It has been a while since I posted an update here. Once again, life and work have been moving faster than my writing. Still, the quiet blog has not meant a quiet few years. I have been building software, changing roles, leading teams, and learning more than ever.
Most recently, I have also been exploring AI. Not just using it to answer questions, but thinking seriously about how it changes the way we design, build, and maintain software.
So, where have I been, what am I doing now, and why am I so excited about AI?
Where I’ve Been
When I last wrote an update in 2022, I was wrapping up my time at Progress. Working with Telerik products gave me broad exposure to the .NET ecosystem, from web and desktop applications to reporting and developer tooling. It also gave me opportunities to write, teach, troubleshoot difficult problems, and contribute to Fiddler Everywhere.
After Progress, I joined Temple Logic, where I worked on full-stack applications using ASP.NET Core, Entity Framework, and Angular. I later moved to SBLI, building applications with .NET, Blazor, and Azure while also supporting and modernizing legacy systems.
In late 2023, I joined Moore through what had previously been Temple Logic. I came in as a senior software engineer and team lead and have since expanded my responsibilities into engineering management.
That progression has changed the scope of the problems I work on. I still enjoy writing code and working through architecture, but I now spend more time helping teams make good technical decisions, mentoring engineers, improving delivery, and connecting business needs to maintainable software.
The systems have become more complex too. My recent work has included modern .NET and Angular applications, Azure infrastructure, CI/CD pipelines, distributed data sources, legacy modernization, authentication, and cloud architecture. I have learned that good engineering is rarely about choosing the newest technology. It is about understanding the problem, the constraints, and the people who will need to live with the solution.
What I’m Doing Now
Today, I am a Manager of Engineering at Moore. It is a new chapter, but it feels like a natural continuation of what I have been doing for years.
I am leading and mentoring engineers while remaining close to architecture and development. That balance matters to me. I want to help teams grow and deliver, but I also want to understand the systems, participate in technical decisions, and keep learning alongside the people I lead.
Much of my work sits at the intersection of application development, data, cloud infrastructure, and business operations. Some of that work involves modernizing established systems, but I have also been building greenfield applications, including new applications that use AI. The mix gives me opportunities to solve new problems without losing sight of the realities of integrating with existing systems and business processes.
My technical horizons have expanded along the way. .NET, Angular, and Azure remain important parts of my work, but I am also working across other languages and ecosystems, including Python, Docker, and Kubernetes. Moving between application code, AI services, containers, infrastructure, and data has given me a broader view of how modern systems fit together.
Whether a system is new or established, the difficult parts are rarely limited to writing code. The work crosses organizational and technical boundaries and requires judgment, collaboration, and an understanding of the people who will depend on what we build.
That environment is also where AI has become especially interesting.
Why I’m Excited About AI
I have seen plenty of technology trends arrive with enormous promises. AI certainly has its share of hype, but something about this moment feels different.
My hands-on experience with AI began in 2023, when I built a sales assistant using OpenAI. It was an interesting project and, at the time, an impressive example of what a large language model could do. The assistant could engage with a user conversationally and follow a detailed set of instructions to support a sales workflow.
But underneath that experience, it was still a really, really smart chatbot with instructions. It could carry on a convincing conversation, but it had a narrow purpose and depended heavily on the information provided in the prompt. It did not have a deep understanding of the surrounding systems, independently gather the context it needed, or take meaningful action through other tools.
That project helped me see both the potential and the limitations of the technology. A good conversational interface was useful, but the more interesting question was what might happen if an AI system could securely access relevant knowledge, interact with software, and work through a larger problem while remaining accountable to a person.
AI tools are already useful in my daily work. They can help explore an unfamiliar codebase, draft tests, explain an error, compare implementation options, summarize technical context, and turn a rough idea into a starting point. Used well, they reduce the time between having a question and being able to test an answer.
What excites me most, however, is not faster code generation. It is the possibility of giving software access to the context and tools it needs to help solve larger problems.
I have been exploring AI-assisted development, retrieval-augmented generation, tool calling, the Model Context Protocol, and agentic workflows. I am particularly interested in systems that can reason across requirements, source code, documentation, and data dependencies. That opens the door to tools that can help with planning, estimation, system discovery, modernization, and other work that depends on understanding more than a single prompt.
This is where AI becomes an engineering problem rather than a novelty. How do we provide the right context? How do we verify an answer? How do we protect sensitive data? How do we make the system observable and predictable? Where should a person remain in the loop? How do we measure whether it actually improves an outcome?
Those questions are far more interesting to me than adding another chat window to an application.
A Tool, Not Magic
I am excited about AI, but I am not under the illusion that it removes the need for software engineers.
AI can produce an impressive answer that is incomplete, insecure, or simply wrong. It can accelerate good decisions, but it can also accelerate bad ones. The quality of the result still depends on context, clear thinking, technical judgment, and verification.
In my experience, the best results come when AI is treated like a capable collaborator whose work must be reviewed. You give it a clear problem, the relevant context, and access to appropriate tools. Then you test what it produces and remain accountable for the outcome.
That does change the job. The ability to define a problem, navigate a system, evaluate tradeoffs, and recognize a plausible-looking mistake becomes even more valuable. Engineers will still need strong fundamentals. We may simply spend less time typing routine code and more time directing, reviewing, integrating, and improving the work.
I find that energizing.
What’s Next for This Blog
This site has always reflected whatever I am learning at the time. That has included .NET, Linux, self-hosting, cloud platforms, development tools, and digital ownership. Those interests have not disappeared. AI now connects with many of them.
I plan to use this blog to document what I am building and learning: practical AI-assisted development workflows, agents, RAG, tool calling, MCP, application architecture, and the lessons that come from trying these ideas on real engineering problems.
I also want to write honestly about what does not work. AI is moving quickly, and it is easy to confuse an impressive demonstration with a dependable system. Finding the difference is part of the fun.
It has been a busy few years. I have moved from individual contribution into technical leadership and management, and from being curious about AI to actively building with it.
There is a lot left to learn. That is exactly why I am excited.
As always, keep learning and keep building.