LLM: HuggingFace's Llama-3.1
Front-End: React
Back-End: Node.js and Express.js
Cloud: AWS Aurora for storing user login information and AWS S3 for storing trading data.

The app enables users to track their trading journey and analyzes their trading patterns using an in-house large language model (LLM) to provide personalized insights and suggestions for improvement. Additionally, the app fetches real-time stock market pre-analysis through API calls to Google Studio's LLM.

Used Nodejs, Express.js, and Nodemailer to build and run cronjob email bot that sends alerts periodically based on AI-generated content.

Used Java Maven, Java Swing and OOP to build a small cards betting game.

Used Bootstrap to build a quick moving company webpage.

Led a team of three to build a dart score record. Used a full cycle development by applying agile methodology priciples. Used Java Maven to build this project, Java Swing to build Gui and github as a central repository to deliver work.

Built a language learning app using Java Maven, Java Swing and OOP. The app's design allows for scalibility.

Built a dog's tinder webpage using Bootstrap, html and css.

Built a birthday invite card using html

Developed a full-stack dice game application with a front-end built using HTML, CSS, and vanilla JavaScript, incorporating Object-Oriented Programming (OOP) principles. The back-end was implemented using Spring Boot and Java, featuring basic user authorization for secure access. Docker was used for containerizing and deploying the application.

Built a Simon game using HTML and CSS for the design, jQuery for manipulating webpage elements, and JavaScript for the game logic.

Developed a webpage that fetches the number of cities around the world sharing the same name. The technologies used include EJS for templating, CSS for styling, and Node.js with Express.js for the backend. Data is fetched using a REST API from OpenWeatherMap.