This week's collection of interesting articles and resources covers AI development, DevOps practices, and open source tools. From GitHub Copilot customization to local AI deployments and containerization best practices, here are the highlights worth your attention.
Top 5 MCP Server Best Practices (Ivan Pedrazas) - mCP server is a very hot topic, thinking about riding your own, here are five best practices to make sure you will be successful.
Containerize Your Apps with Ask Gordon (Steve Buchanan) - I already have Docker desktop on my Windows pc, I should definitely give Gordon a try more to come
DevOps
Local Deploy with Bicep (Sam Cogan) - A perfect short story, I'll explain why the hell bicep can now deploy locally and how to do it
Open Source
Introducing OpenCLI (Patrik Svensson) - A standard that describes CLI so both humans and agents can understand how it works. Love it!
Getting a complete CI/CD pipeline for your .NET Aspire solution doesn't have to be complicated. I've created a template that gives you everything you need to get started in minutes.
Replace the sample project with your own .NET Aspire code
Push to your GitLab repository
Watch your CI/CD pipeline run automatically
That's it! You immediately get automated builds, testing, and security scanning.
Pro Tip: The best time to set up CI/CD is when you're just starting your project because everything is still simple.
Part 2: Building the Template with GitLab Duo
Now let me share my experience creating this template using GitLab's AI assistant, GitLab Duo.
Starting Simple, Growing Smart
I didn't build this complex pipeline all at once. I started with something very basic and used GitLab Duo to gradually add features. The AI helped me:
Add secret detection when I asked: "How can I scan for accidentally committed secrets?"
Fix test execution issues when my unit tests weren't running properly
Optimize the pipeline structure for better performance
Working with GitLab in VS Code
While you can edit .gitlab-ci.yml files directly in GitLab's web interface, I prefer VS Code. Here's my setup:
Install the official GitLab extension from the VS Code marketplace
Once you've signed in, this extension gives you:
Direct access to GitLab issues and work items
AI-powered chat with GitLab Duo
GitLab Duo in Action
GitLab Duo became my pair programming partner. Here's how I used it:
Understanding Code: I could type /explain and ask Duo to explain what any part of my pipeline configuration does by highlighting that section.
Solving Problems: When my solution didn't compile, I described the issue to Duo and got specific suggestions. For example, it helped me realize some projects weren't in .NET 9 because dotnet build required the Aspire workload. I could either keep my project in .NET 8 and add a before_script instruction to install the workload or upgrade to .NET 9; I picked the latest.
Adding Features: I started with just build and test, then incrementally asked Duo to help me add security scanning, secret detection, and better error handling.
Adding Context: Using /include to add the project file or the .gitlab-ci.yml file while asking questions helped Duo understand the context better.
Learn More with the Docs: During my journey, I knew Duo wasn't just making things up as it was referencing the documentation. I could continue my learning there and read more examples of how before_script is used in different contexts.
The AI-Assisted Development Experience
What impressed me most was how GitLab Duo helped me learn while building. Instead of just copying configurations from documentation, each conversation taught me something new about GitLab CI/CD best practices.
Conclusion
I think this template can be useful for anyone starting a .NET Aspire project. Ready to try it? Clone the template at cloud5mins/aspire-template and start building with confidence.
Whether you're new to .NET Aspire or CI/CD, this template gives you a good foundation. And if you want to customize it further, GitLab Duo is there to help you understand and modify the configuration.
If you think we should add more features or improve the template, feel free to open an issue in the repository. Your feedback is always welcome!
This week, we're exploring a wide range of topics, from .NET 10 previews and A/B testing to the latest in Azure development and AI. Plus, a selection of insightful podcast episodes to keep you informed and inspired.
Docker Model Runner ( DevOps and Docker Talk: Cloud Native Interviews and Tooling) - I tried the new model feature of Docker and had many questions. All of them were answered during this episode.
