I document what I learn while building real-world software — from architecture and distributed systems to the engineering decisions behind them. This is where I share my experiments, lessons, and projects as I grow as an engineer.
F1Predictor (GridMind) is a .NET 10 solution built using Clean Architecture, which utilises ML.NET classifiers to predict podium finishes and points standings in Formula 1 races. The application features live standings and includes a Monte Carlo simulation for title predictions. It has a frontend developed with Next.js and React, while the backend is powered by PostgreSQL and Entity Framework Core.
A .NET global tool that quickly scaffolds production-ready Clean Architecture projects featuring CQRS, the Result pattern, domain events, and FluentValidation.
My personal portfolio site to showcase my skills and projects. Designed with attention to detail and built with Next.js, TypeScript, and Tailwind CSS for a modern, responsive experience.
I explore how I learned machine learning by building an F1 race prediction model in C# using ML.NET, OpenF1 data, and familiar software engineering principles.
Stop guessing which C# keywords to use. Master the CIDIS model to choose between class, record, interface, internal, and sealed with confidence. Learn how focusing on these C# fundamentals reduces bugs, improves code quality, and helps your team release faster.
A comprehensive comparison of JWT and cookie-based authentication, exploring how each works, their security implications, and when to use each approach in modern web applications.