LazyDigest: Zero-Effort Note Digests Powered by Local Gemma

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

What I Built

I built LazyDigest, a small background utility that takes the raw, unorganized Markdown notes I drop into a folder and turns them into a short daily digest: a category, a one-line takeaway and an optional action item for each note.

Instead of organizing folders, adding tags or re-reading messy scratchpads, LazyDigest runs quietly every night at 03:00 using our Faithful Servant, Google's Gemma 3 1B open-weight model, through Ollama. I dump files into a folder, go to sleep, and wake up to a summary.

Demo

(The video shows LazyDigest reading a folder of raw Markdown notes, writing a digest file offline, and moving the processed notes into the archive folder.)

Code

elghaly-dev / lazydigest

Local-first daily digest of your Markdown notes with Gemma 3 1B via Ollama

LazyDigest: Local Markdown Snippet Synthesizer

LazyDigest is an open-source, local-first background utility that reads the scattered Markdown notes you drop into a folder and turns them into one short daily digest — a category, a one-line takeaway, and an optional action item per note — using Google's Gemma model running locally through Ollama.

Built for the Hacktoberfest Open-Source AI Challenge 2026 (Week 3: Slow and Steady).

What It Does

1. Reads every .md file in data/input_notes/.
2. Analyzes each note with gemma3:1b via Ollama, without sending data to external APIs.
3. Writes the results into data/output_digests/digest_output.md, then moves processed notes into data/processed/ so they are never read twice.

Lazy Payoff

• Time saved: roughly 75 minutes a week of re-reading raw notes, tagging them, and copying out action items (our own estimate).
• Set and forget: run --daemon and it digests your notes every night at 03:00 while you sleep.

Setup &

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View on GitHub

How I Built It

• Local inference: ollama-python with gemma3:1b.
• No API keys: no cloud services or credentials.
• CPU friendly: temperature 0.1 for short, structured JSON on CPU-only laptops.
• Robust parsing: strips code fences, fills missing fields, and falls back to the note's first line if the model output is not valid JSON.
• Archiving and scheduling: the schedule library runs it daily at 03:00 and moves processed files into data/processed/ so nothing is read twice.
• Tests: the model is mocked, so the tests run without Ollama.

Why Does Open Innovation Matter?

1. Privacy: personal scratchpads stay on my own machine.
2. Offline: it works without an internet connection.
3. Ownership: an open-weight model keeps a local tool working without API deprecations or surprise bills.

The Lazy Payoff

• Time saved: about 75 minutes a week (my estimate) that I used to spend re-reading notes and picking out action items.
• Effort saved: no more manual sorting or tagging; I just drop files in the folder.

Prize Categories

• Best Use of Gemma