Michael Washington: The Nature Of Data - Episode 353 (Azure & DevOps Podcast) - Interesting discussion about data, and a bit more about a really cool project, Michael's Data warehouse, because sometimes we need something that runs locally.
Recently, someone asked me an interesting question: "Can GitHub Copilot or AI help me convert an application from one language to another?" My answer was a definitive yes! AI can not only help you write code in a new language, but it can also improve team collaboration and bridge the knowledge gap between developers who know different programming languages.
To demonstrate this capability, I decided to convert a COBOL application to Java—a perfect test case since I don't know either language well, which means I really needed Copilot to do the heavy lifting. All the code is available on GitHub.
The first step was setting up a proper development environment. I used a dev container and asked Copilot to help me build it. I also asked for recommendations on the best VS Code extensions for Java development. Within just a few minutes, I had a fully configured environment ready for Java development.
Choosing the Right Copilot Agent
When working with GitHub Copilot for code conversion, you have different mode to choose from:
Ask: Great for general questions (like asking about Java extensions)
Edit: Perfect for simple document editing (like modifying the generated code)
Agent: The powerhouse for complex tasks involving multiple files, imports, and structural changes
For code conversion projects, the Agent is your best friend. It can look at different source files, understand project structure, edit code, and even create new files on your behalf.
The Conversion Process
I used Claude 3.5 Sonnet for this conversion. Here's the simple prompt I used:
"Convert this hello business COBOL application into Java"
Copilot didn't just convert the code, it also provided detailed information about how to execute the Java application, which was invaluable since I had no Java experience.
The results varied depending on the AI model used (Claude, GPT, Gemini, etc.), but the core functionality remained consistent across different attempts. Since the original application was simple, I converted it multiple times using different prompts and models to test the consistency. Sometimes it generated a single file, other times it created multiple files: a main application and an Employee class (which wasn't in my original COBOL version). Sometimes it updated the Makefile to allow compilation and execution using make, while other times it provided instructions to use javac and java commands directly.
This variability is expected with generative AI results will differ between runs, but the core functionality remains reliable.
Real-World Challenges
Of course, the conversion wasn't perfect on the first try. For example, I encountered runtime errors when executing the application. The issue was with the data format—the original file used a flat file format with fixed length records (19 characters per record) and no line breaks.
I went back to Copilot, highlighted the error message from the terminal, and provided additional context about the 19 character record format. This iterative approach is key to successful AI assisted conversion.
"It's not working as expected, check the error in #terminalSelection. The records have fixed length of 19 characters without line breaks. Adjust the code to handle this format"
The Results
After the iterative improvements, my Java application successfully:
Compiled without errors
Processed all employee records
Generated a report with employee data
Calculated total salary (a nice addition that wasn't in the original)
While the output format wasn't identical to the original COBOL version (missing leading zeros, different line spacing), the core functionality was preserved.
Video Demonstration
Watch the complete conversion process in action:
Best Practices for AI-Assisted Code Conversion
Based on this experience, here are my recommendations:
1. Start with Small Pieces
Don't try to convert thousands of lines at once. Break your conversion into manageable modules or functions.
2. Set Up Project Standards
Consider creating a .github folder at your project root with an instructions.md file containing:
Best practices for your target language
Patterns and tools to use
Specific versions and frameworks
Enterprise standards to follow
3. Stay Involved in the Process
You're not just a spectator - you're an active participant. Review the changes, test the output, and provide feedback when things don't work as expected.
4. Iterate and Improve
Don't expect perfection on the first try. In my case, the converted application worked but produced slightly different output formatting. This is normal and expected, after all you are converting between two different languages with different conventions and styles.
Can AI Really Help with Code Conversion?
Absolutely, yes! GitHub Copilot can significantly:
Speed up the conversion process
Help with syntax and language specific patterns
Provide guidance on running and compiling the target language
Bridge knowledge gaps between team members
Generate supporting files and documentation
However, remember that it's generative AI, results will vary between runs, and you shouldn't expect identical output every time.
