About Me
A. H. Malik (aka Malik Sahab) is a dynamic electrical engineer with a passion for expanding his horizons. His diverse interests span across game development, generative AI, website design and blockchain technology. Always eager to learn and innovate, Malik is dedicated to exploring new frontiers and delivering exceptional results.
In addition to his professional pursuits, Malik is an avid gamer and YouTube content creator. He enjoys sharing his expertise and engaging with his audience, creating a vibrant online community. His dedication to his craft is evident in the quality of his work and the enthusiasm he brings to each project.
When he is not immersed in his various pursuits, Malik finds solace in the company of his beloved cats. Their presence brings balance to his busy life, allowing him to recharge and refocus. With a commitment to continuous learning and a genuine love for his work, Malik Sahab is a true innovator in his field.
Projects Showcase
Here are some of the projects I have worked on. These include projects from Unreal Engine, Artificial Intelligence, Next JS and more.
A collection of various Melee Sci-Fi Weapons, perfect for your next Hack n Slash Game
Simple procedurally generated Infinite Tiles for your next looping visuals
A simple portfolio website built using Next JS. Also includes Tailwind CSS and Shad CN
Use a locally hosted Large Language Model (LLM) to power NPC conversations in Unreal Engine
A CNN model that detects lung cancer from chest X-ray images. Trained on a very small dataset of lung cancer images
An E-Commerce website built using Next JS. Also includes Tailwind CSS, Shad CN, Drizzle ORM, Stripe and Clerk
A personal game project I'm working on. Uses Unreal's Gameplay Ability System to create a unique abilities for player.
A UE Plugin that is supposed to make your life easier while using Unreal's Gameplay Ability System.
Trailer for my personal game project, showcasing some of the gameplay elements
This is a project that uses YOLOv5 model to detect objects in an image. It also crops the detected objects into separate images. Additionally, it can fetch images from URLs for object detection
Predict the yield of wild blueberries using machine learning techniques. The evaluation metrics is RMSE score
