My Friend Keeps Forgetting Things Buried in WhatsApp and Voice Notes — So I Built Nudge

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

I built Nudge, a small local-first AI assistant for a friend who often has multiple things to remember but keeps them buried in messy notes and messages.

Instead of manually organizing those thoughts, they can simply write something naturally, such as:

“Tomorrow I need to finish my DBMS assignment, call Rahul tonight about the project, and pay Ahmed ₹500 on Friday.”

Nudge uses Gemma 3 through Ollama to understand the note and turn it into clear, actionable tasks with deadlines, priorities, and categories.

For example:

• Finish DBMS assignment — Tomorrow — High — College
• Call Rahul about the project — Tonight — Medium — Personal
• Pay Ahmed ₹500 — Friday — High — Finance

The goal isn't to build another complicated productivity app. It is to remove the friction between having a thought and turning it into an action.

Demo

Video Demo: Watch Nudge on YouTube

The demo shows a messy natural-language note being processed locally and transformed into structured, actionable tasks.

Code

GitHub Repository: mohammedabidali1956/Nudge-Hacktoberfest-2026-Build-for-a-Friend-Challenge

The project is intentionally small and focused so that the core AI workflow is easy to understand and reproduce.

How I Built It

Nudge is built around Gemma 3, Google's open-weight model, running locally through Ollama.

The core pipeline is:

User's Note
     ↓
Nudge (Streamlit)
     ↓
Ollama
     ↓
Gemma 3 (1B)
     ↓
Structured JSON
     ↓
Actionable Tasks

The application uses Python and Streamlit for the interface and communicates directly with the locally running Ollama instance.

Gemma handles the core natural-language understanding: identifying individual actions, interpreting relative deadlines such as “tomorrow” or “Friday,” assigning priorities, and categorizing tasks.

For example:

“Tonight call Rahul and tomorrow submit the DBMS assignment.”

becomes structured task data rather than a generic AI response.

I deliberately kept the architecture simple so that the open-weight model is the core of the product rather than an additional chatbot feature.

Why Does Open Innovation Matter?

For Nudge, local open-weight AI is not just a technology choice. It directly affects how the product can be used.

A friend's notes can contain personal plans, payments, relationships, and other private information. With Nudge's local-first architecture, that information does not need to be sent to a proprietary cloud AI API.

Instead, the workflow is:

Friend's Computer
       ↓
     Nudge
       ↓
    Ollama
       ↓
     Gemma

The AI processing can happen directly on the user's computer.

Using an open-weight model also means the AI layer is not permanently tied to a single closed provider. The model can be replaced or upgraded while keeping the core application architecture simple.

For this project, open AI made it possible to build a private, reproducible assistant that can run on a personal computer without requiring API keys or a proprietary AI service.

Prize Categories

Best Use of Gemma

Nudge is built around Gemma 3 as its core natural-language understanding engine.

Gemma performs the task extraction, deadline interpretation, prioritization, and categorization that make Nudge useful.

I am entering Nudge in the Best Use of Gemma category.