Inbox Zen: Privacy-First File Sorting with Local Gemma

A developer built Inbox Zen, a privacy-first AI agent that scans a local clutter directory, sends file contents to a locally hosted Gemma model via Ollama, and uses the model's categorization to sort files into folders such as Invoices and Personal Notes with shutil, logging results to a zen_summary.txt file. A Flask server deployed on Render displays the latest sorting summary, and Sentry tracing was added to flush transaction data before the script exits. The developer says the local-first design keeps sensitive documents off proprietary cloud APIs, eliminates API costs, and saves roughly 10-15 minutes of manual filing each morning.

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 "Inbox Zen" is a privacy-first AI agent that automatically categorizes and archives cluttered local files or emails overnight. It scans a designated "clutter" directory, reads the text, and intelligently sorts them into appropriate folders like "Invoices", "Reading List", or "Personal Notes" using a local AI model. Finally, it generates a simple morning summary so you can wake up to a clean digital workspace without the dread of manually sorting files. This directly targets the "Slow and Steady" theme: automating a tedious, repetitive daily chore. Here is the live link to my daily "Inbox Zen" summary dashboard deployed on Render: https://inbox-zen.onrender.com https://inbox-zen.onrender.com Note: If you visit, you'll see the latest summary of the files Gemma successfully sorted on my local machine You can find the full Python script and Flask server code in my GitHub repository here: https://github.com/2AJ0/inbox-zen https://github.com/2AJ0/inbox-zen I built this project completely manually focusing on a local-first approach to protect sensitive data: requests library to send file contents to the local Gemma instance, prompting it to strictly categorize the text. The script then uses shutil to move the files and generates a zen summary.txt log. sentry sdk.start transaction block and used .flush to ensure trace data was sent before the script closed. Open innovation is the absolute core of this project. Because I am dealing with personal files like invoices or personal notes , sending this data to a closed, proprietary API in the cloud is a massive privacy risk. By utilizing an open-weight model like Gemma and running it locally with Ollama, my sensitive data never leaves my machine. It also means the tool is completely free to run with zero API costs, demonstrating how open-source AI democratizes powerful automation. This agent saves me about 10-15 minutes of dragging and dropping files every single morning. More importantly, it removes the "digital tax" and mental friction of starting my day staring at a messy downloads folder. I just check my dashboard, see what was filed where, and start working. I am entering this submission for the following partner categories: