Habit Tracker: A Web Application to Track Your Daily Habits
This is a submission for the GitHub Copilot Challenge : New Beginnings What I Built Habit Tracker is a web application designed to help users track their daily habits. The application allows users to register, log in, add habits, mark them as completed, and receive daily email reminders to complete their habits. The application also features a dark mode toggle for a better user experience. Demo You can access the Habit Tracker application here. Below are some screenshots of the application in action: Repo You can find the public GitHub repository for the Habit Tracker project here. Copilot Experience Throughout the development process, I extensively used GitHub Copilot to assist with coding. Here are some ways I utilized Copilot: Prompts: I used natural language prompts to generate boilerplate code for Flask routes, SQLAlchemy models, and HTML templates. Autocomplete: Copilot's autocomplete feature helped me quickly write repetitive code, such as form handling and database queries. Edits: I made edits to the generated code to tailor it to my specific needs, ensuring it met the application's requirements. Chat: I used the chat feature to ask questions about specific errors and get suggestions for improvements. Model Switcher: I experimented with different models to see which one provided the best suggestions for my project. Voice chat: The GitHub Copilot voice chat made asking questions and clarification smooth. GitHub Models I used GitHub Models to prototype LLM capabilities in my app. Specifically, I leveraged the models to generate code snippets for Flask routes, SQLAlchemy models, and email reminder functionality. This significantly sped up the development process and allowed me to focus on refining the application's features. Conclusion Building the Habit Tracker application with the help of GitHub Copilot was an enlightening experience. Copilot's ability to understand natural language prompts and generate relevant code snippets made the development process smoother and more efficient. This project has the potential to help users build and maintain positive habits, ultimately leading to better productivity and well-being. I look forward to exploring more ways to integrate AI-powered tools like GitHub Copilot into my development workflow.
This is a submission for the GitHub Copilot Challenge : New Beginnings
What I Built
Habit Tracker is a web application designed to help users track their daily habits. The application allows users to register, log in, add habits, mark them as completed, and receive daily email reminders to complete their habits. The application also features a dark mode toggle for a better user experience.
Demo
You can access the Habit Tracker application here. Below are some screenshots of the application in action:
Repo
You can find the public GitHub repository for the Habit Tracker project here.
Copilot Experience
Throughout the development process, I extensively used GitHub Copilot to assist with coding. Here are some ways I utilized Copilot:
- Prompts: I used natural language prompts to generate boilerplate code for Flask routes, SQLAlchemy models, and HTML templates.
- Autocomplete: Copilot's autocomplete feature helped me quickly write repetitive code, such as form handling and database queries.
- Edits: I made edits to the generated code to tailor it to my specific needs, ensuring it met the application's requirements.
- Chat: I used the chat feature to ask questions about specific errors and get suggestions for improvements.
- Model Switcher: I experimented with different models to see which one provided the best suggestions for my project.
- Voice chat: The GitHub Copilot voice chat made asking questions and clarification smooth.
GitHub Models
I used GitHub Models to prototype LLM capabilities in my app. Specifically, I leveraged the models to generate code snippets for Flask routes, SQLAlchemy models, and email reminder functionality. This significantly sped up the development process and allowed me to focus on refining the application's features.
Conclusion
Building the Habit Tracker application with the help of GitHub Copilot was an enlightening experience. Copilot's ability to understand natural language prompts and generate relevant code snippets made the development process smoother and more efficient. This project has the potential to help users build and maintain positive habits, ultimately leading to better productivity and well-being.
I look forward to exploring more ways to integrate AI-powered tools like GitHub Copilot into my development workflow.