Final Thoughts
GitHub Copilot is definitely a tool you need in your toolkit for conversion projects. It won't replace the need for human oversight and testing, but it will dramatically accelerate the process and help teams collaborate more effectively across different programming languages.
The key is to approach it as a collaborative process where AI does the heavy lifting while you provide guidance, context, and quality assurance. Start small, iterate often, and don't be afraid to ask for clarification or corrections when the output isn't quite right.
Have you tried using AI for code conversion? I'd love to hear about your experiences in the comments below! Visit c5m.ca/copilot to get started with GitHub Copilot.
Welcome to the 655th Reading Notes. This edition explores embedding Python in .NET, working with stacked git branches, and an introduction to cloud-native. Plus, a quick tip for the Azure Portal and using local AI for code reviews.
Introduction to Cloud Native Computing (TNS Staff) - Very complete and interesting article that is the perfect point to get started with clout native application covering what it is the strategy the architecture everything
Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.
Welcome to another edition of my reading notes! This week, I’ve gathered a selection of insightful articles and resources covering topics like AI, cloud security, open source, and developer productivity. Whether you’re interested in best practices, new tools, or thought-provoking perspectives, there’s something here for everyone.
Dive in and enjoy the highlights!
Suggestion of the week
Copilot, The Good Parts: Efficiency (Rob Conery) - I love that post, it's so true! There are good and bad ways to use any tools. And I personally would really like seeing Rob build his stuff. Let's him know If you think like me.
Fantastic Alert Messages Using SweetAlert (Héctor Pérez ) - A great component to manage our alerts in C# instead of having JavaScript. That makes code easier to test when all is in the same language.
You DON’T Need Microservices for Serverless! (Derek Comartin) - A great post that explained the difference between cold coupling and monolithics versus microservices great post.
Open Source
How to convince your boss to sponsor Open Web Docs (Patrick Brosset) - Open source is important! And to contribute, it doesn't have to be code. Nice post that shares ideas and explains a few things about OSS.
Local code review with Docker and smollm2 before pushing to git (Gerardo Lopez) - This is a great idea! Definitely a good way to avoid the light of shame and be able to quickly validate that your code looks okay. It's also a great way to experiment free hook.
Welcome to Reading Notes #653 another packed edition of insights, tools, and updates from the tech world! This week's roundup dives into legendary engineering wisdom, AI controversies, and the latest innovations in Docker, Azure, and VS Code. Whether you're exploring MCP, refining your scripting skills, or gearing up for the newest Azure Developer CLI release, there's something here for every developer.
Let’s get into it!
Cloud
Azure Developer CLI (azd) - June 2025 (Kristen Womack) - Love that tool, great updates, so many new features and improvements in this version, very looking forward to try all of them, turning them all
AI
Publishing AI models to Docker Hub (Kevin Wittek) - Running model locally is a lot of people are looking forward to it, so this is good news can't wait to try it
As developers, we're constantly looking for tools that can help us stay in the flow and be more productive. Today, I want to share a powerful tool that's been gaining traction in the developer community: GitKraken CLI. This command-line interface brings together several key features that modern developers love - it's AI-powered, terminal-based, and incredibly efficient for managing Git workflows.
GitKraken CLI (accessible via the gk command) stands out because it simplifies complex Git workflows while adding intelligent automation. Unlike traditional Git commands, it provides a more intuitive workflow management system that can handle multiple repositories simultaneously.
Getting Started
Installation is straightforward. On Windows, you can install it using:
winget install gitkraken.cli
Once installed, you'll have access to the gk command, which becomes your gateway to streamlined Git operations.
The Workflow in Action
Let's walk through a typical development session using GitKraken CLI:
1. Starting a Work Session
Instead of manually creating branches and switching contexts, you can start a focused work session:
gk w start "Add Behind my Cloud feed" -i "Add Behind my Cloud feed #1"
This single command:
Creates a new branch based on your issue/feature name
Switches to that branch automatically
Links the work session to a specific issue
Sets up your development environment for focused work
2. Managing Multiple Work Sessions
You can easily see all your active work sessions:
gk w list
This is particularly powerful when working across multiple repositories or juggling several features simultaneously.
