N-CODE LazyFlow AI: Making Everyday Tasks Easier with Open-Weight Gemma

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

What I Built

I built N-CODE LazyFlow AI, an open-source productivity assistant powered by Google's open-weight Gemma 3 model and Ollama.

LazyFlow helps people turn overwhelming to-do lists into manageable daily plans.

Instead of manually organizing tasks, users can enter their tasks, select their energy level, and choose how much time they have available.

The AI then generates prioritized activities, realistic time allocations, a small first step, and suggestions for tasks that can wait.

The goal is to reduce the effort of planning and make everyday work feel more manageable.

Demo

View verified AI demo and download recording

GitHub Repository:

https://github.com/Deshan217/ncode-lazyflow-ai-hf26

Verified Real Gemma AI Test:

https://github.com/Deshan217/ncode-lazyflow-ai-hf26/actions/runs/39414746192

Real AI Output Artifact:

https://github.com/Deshan217/ncode-lazyflow-ai-hf26/actions/runs/39414746192/artifacts/12260269969

This proof of concept runs locally using Ollama and Gemma 3. The linked GitHub Actions workflow successfully executed the real model through the application API.

The application is designed for local execution rather than a publicly hosted AI service.

Code

The complete project is open source under the MIT License.

Source Code:

https://github.com/Deshan217/ncode-lazyflow-ai-hf26

The repository includes the application, responsive dashboard, local AI integration, automated tests, and GitHub Actions verification.

How I Built It

I used a simple, lightweight stack:

• Python standard library for the local HTTP server.
• HTML, CSS, and JavaScript for the responsive dashboard.
• Ollama for local AI inference.
• Gemma 3 1B as the open-weight language model.
• GitHub Actions for automated testing and real AI verification.

How It Works

1. The user enters an unorganized task list.
2. The user selects an energy level and available time.
3. The Python server validates the input.
4. The application sends a structured prompt to Gemma through Ollama.
5. Gemma generates a JSON-based action plan.
6. The application validates the response and adjusts time allocations to fit the selected budget.
7. The user receives a daily plan and can export it as Markdown.

Real AI Verification

On October 20, 2026, GitHub Actions successfully executed Gemma 3 1B through Ollama.

The workflow started the local application, submitted a planning request to its API, and verified that a nonempty AI-generated plan was returned.

Automated unit tests and syntax checks also passed.

The evidence is publicly accessible through the repository's GitHub Actions history.

Why Does Open Innovation Matter?

Open-weight AI allows developers to build useful applications with more control over inference and deployment.

Using Gemma through Ollama enables local AI processing without requiring a proprietary cloud language-model API.

For LazyFlow, this means users can keep their task lists on their own machines while still benefiting from AI-assisted organization.

The project also demonstrates how open-source tools can lower the technical barrier to experimenting with useful AI workflows.

The Slow and Steady Approach

LazyFlow focuses on manageable progress rather than unrealistic productivity goals.

It encourages users to start with a small action, prioritize achievable tasks, and work within their actual available time.

The project's effectiveness in saving time has not yet been measured through formal user testing.

Challenges and Lessons

One challenge was ensuring that the AI returned useful structured data rather than free-form text.

I addressed this by using structured JSON generation and validating the model's output before presenting it in the application.

Another challenge was verifying real open-weight AI inference rather than relying exclusively on mocked test data.

GitHub Actions provided a repeatable way to install Ollama, run Gemma, and capture genuine model output.

What's Next?

Future improvements include accessibility enhancements, optional local task history, better plan editing, and feedback from real users.

My aim is to continue improving LazyFlow as a practical, privacy-conscious productivity tool.

Built by N-CODE STUDIO for Hacktoberfest 2026.