C# Programming Language Articles

C# is Microsoft’s modern, statically-typed language for .NET development. This collection explores C# features, language evolution, practical patterns, and techniques for writing clear, efficient code in the C# ecosystem.

Language Evolution and Features

C# has evolved significantly since its creation, adopting features from other languages and innovating independently. Modern C# (10+) includes records for immutable types, nullable reference types for null-safety, pattern matching for elegant code flow, and async/await for asynchronous programming.

Records provide concise syntax for immutable data types with structural equality, ideal for domain models and data transfer objects.

Nullable Reference Types make null-safety explicit at compile time, preventing the infamous “billion-dollar mistake” of null reference exceptions.

Pattern Matching enables elegant code for type checking, null checking, and property matching without verbose if-else chains.

Async/Await abstracts the complexity of asynchronous programming, enabling responsive applications without callback hell.

Practical C# Development

Articles in this section cover language features, effective C# patterns, performance optimization, LINQ mastery, and integration with .NET libraries. Topics include null handling strategies, immutability patterns, error handling approaches, and leveraging type system features.

The goal is writing C# code that’s not just correct but also expressive, maintainable, and performant—code that communicates intent clearly and resists bugs through type safety and language features.

TUnit.Mocks: No Castle, No Reflection, No Drama

Most .NET test projects rely on NSubstitute and Castle.DynamicProxy. That foundation is cracking: NativeAOT breaks it, trimming strips it, cold-start costs accumulate. TUnit.Mocks takes a different route: a source generator emits typed mocks at compile time, IService.Mock() (or Mock.Of<T>()) is the entry point, runtime reflection is gone.

The Codebase Doesn't Know You Quit

The first four parts of this series treated legacy as something between me and myself: Past Self leaving code for Future Self, with an AI in the middle. That framing is incomplete. Code outlives employment, not just memory. The companies I worked for have forgotten most of what I did there. The repositories haven’t. This is the fifth part of the Code as Legacy series, about the legacy you leave when you’re not around to defend it.

The Machine Writes. The Legacy Is Still Mine.

Part three ended with me promising to stop adding to Past Self’s pile. I was already wrong. I’m not the only author anymore: Copilot and Claude finish methods before I’ve finished thinking, shipping code under my name with my git config as the committer. This is part four of the Code as Legacy series, about what changes when the author is partly a machine.

You're Shipping Bugs Faster, and Your Tests Are Helping

AI coding agents make you faster. They also make your bugs faster: they generate plausible-looking code, you review quickly, the tests pass, and production breaks two weeks later. I keep falling into this pattern myself, always on the happy path, telling myself the edge cases come later. They don’t. Here’s the pattern, why most test frameworks make it worse, and how TUnit fights it.

I'm Done Making Empty Promises

I’ve spent two articles in this series describing Past Self and the damage he left behind. What I haven’t said yet is the uncomfortable part: I am Past Self, right now, making the same promises he made. // TODO: fix this properly. We’ll clean that up after the release. I’ll write the tests when there’s time. This is the third part of the Code as Legacy series, and I’m deciding to stop.