3. Committing with Intelligence
After making your changes, adding files works as expected:
gk add .
But here's where the AI magic happens. Instead of writing commit messages manually:
gk w commit --ai
The AI analyzes your changes and generates meaningful, descriptive commit messages automatically. No more "quick fix" or "update stuff" commits!
4. Pushing and Creating Pull Requests
Publishing your work is equally streamlined:
gk w push
And when you're ready to create a pull request:
gk w pr create --ai
Again, AI assistance helps generate appropriate PR titles and descriptions based on your work.
5. Wrapping Up
Once your work is complete and merged, clean up is simple:
gk w end
This command:
Switches you back to the main branch
Deletes the feature branch, locally and on GitHub
Closes the work session
Leaves your repository clean and ready for the next task
Why This Matters
The beauty of GitKraken CLI lies in its ability to keep you in the zone. You don't need to:
Switch between multiple tools
Remember complex Git commands
Write commit messages from scratch
Manually manage branch lifecycle
Everything flows naturally from one command to the next, maintaining your focus on what matters most: writing code.
Multi-Repository Power
One of the standout features is GitKraken CLI's ability to manage multiple repositories simultaneously. This is invaluable for:
Microservices architectures
Full-stack applications with separate frontend/backend repos
Organizations with multiple related projects
Try It Yourself
GitKraken CLI is part of a broader suite of developer tools that GitKraken offers. The CLI itself is free to use, which makes it easy to experiment with and integrate into your workflow without any upfront commitment. If you find value in the CLI and want to explore their other tools, GitKraken has various products that might complement your development setup.
The learning curve is genuinely minimal since it builds on Git concepts you already know while adding helpful automation. I've found that even small workflow improvements can compound over time, especially when you're working on multiple projects or dealing with frequent context switching.
If you're curious about what else GitKraken offers beyond the CLI, you can explore their full product lineup here. For those who decide the Pro features would benefit their workflow, as an ambassador of GitKraken I can share my code to provide a 50% discount for your GitKraken Pro subscription.
The combination of AI assistance and intuitive commands addresses real pain points that many developers face daily. Whether GitKraken CLI becomes a core part of your toolkit will depend on your specific workflow, but it's worth trying given that it's free and takes just a few minutes to set up.
The best tools are the ones that get out of your way and let you focus on building. GitKraken CLI aims to do exactly that.
This week, we explore a variety of topics, from database containerization and AI security risks to the evolving landscape of gaming devices and cloud technologies. We also explore the shift towards security-first development and the integration of .NET Aspire with SQL Server for integration testing.
Let's dive in!
Suggestion of the week
GitHub MCP Exploited: Accessing private repositories via MCP (Marco Milanta, Luca Beurer-Kellner) - AI tools are very powerful but also pretty new in our lives. It's important to stay up to date and understand the risks. Not scared, but to see the potential flaws and how to avoid them.
Beyond DevSecOps: The Rise of Security-First Development (Industry Perspectives) - DevSecOps was a wake-up call, and we need to build our app security first. That seems to make sense, right? Have a read of this post to dig deeper into this idea and understand its foundation
Testing has always been one of those tasks that developers know is essential but often find tedious. When I decided to add comprehensive unit tests to my NoteBookmark project, I thought: why not make this an experiment in AI-assisted development? What followed was a fascinating 4-hour journey that resulted in 88 unit tests, a complete CI/CD pipeline, and some valuable insights about working with AI coding assistants.
NoteBookmark is a .NET application built with C# that helps users manage and organize their reading notes and bookmarks. The project includes an API, a Blazor frontend, and uses Azure services for storage. You can check out the complete project on GitHub.
