Let's build software people dream of using.
Practical notes on software architecture, .NET, containers, AI-assisted development, and the side projects that keep engineering fun.
March 19, 2026
I had the day off for spring break to spend time with family, but everyone was still asleep and I had a quiet block of time in the morning. Instead of scrolling, I decided to use that time to level up my AI workflow. Over the last few weeks, GitHub Copilot has shifted for me from a tool that answers questions and occasionally completes code to something closer to an entry-level co-contributor.
What you'll find here
Practical DX
AI workflows and developer productivity
Notes on using Copilot agents, CLI workflows, and implementation experiments that save real engineering time.
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architecture, containers, AI, and projects
Hands-on
implementation-first writeups with concrete examples
I had the day off for spring break to spend time with family, but everyone was still asleep and I had a quiet block of time in the morning. Instead of scrolling, I decided to use that time to level up my AI workflow. Over the last few weeks, GitHub Copilot has shifted for me from a tool that answers questions and occasionally completes code to something closer to an entry-level co-contributor.
I’ve been trying to use Copilot more for focused workflows lately, and one of the best upgrades is creating custom agents for specific tasks. Instead of re-explaining context in every prompt, I can encode behavior once in an agent profile and reuse it. In this article, I'll share how I created a custom Copilot agent for authoring blog post which actually helped me with the rough draft of this blog article.
Generative AI is reshaping software development and becoming a tool that is rapidly expanding in capability. As such, I plan to spend more time on learning and implementing AI-related functionality over the coming year. I'm going to make an LLM that translates a statement to pig latin.
For years, I've been using open api generator or NSwag to generate HTTP clients for my projects. While this work well, there were edge cases I felt were hard to satisfy. Recently I learned about Kiota to generate API clients, and it takes a much different approach that I think is better.
I've been taking a break for a while from my blog. I had been spending some extra time outside of work trying to push forward an initiative inside work to move my workplace forward on containers. It's just been one of those things I personally find important in technical leadership.
It's getting close to November, and that means a new .NET release is about to be released. While there are a lot of features that deserve attention, there is one feature that is one of my favorites as it's going to reduce the amount of code I need to write. That feature is primary constructors.
When writing automated tests, one of the hardest parts of is when integration testing and external dependencies are required-- like a database or external service. While running these things may not be hard an and of itself, maintaining consistency between the tests and the dependencies can be difficult. Recently, I've been working on a legacy data access library that needed some refactoring to get to .NET 6 from .NET 4.8.
In my pursuit of refining software development methodologies, I have put a few artificial intelligence (AI) assistants to the test. Initial experiences, were met with mixed results. However, they are continually making progress.
Many branching strategies work to split up work into smaller pieces that are regularly integrated into a mainline branch. However, often time it is difficult to do this with much larger tasks. I want to highlight a common software development strategy used to overcome this: Branching by Abstraction.
Every year I try to pick up a few engineering books and read them. I decided to look through my bookshelf and compile a list of some of my favorites I've gone through in the past few years and record them in a blog post. Hopefully in a few more years, I will do this again.
