This week explores the synergy between Dapr and WebAssembly for modern microservices, highlights the transformative potential of Wasm 3.0 for .NET developers, and delves into best practices for structuring Rust web services. In the AI realm, we examine the emergence of developer-friendly AI frameworks like Microsoft’s Agent Framework and Google’s Jules Tools, which bridge AI capabilities directly into terminals and workflows. Additionally, we examine AI obfuscation techniques and their implications, alongside updates on Perplexity’s free Comet AI browser and its new background assistant. Whether you’re building scalable systems, optimizing code, or integrating cutting-edge AI tools, this post offers a snapshot of trends shaping tech today.
Obfuscation for AI: How it Works, Best Practices, and Metrics – PreEmptive (preemptive) - It's been a while since I last used a station in my code because I'm doing demos most of the time, but it always fascinated me. I feel I should try it with AI, it looks very interesting and powerful. This post shares a lot about what's possible, the risk, and so much more
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 week covers Microsoft’s open-source Agent Framework for agentic AI, prompt-injection risks and mitigations, and the causes of language model hallucinations. It also highlights NuGet package security updates, Azure SQL Data API Builder improvements, Reka’s new Parallel Thinking feature, and the latest in AI benchmarking.
MCP Prompt-Injection: The Trust Paradox in AI (Saurabh Davala, Sundeep Gottipati) - This is a good post to learn to start learning about the real danger. To be aware of the potential risk and learning about what we can do.
Why language models hallucinate - Very interesting post that explains the reason why we still have hallucinations and how it works
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 week post explores the intersection of AI, cloud, and DevOps, featuring updates on Microsoft’s Logic Apps integration, practical .NET tools for system automation, and strategies to enhance documentation for AI-driven workflows. Whether you’re refining enterprise security practices with NuGet’s Trusted Publishing or diving into the ethical nuances of AI through vector databases, this post offers a blend of technical deep dives and thought-provoking discussions. Don’t miss the podcast highlights, from DevOps innovation to the business impact of employee well-being, perfect for developers, architects, and curious minds alike. Let’s connect the dots in a world where code, creativity, and collaboration drive progress.
Prompt Files and Instructions Files Explained - .NET Blog (Wendy Breiding) - One question I often hear is how can I tell Copilot the standard and conventions my enterprise uses. Well, now I can send them to this post. Well done!
ohn Bristowe: The Latest from Octopus Deploy - Episode 368 (Azure & DevOps Podcast) - Loved this episode, I don't know a lot about Octopus. Discussing deployment pipelines, AI integration in DevOps, and the evolution from manual weekend deployments to automated, reliable workflows.
The business case for employee well-being (Modern Mentor) - Really enjoyed this episode on how workplace wellbeing isn't just about perks but building better work systems that boost both results and employee experience
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 some interesting finds across programming, AI, and general tech topics. From exciting Cake updates to practical AI implementation advice, here are the articles that caught my attention recently.
Programming
Cake.Sdk 5.0.25257.82-beta released (devlead) - I'm happy to see the new release of this cake version coming to a recent .NET version. I have one of my projects using cake, and I was waiting for it; this is amazing
AI
Performance Improvements in .NET 10 (Stephen Toub) - There's a lot of good advice in this post. Assuming you're using AI you should definitely read 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.
In this edition, we explore modern development's evolving landscape. From Microsoft's .NET Aspire simplifying distributed applications to AI security considerations, Git workflow optimizations, and backlog management strategies, there's something here to spark your next breakthrough.
The tech world never sleeps, and neither does innovation. Let's explore what caught my attention this week and might just spark your next big idea or solve that problem you've been wrestling with.
Identity and Access Management for .NET (Khalid Abuhakmeh) - This package looks very interesting to add multiple handlers to an HTTP client. The first question that pops in my mind is why this is not already in .NET, I think it should. I'll definitely give it a try.
AI Injection Attacks (ericlaw) - Great post that talks about the current risk when using AI and how we should try to do our best to protect the important information.
How to Get Things Done, Stay Focused, and Be More Productive (The Mel Robbins Podcast) - This compelling episode (available in audio and video) takes a fresh approach to productivity. Having read their books, I found the conversation particularly engaging and highly recommend it.
Welcome to another edition of my weekly reading notes! This week's collection brings together some fascinating developments across the tech landscape. From the intricacies of building cross-platform .NET tools to impressive AI breakthroughs like Warp's stellar performance on SWE-bench, there's plenty to explore. I've also discovered some thought-provoking content about leadership, product management, and the art of meaningful communication. Whether you're interested in the latest AI tools, looking for career insights, or simply want to stay current with industry trends, this week's selection has something valuable for every developer and tech professional.
