tiny model got a grocery detail wrong. It listed "half a loaf of bread" when the note actually says to skip bread. A bigger GGUF should be more accurate, but it will be slower. For a quick skim it's fine. For anything that matters, read the note. [https://gitlab.com/engelstands-hue/slothnotes](https://gitlab.com/engelstands-hue/slothnotes) MIT licensed. It has a README, three pytest tests that run without the model, and a GitLab CI job. `--model` takes any GGUF, so you can swap models without touching code. To try it: ``` pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu curl -L -o models/qwen2.5-0.5b-instruct-q4_k_m.gguf https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF/resolve/main/qwen2.5-0.5b-instruct-q4_k_m.gguf python slothnotes.py demo_notes --out digest.md ``` Notes are personal. Standups, landlord emails, grocery lists. Because slothnotes runs an open-weight model locally, none of that leaves the machine. It works offline, and each run costs nothing. Since the model is open, you can swap in a bigger or fine-tuned one with a single flag. A closed API would mean an account, a key, a bill, and sending my notes to someone else's server. For a tool whose whole point is being lazy, that's too much setup. None. This entry doesn't use partner technology. More of what I build: [https://engelailabs.com](https://engelailabs.com)