The Challenge: Starting from Zero
I'll be honest - it had been a while since I'd written comprehensive unit tests. Rather than diving in myself, I decided to see how different AI models would approach this task. My initial request was deliberately vague: "add a test project" without any other specifications.
Looking back, I realize I should have been more specific about which parts of the code I wanted covered. This would have made the review process easier and given me better control over the scope. But sometimes, the best learning comes from letting the AI surprise you.
The Great AI Model Comparison
GPT-4.1: Competent but Quiet
GPT-4.1 delivered decent results, but the experience felt somewhat mechanical. The code it generated was functional, but I found myself wanting more context. The explanations were minimal, and I often had to ask follow-up questions to understand the reasoning behind certain test approaches.
Gemini: The False Start
My experience with Gemini was... strange. Perhaps it was a glitch or an off day, but most of what was generated simply didn't work. I didn't persist with this model for long, as debugging AI-generated code that fundamentally doesn't function defeats the purpose of the exercise. Note that at the time of this writing, Gemini was still in preview, so I expect it to improve over time.
Claude Sonnet: The Clear Winner
This is where the magic happened. Claude Sonnet became my co-pilot of choice for this project. What set it apart wasn't just the quality of the code (though that was excellent), but the quality of the conversation. It felt like having a thoughtful colleague thinking out loud with me.
The explanations were clear and educational. When Claude suggested a particular testing approach, it would explain why. When it encountered a complex scenario, it would walk through its reasoning. I tried different versions of Claude Sonnet but didn't notice significant differences in results - they were all consistently good.
The Development Process: A 4-Hour Journey
Hour 1-2: Getting to Compilation
The first iteration couldn't compile. This wasn't surprising given the complexity of the codebase and the vague initial request. But here's where the AI collaboration really shined. Instead of manually debugging everything myself, I worked with Copilot to identify and fix issues iteratively.
We went through several rounds of:
Identify compilation errors
Discuss the best approach to fix them
Let the AI implement the fixes
Review and refine
After about 2 hours, we had a test project with 88 unit tests that compiled successfully. The AI had chosen xUnit as the testing framework, which I was happy with - it's a solid choice that I might not have picked myself if I was rusty on the current .NET testing landscape.
Hour 2.5-3.5: Making Tests Pass
Getting the tests to compile was one thing; getting them to pass was another challenge entirely. This phase taught me a lot about both my codebase and xUnit features I wasn't familiar with.
I relied heavily on the /explain feature during this phase. When tests failed, I'd ask Claude to explain what was happening and why. This was invaluable for understanding not just the immediate fix, but the underlying testing concepts.
One of those moment was learning about [InlineData(true)] and other xUnit data attributes. These weren't features I was familiar with, and having them explained in context made them immediately useful.
Hour 3.5-4: Structure and Style
Once all tests were passing, I spent time ensuring I understood each test and requesting structural changes to match my preferences. This phase was crucial for taking ownership of the code. Just because AI wrote it doesn't mean it should remain a black box. Let's repeat this: Understanding the code is essential; just because AI wrote it doesn't mean it's good.
Beyond Testing: CI/CD Integration
With the tests complete, I asked Copilot to create a GitHub Actions workflow to run tests on every push to main and v-next branches, plus PR reviews. Initially it started modifiying my existing workflow that takess care of the Azure deployment. I wanted a separate workflow for testing, so I interrupted (that's nice I wasn't "forced" to wait), and asked it to create a new one instead. The result was the running-unit-tests.yml workflow that worked perfectly on the first try.
This was genuinely surprising. CI/CD configurations often require tweaking, but the generated workflow handled:
Multi-version .NET setup
Dependency restoration
Building and testing
Test result reporting
Code coverage analysis
Artifact uploading
The PR Enhancement Adventure
Here's where things got interesting. When I asked Copilot to enhance the workflow to show test results in PRs, it started adding components, then paused and asked if it could delete the current version and start from scratch.