Programming
Using and authoring .NET tools (Andrew Lock) - Interesting post that shares the behind-the-scenes when you're building a tool for multiple targets and the challenge that it represents. Those also share the new ways of .NET 10
Design at GitHub with Diana Mounter (.NET Rocks!) - Very interesting, discussion about so many things: career, the balance between design and engineering, GitHub, and so much more.
How to Lead with Value with Dr. Morgan Depenbusch (How to Lead with Value with Dr. Morgan Depenbusch) - I really enjoyed this episode about the little things we can do to shift the way we interact with others.
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.
I wanted to kick the tires on the upcoming .NET 10 C# script experience and see how far I could get calling Reka’s Research LLM from a single file, no project scaffolding, no .csproj. This isn’t a benchmark; it’s a practical tour to compare ergonomics, setup, and the little gotchas you hit along the way. I’ll share what worked, what didn’t, and a few notes you might find useful if you try the same.
All the sample code (and a bit more) is here: reka-ai/api-examples-dotnet · csharp10-script. The scripts run a small “top 3 restaurants” prompt so you can validate everything quickly.
We’ll make the same request in three ways:
OpenAI SDK
Microsoft.Extensions.AI for OpenAI
Raw HttpClient
What you need
The C# "script" feature used below ships with the upcoming .NET 10 and is currently available in preview. If you prefer not to install a preview SDK, you can run everything inside the provided Dev Container or on GitHub Codespaces. I include a .devcontainer folder with everything set up in the repo.
Set up your API key
We are talking about APIs here, so of course, you need an API key. The good news is that it's free to sign up with Reka and get one! It's a 2-click process, more details in the repo. The API key is then stored in a .env file, and each script loads environment variables using DotNetEnv.Env.Load(), so your key is picked up automatically. I went this way instead of using dotnet user-secrets because I thought it would be the way it would be done in a CI/CD pipeline or a quick script.
Run the demos
From the csharp10-script folder, run any of these scripts. Each line is an alternative
dotnet run 1-try-reka-openai.cs
dotnet run 2-try-reka-ms-ext.cs
dotnet run 3-try-reka-http.cs
You should see a short list of restaurant suggestions.
OpenAI SDK with a custom endpoint
Reka's API is using the OpenAI format; therefore, I thought of using the NuGet package OpenAI. To reference a package in a script, you use the #:package [package name]@[package version] directive at the top of the file. Here is an example:
#:package OpenAI@2.3.0
// ...
var baseUrl = "http://api.reka.ai/v1";
var openAiClient = new OpenAIClient(new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
Endpoint = new Uri(baseUrl)
});
var client = openAiClient.GetChatClient("reka-flash-research");
string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";
var completion = await client.CompleteChatAsync(
new List<ChatMessage>
{
new UserChatMessage(prompt)
}
);
var generatedText = completion.Value.Content[0].Text;
Console.WriteLine($" Result: \n{generatedText}");
The rest of the code is more straightforward. You create a chat client, specify the Reka API URL, select the model, and then you send a prompt. And it works just as expected. However, not everything was perfect, but before I share more about that part, let's talk about Microsoft.Extensions.AI.
Microsoft Extensions AI for OpenAI
Another common way to use LLM in .NET is to use one ot the Microsoft.Extensions.AI NuGet package. In our case Microsoft.Extensions.AI.OpenAI was used.
#:package Microsoft.Extensions.AI.OpenAI@9.8.0-preview.1.25412.6
// ....
var baseUrl = "http://api.reka.ai/v1";
IChatClient client = new ChatClient("reka-flash-research", new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
Endpoint = new Uri(baseUrl)
}).AsIChatClient();
string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";
Console.WriteLine(await client.GetResponseAsync(prompt));
As you can see, the code is very similar. Create a chat client, set the URL, the model, and add your prompt, and it works just as well.
That's two ways to use Reka API with different SDKs, but maybe you would prefer to go "SDKless", let's see how to do that.
Raw HttpClient calling the REST API
Without any SDK to help, there is a bit more line of code to write, but it's still pretty straightforward. Let's see the code:
using var httpClient = new HttpClient();
var baseUrl = "http://api.reka.ai/v1/chat/completions";
var requestPayload = new
{
model = "reka-flash-research",
messages = new[]
{
new
{
role = "user",
content = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in New York city"
}
}
};
using var request = new HttpRequestMessage(HttpMethod.Post, baseUrl);
request.Headers.Add("Authorization", $"Bearer {REKA_API_KEY}");
request.Content = new StringContent(jsonPayload, Encoding.UTF8, "application/json");
var response = await httpClient.SendAsync(request);
var responseContent = await response.Content.ReadAsStringAsync();
var jsonDocument = JsonDocument.Parse(responseContent);
var contentString = jsonDocument.RootElement
.GetProperty("choices")[0]
.GetProperty("message")
.GetProperty("content")
.GetString();
Console.WriteLine(contentString);
So you create an HttpClient, prepare a request with the right headers and payload, send it, get the response, and parse the JSON to extract the text. In this case, you have to know the JSON structure of the response, but it follows the OpenAI format.
