Monty Dean
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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.

Modern Developer Competency

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.

Use the right navigation arrow to explore my other projects! Use the right navigation arrow or swipe the card to explore my other projects!

Radiology AI Summarizer

Hand Coded

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.

Primary Purpose Summarizes radiology findings into clinical impressions
Model Footprint 2.97 GB (Compressed by 68% from 9.3 GB for CPU)
Accuracy Score 0.4455 ROUGE-1 F1 (+156.5% vs Base 0.1737)
Live API Host Hugging Face Space REST API
Paste or type clinical findings to summarize
Synthesizing impressions...
Base Model (ROUGE-1: 0.1737)

Fine-Tuned Model (ROUGE-1: 0.4455)

Spiral Data Classifier

Hand Coded
GitHub

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.

The Challenge Separate 3 intertwined spiral arms of data points
Traditional Math (Linear) Fails: Draws straight lines (51.7% Accuracy)
Model Architecture 2 inputs → 20 hidden units (ReLU) → 3 outputs
Adaptive Learning (Adam) Succeeds: Wraps around spirals (98.0% Accuracy)
Hover to Classify Coordinates
Coord: (x: 0.00, y: 0.00) Class: None
Red (Class 0)
33%
Green (Class 1)
33%
Blue (Class 2)
33%

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.

Training Progress:
Training Progress Animation
Calculated in 1 step (no learning)

Visual Product Recommender

Hand Coded

What It Does

An unsupervised computer vision recommendation system for finding visually similar clothing items from an online retailer store catalog using PyTorch ResNet-50 deep feature extraction and cosine similarity matching.

Vision Backbone ResNet-50 (2048-dim normalized embedding space)
Matching Metric Pairwise Cosine Similarity (>0.70 score)
Live API Endpoint FastAPI REST API on Render (visual-product-recommender-api.onrender.com)
User Interface Interactive Vector Search Engine Demo
Select Query Apparel or Upload Custom Photo
Selected Query Image
Cyan Wrap Dress Preset Catalog Query Item 1
Extracting ResNet-50 2048-dim vectors & ranking similarities...
Top 5 Visually Similar Catalog Matches

UK Public Utility Suite

Live Site

What It Does

A zero-login, client-side web suite protecting UK citizens from compounding fines, 62% tax traps, illegal energy back-billing, and unlawful eviction notices. Built 100% autonomously using AI agents—from statutory web research to WASM code generation, UI compilation, and Vercel deployment.

Role & Architecture Chief Architect & AI Orchestrator
Core Tech Stack React, TypeScript, WASM (Rust/JS), pdf-lib (AcroForms)
Privacy Guarantee 100% Client-Side In-Browser Execution (Zero Server Database)
Statutory Ingestion Renters' Rights Act 2025, MTD ITSA, Ofgem SLC 21BA, UK261

Deep Research & Community Demand Vectoring

Driven by automated AI prompts analyzing community friction across r/UKPersonalFinance, r/HousingUK, r/LegalAdviceUK, and r/DWPhelp:

  • Scraping Real-World Friction: Targeted top 10 statutory bottlenecks where citizens suffer severe financial loss due to confusing math.
  • Statutory Text Ingestion: Encoded UK legal frameworks (Renters' Rights Act 2025, MTD ITSA, Ofgem SLC 21BA, Finance Act 2024, UK261).
  • Fixing Out-of-the-Box LLM Calculation Errors: Solved failures where ChatGPT miscalculates deposit 5-week caps using linear Rmonthly / 4 instead of exact statutory Rmonthly × (15/13), or miscalculates step-function deductions.

Autonomous Agent Build Pipeline

Stage 1

Math Rule Extraction

Prompted AI research agents to output strict JSON schemas of statutory math, non-linear logarithmic curves (SAP 10.2), and date routing.

Stage 2

Deterministic Engine

Compiled routines directly into client-side WASM & TypeScript modules for sub-millisecond execution and zero arithmetic drift.

Stage 3

In-Memory PDF AcroForms

Integrated pdf-lib to auto-populate official court defense forms (Form N11B) and legal dispute letters directly in memory.

Stage 4

Verification & Testing

Prompted verification agents to run edge-case unit tests against HMRC and UK court guidance baselines to guarantee 100% calculation accuracy.

Launch Live App on Vercel (uk-utility-suite.vercel.app)

YieldStorage

Live Site

What It Does

Most storage owners charge the same rent every month of the year — even in summer, when families desperately need storage and would happily pay more. That is like an airline selling holiday tickets at winter prices. YieldStorage watches how full a facility is and nudges prices up when units are nearly full, so owners stop leaving money on the table.

Chart comparing flat rent against peak-season surge pricing; the shaded gap between the two lines is the money left on the table.
The landing-page chart: flat rent vs. surge pricing — the shaded area is money left on the table.
Primary Purpose Dynamic seasonal pricing for independent self-storage facilities
Core Engine Nightly stochastic Bellman rate solver (02:00 AM CST)
PMS Integrations SiteLink, storEDGE, RentManager & Yardi Voyager
Privacy-First Audit 100% In-Browser WASM Revenue Audit (Zero Data Upload)
Visit yieldstorage.com

Private Repository

Due to these projects being created for an ITCareerSwitch course, repository rules require them to be kept as private repos.

If you would like to inspect code architecture or discuss technical implementation, please feel free to reach out via the Contact link.