I said yes, and I'm glad I did. The rebuilt version created beautiful PR comments showing:
Test results summary
Code coverage reports (which I didn't ask for but appreciated)
Detailed breakdowns.
The Finishing Touches
No project is complete without proper status indicators. I added a test status badge to the README, giving anyone visiting the repository immediate visibility into the project's health.
Key Takeaways
What Worked Well
AI as a Learning Partner: Having Copilot explain testing concepts and xUnit features was like having a patient teacher
Iterative Refinement: The back-and-forth process felt natural and productive
Comprehensive Solutions: The AI didn't just write tests; it created a complete testing infrastructure
Quality Over Speed: While it took 4 hours, the result was thorough and well-structured
What I'd Do Differently
Be More Specific Initially: Starting with clearer scope would have streamlined the process
Set Testing Priorities: Identifying critical paths first would have been valuable
Plan for Visual Test Reports: Thinking about test result visualization from the start
Lessons About AI Collaboration
Model Choice Matters: The difference between AI models was significant
Conversation Quality Matters: Clear explanations make the collaboration more valuable
Trust but Verify: Understanding every piece of generated code is crucial
Embrace Iteration: The best results come from multiple refinement cycles
The Bigger Picture
This experiment reinforced my belief that AI coding assistants are most powerful when they're true collaborators rather than code generators. The value wasn't just in the 88 tests that were written, but in the learning that happened along the way.
For developers hesitant about AI assistance in testing: this isn't about replacing your testing skills, it's about augmenting them. The AI handles the boilerplate and suggests patterns, but you bring the domain knowledge and quality judgment.
Conclusion
Would I do this again? Absolutely. The combination of comprehensive test coverage, learning opportunities, and time efficiency made this a clear win. The 4 hours invested created not just tests, but a complete testing infrastructure that will pay dividends throughout the project's lifecycle.
If you're considering AI-assisted testing for your own projects, my advice is simple: start the conversation, be prepared to iterate, and don't be afraid to ask "why" at every step. The goal isn't just working code - it's understanding and owning that code.
The complete test suite and CI/CD pipeline are available in the NoteBookmark repository if you want to see the results of this AI collaboration in action.
Welcome to another edition of my reading notes! This week brings some fascinating insights into AI's real-world impact, exciting developments in .NET and containerization, plus practical tools for improving our development workflows.
From local AI-powered code reviews to Docker security hardening and the upcoming .NET 10 features, there's plenty to explore.
AI
The promise that wasn’t kept (Salma Alam Maylor) - Interesting thoughts about AI and its impact on our work but also on our life, and the planet.
Inside GitHub: How we hardened our SAML implementation (Greg Ose, Taylor Reis) - Very interesting post that pushes the curtain a little bit so we could see behind the scene how this very used but notification system works and has been updated
Enhance productivity with AI + Remote Dev (Brigit Murtaugh, Christof Marti, Josh Spicer, Olivia Guzzardo McVicker) - I love the dev container environments, they are so useful! And I also use the remote one when I'm not on my dev device so easy. Happy to see that Copilot will be right there with me.
It's time for another edition of Reading Notes! This week brings exciting developments in the open source world, with major announcements from Microsoft making WSL and VS Code's AI features open source. We've also got updates on Azure Container Apps, .NET Aspire, and some great insights on developer productivity tools.
Let's dive into these interesting reads that caught my attention this week.
Cloud
Happy 5th Birthday Bicep! (Sam Cogan) - What?! Five years already! That's incredible, I remember all the discussion about how we make our business better and honestly, bicep is a big success. Congrats to the team
Have I Been Pwned 2.0 is Now Live! (Troy Hunt) - New look, new merch, and confetti, all without API breaking changes! Learn all about this major update in this post.
What's new in .NET Aspire 9.3 (David Pine) - Wow! How so many great new features can be added in a single version?! Aspire is a must for all .NET developers.