What did I learn from this experiment?
I used VS Code while trying the script functionality. One thing that surprised me was that I didn't get any IntelliSense or autocompletion. I try to disable the DevKit extension and change the setting for OmniSharp, but no luck. My guess is that because it's in preview, and it will work just fine in November 2025 when .NET 10 will be released.
In this light environment, I encountered some issues where, for some reason, I couldn't use an https endpoint, so I had to use http. In the raw httpClient script, I had some errors with the Reflection that wasn't available. It could be related to the preview or something else, I didn't investigate further.
For the most part, everything worked as expected. You can use C# code to quickly execute some tasks without any project scaffolding. It's a great way to try out the Reka API and see how it works.
What's Next?
While writing those scripts, I encountered multiple issues that aren't related to .NET but more about the SDKs when trying to do more advanced functionalities like optimization of the query and formatting the response output. Since it goes beyond the scope of this post, I will share my findings in a follow-up post. Stay tuned!
Here are my reading notes for the week: a mix of AI research and evaluation, .NET and Linux troubleshooting, testing framework changes, and JavaScript/TypeScript perspectives, plus a few podcast episodes on C#, work design, and software modernization that I found worthwhile.
AI
Introducing Research-Eval: A Benchmark for Search-Augmented LLMs (Reka Team) - One thing that has fascinated me since the beginning of this AI trend is how they test and measure the efficiency of those models. This post is going to go into details and share the benchmark (oss) and the results very interesting
Converting an xUnit test project to TUnit (Andrew Lock) - Like Andrew said in this post, changing your test framework is a big deal, but I will definitely consider TUnit for my next project. A very interesting post.
C# 14 with Dustin Campbell (.NET Rocks!) - Nice episode talking about C# and more precisely things that are related to Razor Pages. Always nice to listen to Carl and Richard.
How work design can reignite tremendous results (Modern Mentor) - Two Modern Mentor episodes this week, I love those shorter, concentrated episodes. This one focuses on ideas to help leaders redesign how work gets done.
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.
From Docker's security practices to the latest in GPT-5 discussions, there's quite a mix of topics to dive into. I particularly enjoyed the thought-provoking piece about junior developers in the age of LLMs - it's a conversation we should all be having.
As always, grab your favorite beverage, and let's explore what caught my attention this week!
DevOps
Practitioner's View: How Docker Enables Security by Default (Pedro IgnĂĄcio, Denis Cruz Rodrigues) - Improving the culture can be a challenge, but security is worth the effort. This post lists your first targets if you want to go that way, and explains why it's important.
AI
Announcing the NuGet MCP Server Preview - .NET Blog (Jeff Kluge) - Oh! That's a cool one, looking forward to trying it and seeing if you can help me when I'm searching for a package but I don't know the name or don't remember the name
The GitHub Prompt Injection Data Heist | Docker (Ajeet Singh Raina) - Yes! This post talks about this injection story. But there is more! It shares how to prevent it, and all the things that we need to be aware of those dangers.
SPI 885: What's Working on Social Media Right Now (The Smart Passive Income Online Business and Blogging Podcast) - Social medias are evolving and this podcast episode talk about how we should adapt and change the way we do business with it.
476: GPT-5 Is Here, What's next? (Merge Conflict) - As much as I like the new GPT-5, they made me realized that I was thinking exactly like them! I won't spoil anything but it is a great episode where James is in fire! ;)
Miscellaneous
The role of junior developers in the world of LLMs (Oren Eini) - This is a great question. AI is definitely affecting the job market. However, this interesting post denies the annihilation of junior roles. What do you think?
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 week post collects concise links and takeaways across .NET, AI, Docker, open source security, DevOps, and broader developer topics. From the .NET Conf call for content and Copilot prompts to Docker MCP tooling, container debugging tips, running .NET as WASM, and a fresh look at the 10x engineer idea.
Running .NET in the browser without Blazor (Andrew Lock) - I never thought about it but it's true we can execute .NET code as WASM without Blazor. This tutorial shows you all the code.
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 week’s notes cover GenAI vs agentic AI, fresh Docker and Aspire news, how to run WordPress in containers, and building apps with React and .NET. Plus a few podcasts worth a listen.
