DevTime: Task Tracking Tool
DevTime is a responsive and Progressive Web App (PWA) designed to streamline project management and task tracking. It harnesses the power of Next.js and React.js for a seamless user experience. With TypeScript, the codebase gains static typing for enhanced maintainability and debugging. Data is stored efficiently in a MySQL database, ensuring reliability and performance.<br/><br/>DevTime empowers Full-Stack Developers, providing a comprehensive suite of features for project organization, task monitoring, and timekeeping. Collaborative tools foster teamwork and efficiency, making it an indispensable tool for professionals.<br/><br/>Join us on a journey to boost productivity and conquer project chaos with DevTime!<br/>
Connect Four Game
I developed a Connect Four game using Next.js, TypeScript, and Tailwind CSS. The idea came to me when my physical Connect Four game broke, and I wanted to be able to play the game even when I had no one to play with. I decided to create a digital version of the game that I could play against the computer.<br/><br/> Using Next.js allowed me to build the game as a React-based single-page application with server-side rendering capabilities. TypeScript was used to add static typing to the codebase, making it easier to maintain and debug. I also utilized Tailwind CSS to quickly style the game's user interface, which saved me a lot of time and effort.<br/><br/> Overall, the Connect Four game was a fun project to work on, and I'm happy to have a working digital version of the game that I can play whenever I want.
Laptop Recommendation System
Laptop Recommendation System is a web application that helps users find the perfect laptop based on their preferences. The idea for this project came about when we realized that many people struggle with finding the right laptop for their needs. To address this issue, we decided to build an AI-powered laptop recommendation system that can analyze users' usage patterns and recommend laptops that meet their specific needs.<br/><br/> To develop this application, we used the Next.js framework with TypeScript for the frontend and Prisma ORM with MySQL for the backend. These technologies allowed us to build a fast, reliable, and user-friendly web application that can make laptop recommendations in real-time.<br/><br/> We started by collecting data on various laptops available on the market, including their specifications and price points. We also gathered information on users' preferences, such as their budget, intended use for the laptop, and desired features. We then trained an AI model to analyze this data and recommend laptops based on the user's specific needs.<br/><br/> The result is a web application that allows users to input their preferences and receive personalized laptop recommendations in seconds. With Laptop Recommendation System, users can make informed decisions when purchasing a new laptop, saving them time and money in the process.
Chat With AI
Chat With AI is a web application that allows users to chat with an Artificial Intelligence. The idea for this project started when we discovered the capabilities of OpenAI's GPT-3.5 language model, which is capable of carrying on natural, human-like conversations and responding to queries in real-time. We wanted to create a web application that could bring the magic of conversing with an intelligent machine to anyone with an internet connection.<br/><br/> To build this project, we chose Next.js as our web framework, as it provides server-side rendering, automatic code splitting, and excellent developer experience. We used TypeScript to add static typing to our codebase, which helps catch errors early in development and improves code readability. For the backend, we used Prisma as our ORM to handle database queries and MySQL as our database of choice.<br/><br/> Our goal was to create a user-friendly and scalable web application that could handle a large number of users and requests simultaneously. With Chat With AI, users can have an engaging and fun experience, whether they want to chat with a friendly chatbot or test the limits of the AI's capabilities. Overall, we are proud of what we have accomplished with this project and hope to continue improving it in the future.
Skincare Recommendation System
Skincare is an important aspect of our daily routine, but it can be challenging to find products that work for our unique skin types. This was the inspiration behind the Skincare Recommendation System, a web application built using Next.js, TypeScript, Prisma, and MySQL. The idea for the project came from a friend who expressed her struggles with finding skincare products that were right for her skin type.<br/><br/> To validate our idea, we conducted extensive research on skincare and collaborated with experts in the field. Through our research, we discovered that many people faced the same challenges when it came to finding the right skincare products. With this in mind, we set out to create a user-friendly web application that would diagnose users' skin types and provide personalized product recommendations based on their unique characteristics.<br/><br/> To build the Skincare Recommendation System, we utilized Next.js, a popular and powerful React framework that allowed us to build fast and scalable web applications. We also used TypeScript, a superset of JavaScript that provided us with type safety and helped us write cleaner and more maintainable code. Prisma, a modern ORM for Node.js, was used to manage our database, while MySQL was used to store our application data.<br/><br/> The Skincare Recommendation System has become a useful tool for people looking to improve their skincare routines. By providing personalized product recommendations, we hope to make it easier for people to find products that work for their unique skin types. Overall, our goal was to create a reliable and intuitive web application that would help people achieve healthy and glowing skin.
Chicken Disease Diagnosis
The idea for Chicken Disease Diagnosis app started when we realized that chicken farmers often struggle with identifying and treating diseases in their flock. We wanted to create a solution that could help farmers quickly diagnose and treat their chickens, potentially saving them time, money, and resources. To bring this idea to life, we collaborated with veterinary experts and used their knowledge to build an AI-powered application that could analyze a flock's symptoms and provide accurate diagnoses in real-time.<br/><br/> We chose to build the application using Next.js, TypeScript, Prisma, and MySQL. Next.js allowed us to build a fast and scalable web application, while TypeScript helped us catch errors early and write more reliable code. Prisma and MySQL were used to store and manage the data necessary for the application.<br/><br/> Overall, Chicken Disease Diagnosis app aims to be a powerful and essential tool for any chicken farmer looking to keep their flock healthy and productive. By leveraging the power of AI and veterinary expertise, we hope to provide farmers with a reliable and effective way to diagnose and treat diseases in their chickens.