Accelerate Your .NET Upgrades with GitHub Copilot (McKenna Barlow) - That's the tool I've been waiting for ages! Adding a Copilot to the extension is the smartest move they could make. I'm going to update an app right away. I'll share more later
Open Source
Edit is now open source (Christopher Nguyen) - That's a great news! I installed it half through the post and it great! Fast, simple, and tiny!! Love it!
Agent mode for every developer (Katie Savage) - Great new for everyone as the agent mode become available in so many different editor. This post also contains videos to shows some scenarios.
Podcasts
Reimagining the Windows Terminal with Warp's Zach Lloyd (Scott Hanselman) - A very interesting talk with the CEO of Warp that answers so many questions I had about this very different terminal. Really interesting episode, and terminal too BTW)
The experience is enough (Salma Alam-Naylor) - Whether we like it or not, we are people creature. We all need to stop hiding behind our screens and get out there!
Automating deployments is something I always enjoy. However, it's true that it often takes more time than a simple "right-click deploy." Plus, you may need to know different technologies and scripting languages.
But what if there was a tool that could help you write everything you need—Infrastructure as Code (IaC) files, scripts to copy files, and scripts to populate a database? In this post, we'll explore how the Azure Developer CLI (azd) can make deployments much easier.
What do we want to do?
Our goal: Deploy the 2D6 Dungeon App to Azure Container Apps.
This .NET Aspire solution includes:
A frontend
A data API
A database
The Problem
In a previous post, we showed how azd up can easily deploy web apps to Azure.
If we try the same command for this solution, the deployment will be successful—but incomplete:
The .NET Blazor frontend is deployed perfectly.
However, the app fails when trying to access data.
Looking at the logs, we see the database wasn't created or populated, and the API container fails to start.
Let's look more closely at these issues.
The Database
When running the solution locally, Aspire creates a MySQL container and executes SQL scripts to create and populate the tables. This is specified in the AppHost project:
var mysql = builder.AddMySql("sqlsvr2d6")
.WithLifetime(ContainerLifetime.Persistent);
var db2d6 = mysql.AddDatabase("db2d6");
mysql.WithInitBindMount(source: "../../database/scripts", isReadOnly: false);
When MySQL starts, it looks for SQL files in a specific folder and executes them. Locally, this works because the bind mount is mapped to a local folder with the files.
However, when deployed to Azure:
The mounts are created in Azure Storage Files
The files are missing!
The Data API
This project uses Data API Builder (dab). Based on a single config file, a full data API is built and hosted in a container.
Locally, Aspire creates a DAB container and reads the JSON config file to create the API. This is specified in the AppHost project:
var dab = builder.AddDataAPIBuilder("dab", ["../../database/dab-config.json"])
.WithReference(db2d6)
.WaitFor(db2d6);
But once again, when deployed to Azure, the file is missing. The DAB container starts but fails to find the config file.
The Solution
The solution is simple: the SQL scripts and DAB config file need to be uploaded into Azure Storage Files during deployment.
You can do this by adding a post-provision hook in the azure.yaml file to execute a script that uploads the files. See an example of a post-provision hook in this post.
Alternatively, you can leverage azd alpha features: azd.operations and infraSynth.
azd.operations extends the provisioning providers and will upload the files for us.
infraSynth generates the IaC files for the entire solution.
💡Note: These features are in alpha and subject to change.
Each azd alpha feature can be turned on individually. To see all features:
azd config list-alpha
To activate the features we need:
azd config set alpha.azd.operations on
azd config set alpha.infraSynth on
Let's Try It
Once the azd.operation feature is activated, any azd up will now upload the files into Azure. If you check the database, you'll see that the db2d6 database was created and populated. Yay!
However, the DAB API will still fail to start. Why? Because, currently, DAB looks for a file, not a folder, when it starts. This can be fixed by modifying the IaC files.
One Last Step: Synthesize the IaC Files
First, let's synthesize the IaC files. These Bicep files describe the required infrastructure for our solution.