Does it Make Sense to Run WordPress in Docker? (Lukas Mauser) - Looking at different options to run WordPress? Check out this blog post. All the code to do it in a docker container is shared and also details the reasons why you should do it or not
Introducing Reka Research: Your AI Research Assistant
Meet Reka Research a powerful AI agent that can search the web and analyze your files to answer complex questions in minutes. Whether you're staying up to date with AI news, screening resumes, or researching technical topics, Reka Research does the heavy lifting for you.
What Makes Reka Research Special?
Reka Research stands out in four key areas:
Top Performance: Best in class results on research benchmarks
Fast Results: Get thorough answers in 1-3 minutes
Full Transparency: See exactly how the AI reached its conclusions. All steps are visible.
Smart Web Search That Actually Works
Ever wished you could ask someone to research the latest AI developments while you focus on other work? That's exactly what Reka Research does.
Watch how it works:
In this demo, Jess and Sharath shows how Reka Research can automatically gather the most important AI news from the past week. The AI visits multiple websites, takes notes, and presents a clean summary with sources. You can even restrict searches to specific domains or set limits on how many sites to check.
File Search for Your Private Documents
Sometimes the information you need isn't on the web - it's in your company's documents, meeting notes, or file archives. Reka Research can search through thousands of private files to find exactly what you're looking for.
See it in action:
In this example, ess and Sharath shows how HR teams can use Reka Research to quickly screen resumes. Instead of manually reviewing hundreds of applications, the AI finds candidates who meet specific requirements (like having a computer science degree and 3+ years of backend experience) in seconds!
Writing Better Prompts Gets Better Results
Like any AI tool, Reka Research works best when you know how to ask the right questions. The key is being specific about what you want and providing context.
Learn the techniques:
Jess and Yi shares practical tips for getting the most out of Reka Research. Instead of asking "summarize meeting minutes," try "summarize April meeting minutes about public participation." The more specific you are, the better your results will be.
Ready to Try Reka Research?
Reka Research is currently available for everyone! Try it via the playground, or using directly the API. Whether you're researching competitors, analyzing documents, or staying current with industry trends, it can save you hours of work.
Want to learn more and connect with other users? Join our Discord community where you can:
This week's reading notes cover a variety of insightful topics, from enhancing your development environment with dev containers on Windows to prioritizing open-source bugs effectively. You'll also find helpful posts on integrating MFA into your login process, exploring RavenDB's vector search capabilities, and understanding the differences between Ask Mode and Agent Mode in Visual Studio.
Why You Should Incorporate MFA into Your Login Process (Suzanne Scacca) - You think the answer is simple, think again. Nice post that explains the difference between 2FA and MFA and why you should or should not implement one of those
Aspire Dashboard (Joseph Guadagno) - Great deep dive about the Aspire dashboard, learn all the features packed inside it
Open Source
How I Prioritize OSS Bugs (jeremydmiller) - A very instructive post on a real-life issue. It's harder than people think to prioritize. And it may help you write better bug reports...
MCP server integration in Visual Studio (Mark Downie) - Great update! That security stuff is very important and a good example using get up to store your tokens love 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.
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.
In a recent post, I shared how to set up a CI/CD pipeline for a .NET Aspire project on GitLab. The pipeline includes unit tests, security scanning, and secret detection, and if any of those fail, the pipeline would fail. Great, but what about code coverage for the unit tests? The pipeline included code coverage commands, but the coverage was not visible in the GitLab interface. Let's fix that.
One thing I initially thought was that the regex used to extract the coverage was incorrect. The regex used in the pipeline was:
coverage: '/Total\s*\|\s*(\d+(?:\.\d+)?)%/'
That regex came directly from the GitLab documentation, so I thought it should work correctly. However, coverage still wasn't visible in the GitLab interface.
So with the help of GitHub Copilot, we wrote a few commands to validate:
That the coverage.cobertura.xml was in a consistent location (instead of being in a folder with a GUID name)
That the coverage.cobertura.xml file was in a valid format
What exactly the regex was looking for
Everything checked out fine, so why was the coverage not visible?
The Solution
It turns out that the coverage command with the regex expression is scanning the console output and not the coverage.cobertura.xml file. Aha! One solution was to install dotnet-tools to changing where the the test results was persisted; to the console instead of the XML file, but I preferred keeping the .NET environment unchanged.
The solution I ended up implementing was executing a grep command to extract the coverage from the coverage.cobertura.xml file and then echoing it to the console. Here's what it looks like:
I hope this helps others save time when setting up code coverage for their .NET projects on GitLab. The key insight is that GitLab's coverage regex works on console output, not on the files (XML or other formats).
If you have any questions or suggestions, feel free to reach out!
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!
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 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.
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
