In a sea of movie reccomendation algorithms, I wanted to create a project which truely stood out. I wanted to create a project which leveraged skills in data science and web development, inspired by dating apps such as Tinder.

I Implemented a Tinder-style movie recommender web app using machine learning, SBERT vector embeddings, and cosine similarity to dynamically match users with films based on preferences. In addition to that, I added an interactive swipe interface for like/dislike input, updating user taste vectors in real time and filtering results using genre, metadata, and collaborative filtering techniques.

TaleCrafterAI is a generative AI tool used for storywriters to build and write stories. I lead its development as part of my first internship at Zoomob, a video game company which recognized the difficulty in crafting novel based video games.

With TaleCrafter, I showcase my skills building scalable SaaS products form the ground up, from designing the tech-stack, developing authentication and backend systems using Google Oauth, database and storage using Supabase, API calls to Openrouter, to building a beautiful frontend with TailwindCSS, I was tansformed from someone with zero knowledge and experience in web development into one who can now create anything I want with limitless possibility.

Every developer needs their own portfolio to showcase their work. But why limit myself to those plain simple copy-paste portfolios? With the limitless potentials of web development, I decided to create a portfolio truely unique and personal which showcases now only my work, but also who I am as a person.

This project was originally inspired through my first internship at Zoomob, a video game company. However, I was instead tasked to lead the development of TaleCrafterAI. While I didn't work on any games, I was exposed to other work through my peers and developed a keen sense of curiosity in 3D modelling. This inspired me to create this project, where I made everything from scratch including 3D modelling, textures, UV maps and texture baking. I combined this with the skills I learned in web development, combining it all with ThreeJS and animations with GSAP.

This was my first project in web development, and I worked on it in preparation for my first internship. It was a great learning experience, diving into web technologies and applying what I had learned to create something tangible.

I developed a real-time AI tutoring platform where users could chat with an AI about any topic they wanted, offering a personalized learning experience. I implemented full-stack functionality, integrating user authentication, subscription management, and payments using NextJS, Supabase, and Stripe. I also deployed and maintained scalable SaaS features like rate limiting, token tracking, and user session control, ensuring the platform could handle increased usage and provide a smooth user experience.


Note: The website is deprecated due to limitations of using a free database. Storage is reserved for other projects. Contact me if you wish to know more.

In 11th grade, I represented Hong Kong at the International Science and Engineering Fair (ISEF). I traveled to Dallas, Texas, where I presented my project, “Detection of Skateboarding Tricks Based on Gyroscopic Data.” After using a gyroscope and accelerometer to collect data samples of skateboarding tricks being performed, I 3D-printed a small box to hold an ESP32 processor and an MPU9250 gyroscope & accelerometer.

With that, I gathered more than 100 samples of various skateboarding tricks, feeding them into my model, which was trained using algorithms like Support Vector Machines and K-nearest neighbors. The device would detect the tricks and classify them.

Additionally, by collecting data samples of commonly made mistakes, the device can detect mistakes a skateboarder makes during their performance and provides pre-written user-specific feedback through a developed application on what to do to improve.

I was able to take this project a step further, composing a 35-page research paper titled: "Investigating the Accuracy and Prediction Speed of K-Nearest Neighbours and Support Vector Machines on the Classification of Skateboarding Tricks Using Inertial Measurement Unit Signals".