With the infraSynth feature activated, run:
azd infra synth
You'll now see a new infra folder under the AppHost project, with YAML files matching the container names. Each file contains the details for creating a container.
Open the dab.tmpl.yaml file to see the DAB API configuration. Look for the volumeMounts section. To help DAB find its config file, add subPath: dab-config.json to make the binding more specific:
You can also specify the scaling minimum and maximum number of replicas if you wish.
Now that the IaC files are created, azd will use them. If you run azd up again, the execution time will be much faster—azd deployment is incremental and only does "what changed."
The Final Result
The solution is now fully deployed:
The database is there with the data
The API works as expected
You can use your application!
Bonus: Deploying with CI/CD
Want to deploy with CI/CD? First, generate the GitHub Action (or Azure DevOps) workflow with:
azd pipeline config
Then, add a step to activate the alpha feature before the provisioning step in the azure-dev.yml file generated by the previous command.
- name: Extends provisioning providers with azd operations
run: azd config set alpha.azd.operations on
With these changes, and assuming the infra files are included in the repo, the deployment will work on the first try.
Conclusion
It's exciting to see how tools like azd are shaping the future of development and deployment. Not only do they make the developer's life easier today by automating complex tasks, but they also ensure you're ready for production with all the necessary Infrastructure as Code (IaC) files in place. The journey from code to cloud has never been smoother!
If you have any questions or feedback, I'm always happy to help—just reach out on your favorite social media platform.
Welcome to another edition of my reading notes! This week I've come across some interesting articles spanning from programming decisions to career tips and AI innovations. Hope you find something valuable in this collection.
Programming
Why we built our startup in C# (Sam Cox) - I really like those true-story blog posts; they teach us about the thought process and the reason behind a decision.
It wasn’t the idea that failed: it was the execution (Salma Alam-Naylor) - Even though I feel like I'm missing a few points about sigma and new stuff coming up in 2025, this post is an amazing memory of our past in terms of evolution in the web industry
Thriving After a Layoff: The First 30 Days (scrappy girl project) - A friend of mine shared with me this post, and I think it will help me in the upcoming days. So here I am sharing so more can benefit from it
Books
The Let Them Theory (Mel Robbins) - Stop wasting energy on things you can’t control. It something we all know, but probably forgot the power if it. Let go is hard, but it works in so many scenario. In this book Mel explains how to make it works, and how to use it with a let me action.
Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.
Welcome to this week's reading notes! Dive into the latest on Microsoft's new AI chat template, explore Docker's MCP CLI, learn about integrating AI into .NET applications, and discover how to automate .NET MAUI library publishing with GitHub Actions.
Whether you're interested in AI advancements, programming techniques, or DevOps practices, there's something valuable waiting for you below.
MsDevMtl Meetup - Uno
Suggestion of the week
Exploring the new AI chat template (Andrew Lock) - This new .NET template looks pretty useful and has a lot of components already baked in. This post explains those and shows how to customize it for our usage.
Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.
This post is a collection of my latest reading notes, highlighting interesting articles and resources on AI, programming, databases, and more. Each link includes a brief summary of what I found valuable or noteworthy.
Help yourself to thrive (Salma) - The human body is an extraordinary machine, extremely strong and conciliant, but it also requires a fine turning. Great post, we must learn from it.
Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.
Welcome to this week's collection of fascinating reads across cloud computing, AI, and programming! As technology continues to evolve at breakneck speed, I've gathered some of the most insightful articles that caught my attention. From securing MCP servers to exploring Rust, there's something here for every tech enthusiast.
Dive in and discover what's new in our rapidly changing digital landscape.
When ASP.NET Core Identity Is No Longer Enough (Andrea Chiarelli) - Authentication and authorization have an important place in our Apps. This post shares details about how things work and the different processes.
New Docker Extension for Visual Studio Code (Remy Suen) - This extension was already a must, they made it better, it's a must must! But seriously give it a try and try it by yourself.
Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.
