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Building a To-Do App with AI Assistance


As developers, we often find ourselves in the cycle of continuous learning. My recent endeavor was to build a To-Do application—a classic project that never gets old. But this time, I had a twist in the tale; I decided to build this app with the assistance of an AI. Yes, you heard it right, an AI partner in coding!

The Idea

The concept was simple: a To-Do List application where tasks can be created, read, updated, and deleted (CRUD). But I wanted to incorporate best practices and containerization with Docker, and get everything right from the get-go. This is where my AI assistant came into play.

The Build

Flask and the Backend

Flask is a micro web framework in Python that is perfect for small-scale projects like a To-Do app. With the AI's guidance, I set up the backend, ensuring a RESTful API design that could handle CRUD operations seamlessly.

Frontend and User Experience

The frontend was kept minimal with HTML and CSS. The AI provided tips on UI/UX design to ensure the application was user-friendly. It suggested a color scheme similar to Trello, a popular project management tool, to give a sense of familiarity to users.

MySQL and Persistence

No To-Do app is complete without a database to store the tasks. MySQL was the database of choice, and Docker helped in setting up a MySQL container. My AI assistant walked me through the integration, ensuring that the tasks persisted across sessions.

Docker Compose and Containerization

Docker compose is a tool for defining and running multi-container Docker applications. With a single command, you can spin up the application with all its service dependencies. The AI helped me write the docker-compose.yml file to define our Flask app and MySQL service.

Challenges and Learning

Every coding journey has its set of challenges. Debugging the connection issues between Flask and MySQL, understanding Docker's nuances, and getting the RESTful endpoints to work were some hurdles I crossed, with AI providing me solutions and explanations at every step.


The result is a functional To-Do application that can be easily spun up on any machine with Docker installed. This project not only reinforced my understanding of Flask, MySQL, and Docker but also showcased the potential of AI in coding assistance.

Future Enhancements

There's always room for improvement. Next steps include adding authentication, allowing multiple users, and deploying the app on the cloud.


A huge shoutout to OpenAI and their AI-powered tools for their assistance throughout this journey. I can confidently say that AI and human collaboration can lead to remarkable achievements in the realm of software development.

Want to Try?

The source code is available on GitHub, and I welcome you to clone, fork, and contribute to the project!
git clone
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Happy coding!

If you enjoyed this article, please like and share. I'm open to discussions and questions in the comments section below. Let's learn and grow together in this ever-evolving field of software development.

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