TidyMind AI：本地 AI 生產力助理｜Hacktoberfest 第 3 週

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

What I Built

I built TidyMind AI, a privacy-focused productivity assistant designed to reduce the time and effort spent organizing everyday tasks, notes, and files.

We often waste time sorting through messy to-do lists, extracting important points from long notes, and deciding how to organize files. TidyMind AI aims to make these repetitive activities easier with the help of open-source AI.

The application helps users:

• Organize tasks: Turn an unstructured list of tasks into categorized, prioritized, actionable items.
• Summarize notes: Extract key points and action items from long text.
• Organize files: Preview suggested file categories and filename changes before applying them.
• Track activity: Review previous operations and manage history.
• Work with privacy in mind: Use a locally running AI model instead of relying on a third-party AI API for inference.

My goal is simple: spend less time organizing work manually and more time actually getting things done.

Demo

Live demo: https://youtu.be/7x0-tReEUZk

Screenshots:

If a public deployment is not available, I will provide screenshots or a short video demonstration showing the main features.

Code

GitHub repository: https://github.com/arya179/tidymind-ai

The project is built with a simple full-stack architecture that separates the user interface, backend logic, and AI integration.

I am building TidyMind AI as an open-source project so other developers can explore the implementation, contribute improvements, and adapt it to their own workflows.

How I Built It

I used Antigravity IDE as my development environment and built the application incrementally, starting with the dashboard and then implementing the core functionality.

Technology stack:

• React + Vite: Frontend and interactive user interface.
• Tailwind CSS: Responsive styling and interface design.
• Python + FastAPI: Backend API and request validation.
• Ollama + Gemma: Local inference using an open-weight AI model.
• SQLite: Local activity history, if enabled in the implemented version.
• Git + GitHub: Version control and open-source collaboration.

The AI integration is designed to send requests from the frontend to the backend, which communicates with the locally running model through Ollama.

For task organization, the model receives an unstructured list and returns structured results that the application validates before displaying them.

For file organization, the application is designed around a preview-first workflow. Users can review proposed changes before confirming them, reducing the risk of accidental file modifications.

I am also including a demo mode so the interface can be explored without requiring an AI model to be installed.

Why Does Open Innovation Matter?

Open innovation matters because productivity tools should be adaptable, transparent, and accessible.

Using an open-weight model gives this project the flexibility to run inference locally, customize prompts, experiment with different models, and avoid depending entirely on a paid third-party AI API.

It also makes the project easier for other developers to learn from and extend. Developers can inspect the code, understand how AI responses are handled, and improve the application through community contributions.

Local inference can also help keep user-provided text on their own machine, provided the application is configured to use local services and does not send that data elsewhere.

For me, this challenge is an opportunity to explore how open-source AI can make small, repetitive tasks easier without requiring every user to depend on a closed AI service.

My Agent Session

I used Antigravity IDE to build TidyMind AI step by step, from project setup and dashboard development to backend integration and testing.

I used an iterative development workflow: implement one feature, test it, fix issues, and then continue to the next milestone.

The Lazy Payoff

TidyMind AI is designed to reduce the manual effort involved in organizing tasks, extracting action items from notes, and planning file changes.

Instead of manually rearranging a long task list, users can get a structured starting point from AI and review it before making decisions.

I plan to measure the actual time saved by comparing manual organization with the application's results. I will avoid claiming specific time savings until I have tested the workflow with real usage.

Prize Categories

• Best Use of Gemma — if TidyMind AI uses Gemma through Ollama for its AI-powered task organization and summarization.

Thanks to the Hacktoberfest community for encouraging developers to experiment with open-source AI and build practical tools that make everyday work a little easier.