Code Compass

Introduction In today’s fast-moving world of software development, Continuous Integration (CI) and Continuous Deployment (CD) are key to building and releasing updates quickly and reliably. For Business Central developers, AL-Go for GitHub makes this process easy by using GitHub Actions to automate builds, testing, and deployments. It comes with ready-to-use templates that help you set up a complete CI/CD pipeline in minutes so you can focus more on coding and less on managing workflows. Each build typically takes around 20 minutes when using containers, which fits comfortably within GitHub’s free limits for most projects 2,000 minutes per month for private repositories on the Free tier, 3,000 minutes on the Team tier, and up to 50,000 minutes on the Enterprise tier. Since public repositories get unlimited free minutes, AL-Go for GitHub offers an affordable and powerful way to maintain professional DevOps practices without extra costs. Overall, it’s a simple, cost-efficient, and deve...

Introduction When working on big projects, it can be hard to quickly find classes, methods, or properties spread across different libraries. Searching through files one by one takes too much time. This is where metadata search in Visual Studio helps. It lets you explore and navigate types from external libraries (like .NET assemblies or NuGet packages) even if you don’t have the source code. Visual Studio creates a readable view of the compiled metadata, so you can instantly see definitions and details, almost like built-in documentation. References Metadata search - Visual Studio Usage In Visual Studio, go to Dynamics 365 > Metadata Search  OR  Ctrl + R, Ctrl + S . Start typing the name of the element you are looking for. Results appear as you type. Double-click a result to go directly to that metadata or code. You can also right-click to add items to your project. You can filter via the follow criteria: Name : Search by element name (default).  Type : Limit by type, ...

Introduction Software development has been around since the 1940s. We started with punch cards, then machine language, followed by assembly, high-level programming languages, low code, no code, and now AI-assisted coding. Along the way, several tools have been developed to make programmers' jobs easier, from card sorters and verifiers to debuggers and IDEs. Now, with the advent of AI, we have large language models (LLMs) writing code for us, but I don't think it's quite there yet. In this article we'll see how AI assists developers, what it can do for us today, its limitations, and where it's headed. The concept of AI began in the 1950s when researchers tried to imbue machines with the magic to think. Early systems followed set rules, but as computers improved and data became more available, smarter methods emerged, such as machine learning, natural language processing, and neural networks. Large Language Models (LLMs) grew from these advances, using huge amounts of...

Introduction In our small development team, we usually merge code without formal pull requests. Instead, changes are committed directly by the developer responsible for the project, and while I don’t need to approve every change in my role as the senior developer, I still need to stay aware of what’s being merged.  Manually reviewing each commit was becoming too time-consuming, so I built an automated process using Power Automate, Azure DevOps, and Azure AI. Now, whenever a commit is made, it triggers a workflow that summarizes the changes and sends me an email. This simple system keeps me informed without slowing down the team’s work. Although I kept the automation straightforward, it could easily be extended further. For example, it could be improved to allow me to reply directly to the committer from the email or even display file changes in detail using a text comparison feature in Outlook. We didn’t need that level of detail, but it’s a good option if deeper insights are ever ...