CODE Magazine

Presenter: John M. Miller When: Wednesday, October 14, 2026, 12:00 pm Central Time (CT) Duration: 75 minutes (approximately) Software archaeology is the disciplined investigation of an existing system.Inherited software is full of valuable but hidden decisions. This practical webinar shows how to use AI as an evidence-driven research partner: trace unfamiliar behavior, distinguish facts from inferences, expose dependencies and risks, and turn discoveries into a prioritized modernization backlog.

AI Makes Your Technical Debt Visible. You Make the Strategic Decisions Laurabeth Barker Mike Yeager Otto Dobretsberger Legacy systems are the silent killers of enterprise innovation. Join this invitation-only working session to turn your technical debt into a strategic asset. You will collaborate directly with peers and industry experts in a private, non-recorded environment to share real-world strategies and build high-level professional connections.

Laurabeth Barker Mike Yeager Otto Dobretsberger Legacy systems are the silent killers of enterprise innovation. Join this invitation-only working session to turn your technical debt into a strategic asset. You will collaborate directly with peers and industry experts in a private, non-recorded environment to share real-world strategies and build high-level professional connections.

Agents Workflows and Orchestration In my last article (The .NET AI Stack, May/June 2026), I introduced Microsoft Agent Framework (AF) as the “spiritual successor” of Semantic Kernel and AutoGen. At that time, AF was a release candidate. As I write this, a few months later, I'm using a very mature v1.15. In that article I talked about Microsoft.Extensions.AI, Semantic Kernel, AutoGen and other parts of the Microsoft AI stack and discussed the basics of Agent Framework for creating agents.

When you move workloads to the cloud, a natural question surfaces early: does moving there actually make things better? The short answer is—it depends entirely on how you build. Plenty of organizations have discovered that lifting and shifting on-premises applications into the cloud without rethinking the design simply moves the same problems to a different location, usually at a higher monthly bill.

Software developers have always looked for ways to move faster. We adopted better editors, source control, build tools, package managers, frameworks, testing libraries, cloud platforms, DevOps pipelines, and static analysis tools. Each generation of tooling promises to reduce friction and help us spend more time solving real problems instead of fighting ceremony. AI-assisted development is the latest step in that long progression. It is also one of the most disruptive.

This article helps you learn how to leverage WebAssembly and .NET to build performant, scalable, cross-platform applications with stellar security. We'll look at Blazor WebAssembly, AOT compilation, and the evolving WASI-based Windows Workloads, explaining how they fit together and why WASM is an appealing option for building compute-heavy and highly responsive applications.

If you've spent any time around large language models (LLMs), you've probably heard the term “fine-tuning” used to mean about six different things. Some people use it to describe writing a really good prompt. Others use it to describe attaching a small classifier on top of a frozen model. Others mean updating every single weight in a 7-billion-parameter model.

Welcome to the era of AI-assisted coding. If you're a developer who loves the speed of thought but hates the friction of syntax, you've likely flirted with AI coding assistants. But today, we're moving beyond the “chat-box-in-the-browser” model. I recently wrote an article about AI-assisted development with Gemini. While I used Gemini, I generically talked about Claude and Codex as side-by-side examples but I chose to use Gemini because you can get started with it for free.

During the process of building software, you may find it necessary to draw the YAGNI card. For those unfamiliar with this acronym, it stands for You Aren't Going to Need It. YAGNI is a tool used to prioritize development resources. It asks the stakeholders whether a feature is a “necessity” or is just a “nice to have.” “Do you really need it?” is the operative question.