This CV Portfolio
A live showcase of modern AI co-piloting
This entire website is a demonstration of resourcefulness. Instead of hand-coding from scratch, I orchestrated advanced AI models to write, refactor, and style the application. This represents the future of software development: directing intelligence to build fast and deliver high quality.
Leveraging AI as a force multiplier—designing structure, aesthetics, and logic through precise prompt direction and QA code review.
Structural Prompting
Directing AI to establish zero-dependency semantic HTML5 layouts.
Aesthetic Design
Instructing models to implement custom variables, light/dark themes, and frosted-glass highlights.
Logic & Integrations
Orchestrating swipe carousel mathematics, state tracking, and live HF model REST APIs.
Radiology AI Summarizer
What It Does
Radiology reports contain detailed clinical observations. This AI tool reads dense findings and automatically generates a concise, accurate diagnostic summary on a CPU.
Spiral Data Classifier
Neural Networks vs. Traditional Math
I built this project to practice building a neural network pipeline from scratch using PyTorch. The challenge is to classify a 3-class spiral dataset (intertwined coordinates resembling pinwheel arms), demonstrating the difference between traditional mathematics and machine learning.
Traditional equations struggle to split these spirals because they are highly curved and intertwined. Instead of manually writing a complex formula, this lightweight neural network learns to draw organic, bending boundaries directly from the raw coordinates through iterative optimization.
Adam Optimizer: Adaptive Speed
By using the modern Adam optimizer, the neural network dynamically adjusts its learning step size for each neuron. It navigates the complex loss landscape efficiently, bending the decision boundary perfectly around the spiral arms in only 600 iterations.
SGD Optimizer: Getting Stuck
A basic training algorithm like Stochastic Gradient Descent (SGD) updates weights with a fixed step size. It struggles to navigate deep valleys in the learning landscape, getting trapped and failing to bend the boundary, even after thousands of epochs.
Linear Boundary: Traditional Math
Without a hidden layer of neurons, the model represents traditional linear algebra. It can only draw straight lines to divide the space. Because spirals are intertwined, straight lines cannot separate them, leaving the accuracy near a coin-toss.
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