Aalsi Babu :The Zero-Effort Local File Munshi

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

What I Built

AalsiBabu (Hindi for "Lazy Clerk") is the ultimate zero-effort local file organizer designed for maximum laziness.

We all have that dreaded Downloads or Desktop folder filled with randomly named files like scan_0042.pdf, IMG_9912.png, and notes_final_v3.txt. Usually, fixing this requires opening each file, reading it, renaming it, and dragging it to a folder.

With AalsiBabu, you do absolutely nothing. You just drop your messy files into the AalsiBabu_Inbox folder and take a nap. A background watchdog wakes up the AI, which inspects the content (text or image), renames the file to something clean (2026-10-25_electricity_bill.pdf), moves it to a categorized folder (e.g., Finance & Receipts), and generates a 2-line TL;DR along with a Babu's Advice (Action Check) in a daily digest. It reads the files so you don't have to!

Demo

• Demo Video Link : https://youtu.be/j76k0wAyW1A?si=iMxFeoGkqNFH-4b5

Code

• GitHub Repository: https://github.com/LakshyaMulchandani/ListCraft-AI.git

How I Built It

AalsiBabu is built around 100% local open-source AI infrastructure:

• Model: Google's Gemma 3 (gemma3:4b). I chose this open-weight model because it is natively multimodal—meaning it can inspect both text documents (via PyPDF) and image receipts (screenshots) using a single, lightweight 4B parameter model.
• Inference Engine: Ollama, running entirely locally on my machine.
• Structured Outputs: I utilized Ollama's support for JSON schemas via Pydantic to ensure AalsiBabu deterministically outputs valid filenames, precise folder categories, and structured summaries without hallucinating.
• Frameworks: A Python backend utilizing watchdog to monitor the inbox folder in the background, and Streamlit with a custom warm/light theme for the "Lazy Dashboard" UI.

Why Does Open Innovation Matter?

When building an automated file babu that reads every PDF, receipt, screenshot, and personal note on your hard drive, a closed cloud API is a complete non-starter. Open innovation made this project possible in three specific ways:

1. Uncompromising Privacy: Our personal folders contain bank statements, tax PDFs, and private notes. Running gemma3:4b locally via Ollama guarantees that my sensitive documents never leave my laptop or touch a third-party server.
2. Set It Once, Pay ₹0 Forever: A background file watcher runs 24/7. If it called a paid cloud vision or text API every time a file was downloaded, I'd be constantly worrying about token bills or rate limits. With open weights, inference is completely free forever.
3. 100% Offline Capability: Whether I'm on a flight or working with zero Wi-Fi, my automated digital clerk still cleans up my file mess.

The Lazy Payoff

The biggest payoff is eliminating "digital clutter anxiety." Before AalsiBabu, organizing my Downloads folder felt like a weekend chore. Now, I just bulk-drag 50 files into the inbox and go watch a movie.

When I come back, my files are neatly sorted into directories, and I just read the Lazy_Digest.md which tells me exactly what I received (e.g., "TL;DR: This is your October Wi-Fi bill. Babu's Advice: Pay ₹800 by Friday."). It saves me roughly an hour of tedious sorting every week, rewarding my laziness with perfect organization.

Prize Categories

• Gemma: Built utilizing Google's open-weight Gemma 3 model for local, multimodal inference.
• Hacktoberfest Open-Source AI Challenge Week 3 (Slow and Steady): Overall Challenge Category.