I’m a frontend software engineer who enjoys turning complex ideas into elegant, intuitive digital experiences. My work brings together software development, teaching, research, and entrepreneurship, with a particular enthusiasm for TypeScript and the possibilities of AI.
I work as a Software Engineer (Front-end) at Rightmo Web Solution & Progress Partners and as a Senior Lecturer at Epic Learn Institute of Higher Education. Building software and helping others understand it are equally important parts of who I am.
THE STORY BEHIND THIS WEBSITE
Why I built Online Web Toolkit
Online Web Toolkit began with a task I needed in my own day-to-day work: turning Markdown into PDF documentation. I built a small command-line tool to handle that conversion. It was a practical way to take the notes and documentation I was already writing and turn them into documents I could share.
That first utility became the starting point for a broader idea. Alongside documentation, I needed to prepare presentations, explain technical decisions, and put together guidance for my team. I wanted to bring those recurring tasks into one accessible website and make the tools useful to other people, too.
FROM A PERSONAL TASK TO A SHARED RESOURCE
01 / THE STARTING POINT
A tool for myself
A personal CLI that turned Markdown into PDF.
02 / EVERYDAY WORK
Part of the workflow
PDF documentation, presentations, and guidance for the team.
03 / SHARED ON THE WEB
Online Web Toolkit
A shared website for tools that grew out of everyday needs.
Original vector illustration of the toolkit’s progression, from a personal Markdown-to-PDF CLI to a shared utility website.
Today, Online Web Toolkit is a working collection of web utilities for everyday tasks, including PDF document creation and Markdown presentations. What started as a personal CLI has grown into a shared resource that people can open in their browser and use in their own workflows.
For me, it is also a way to put my frontend engineering skills into practice. Building the toolkit means thinking through the whole experience: how someone finds a tool, understands its controls, checks the result, and takes it into their work. I want the care in those details to be as visible as the functionality itself.
Building a product means thinking about the people who will use it, as well as the code that makes it work. My independent projects give me room to bring those decisions together.
The AI benchmark article is a working example of the interface decisions behind this journal. It connects reading a claim with inspecting the evidence and trying your own assumptions.
01
Choose a question
Benchmark controls expose their selected state and update a labeled comparison.
02
Inspect the evidence
A native disclosure opens a table with row and column headings. Source conditions stay beside the chart.
03
Try your assumptions
The calculator validates inputs and calculates locally, including review time and failed attempts.
I craft frontend experiences with an emphasis on clarity, thoughtful interaction, and maintainable code. React.js, Next.js, and TypeScript are central to the way I build for the web.
EDUCATION
Senior Lecturer
Epic Learn Institute of Higher Education
I lecture in React.js, Next.js, TypeScript, and other frontend technologies. My focus is helping students connect the concepts they learn with the decisions they make while building applications.
Research, development, and a TypeScript mindset
R&D gives me room to question familiar approaches and explore new ones. My interests include AI research, agentic coding, and prompt engineering: how these tools fit into software development, where they help, and how to assess their output with care.
As a TypeScript enthusiast, I value making assumptions explicit. That same habit shapes how I approach unfamiliar technology: understand the problem, examine the evidence, and keep a clear distinction between a promising idea and a demonstrated result.
Technologies and areas I work with
AI & research
Agentic Coding
Prompt Engineering
AI Research
Python
Frontend & mobile
Next.js
React.js
TypeScript
JavaScript
React Native
Astro
Tailwind CSS
Backend & data
Node.js
Express.js
Laravel
MongoDB
PostgreSQL
Design & components
Figma
Storybook
Build & delivery
Docker
npm
pnpm
Vercel
Cloudflare
Education
Coventry University, UK
I graduated with a First Class Division. Continuing to learn remains part of both my engineering practice and my work as a lecturer.
Why I write this journal
I write for developers and students navigating changes in software and AI. I want each article to offer something useful: a clearer explanation, a practical method, or a resource you can adapt to your own work.
Sources, evidence, and AI assistance
Trace claims to their sources. Research papers, official documentation, and published evaluations are linked so you can inspect the evidence. Charts include relevant conditions and limitations.
Separate analysis from firsthand results. Published model benchmarks are attributed to their publishers. They are not presented as my own experiments. Forecasts, fictional scenarios, and synthetic examples are labeled.
Make the practical contribution inspectable. The AI series includes formulas, downloadable worksheets, and test fixtures that explain the reasoning behind the guidance.
Be transparent about assistance. AI was used for source discovery, drafting, review assistance, code, and graphics in the AI series. Article-level notes explain that process and its limitations.
Corrections and reader feedback
If you find an incorrect value, a changed source, or an unclear comparison, contact me on LinkedIn. Include the article URL, the exact passage, and a supporting source or suggested correction. Please leave out private student, customer, or employer information.
Substantive corrections should be recorded in the article’s revision note, with the modified date updated and the original publication date preserved.
