The art of simplicity

Love is in the air, and like many couples, my girlfriend and I have our Valentine's Day traditions. Hers involves cuddling up to watch a romantic movie, while I secretly wish we were watching Bruce Willis jump off a building. This year, I decided to bridge this divide with a creative solution: I built an AI-powered app that can transform any movie into a romantic masterpiece. The Valentine's Day Dilemma We've all been there. It's February 14th, and you're staring at a choice between "The Notebook" and literally anything else. While I appreciate a good love story, my heart belongs to action sequences, plot twists, and the occasional explosion. But how do you convince your romantic partner that "Die Hard" is actually a touching love story about a man trying to reunite with his wife on Christmas Eve? Enter the Romantic Movie Night Generator That's where my new web application comes in. It uses Azure OpenAI to analyze any movie poster and...

After successfully installing a new SonarQube server, I followed the documented steps to configure the Azure DevOps integration. However when trying to register our on premise Azure DevOps instance, SonarQube throwed the following exception: 2025.02.10 11:47:30 ERROR web[13308e38-2d1a-46ae-80d9-baa54c12ffac][o.s.a.c.a.AzureDevOpsHttpClient] Unable to contact Azure DevOps server for request [https://azuredevops.server.be/tfs/defaultcollection/_apis/projects?api-version=3.0]: [PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target I found an old post online that links this error message to a problem with our SSL certificate. It seems that Java is unable to recognize the root certificate authority (CA). This could make sense as our internal Azure DevOps instance is using an SSL certificate signed by an internal root certificate. To fix the issue, I followed the steps described in the post: ...

As part of our build process we use SonarQube for our code quality analysis. However after upgrading to the latest version of the SonarQube tasks for Azure DevOps, we encountered the following error message "The SonarScanner did not complete successfully." This seemingly vague error can stop your build pipeline dead in its tracks, leaving us scratching our head about what went wrong. Here is the full stack trace: at java.base/java.net.URLClassLoader.findClass(URLClassLoader.java:451) ##[error]at org.sonarsource.scanner.api.internal.IsolatedClassloader.loadClass(IsolatedClassloader.java:82) at org.sonarsource.scanner.api.internal.IsolatedClassloader.loadClass(IsolatedClassloader.java:82) ##[error]at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:522) at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:522) ##[error]at org.sonarsource.scanner.api.internal.batch.DefaultBatchFactory.createBatch(DefaultBatchFactory.java:32) at org.sonarsource....

Lakera, a product company that offers solutions to monitor, protect and control your AI agents, has created Gandalf , a game that allows you to test your prompt injection skills. Named after the wise wizard from The Lord of the Rings, this engaging security game tests your abilities to outsmart AI systems through prompt injection techniques. What is prompt injection? Before diving into Gandalf , it's important to understand what prompt injection is. Prompt injection is a technique where users attempt to manipulate an AI system's behavior by crafting inputs that override or bypass its intended operating parameters. Think of it like SQL injection, but for AI language models. When we interact with AI systems, we typically provide them with a prompt - a piece of text that guides their response. However, these systems often retain their base instructions (sometimes called a system prompt) that define their behavior and limitations. Prompt injection attempts to override these i...

Azure has a lot to offer if you want start building AI enabled solutions. But where do you start? There is just so much to learn and explore that it is easy to get lost in all the services, documentation and information out there. That's where the Book of AI comes in. This comprehensive guide is designed to help you navigate the world of AI services in Azure, providing you with the resources, tutorials, and sample code you need to build innovative solutions using Azure AI services. Why use the Book of AI? The website itself gives the following arguments: I can only agree with that. Use the book of AI to try out not only the different Azure AI services but also the different SDK’s and client libraries. Not a C# developer?  No problem! All examples are available in multiple programming languages.   Eat your own AI But for me the greatest feature of the Book of AI is not the content itself but the way it has been created. Instead of manually writing the complete webs...

Everyone who works in software development knows this: naming things is hard. And when you need to create a lot of resources in Azure, naming things there can even be harder. If you recognize this struggle, I have some good news for you. With the help of the Azure Naming Tool, applying a good and consistent naming strategy becomes a lot easier. Why naming conventions matter Consistency and Clarity : Consistent naming conventions provide clarity, making it easier for team members to understand the purpose and function of each resource. This reduces confusion and enhances productivity. Simplified Management : A structured naming convention simplifies resource management by grouping related resources together and enabling straightforward identification. This is particularly useful in large-scale environments where numerous resources are deployed. Enhanced Security and Compliance : Proper naming conventions can help ensure compliance with security policies and regulatory requi...

The introduction of the DeepSeek R1 model has sent shockwaves through the AI industry, challenging established norms and redefining the economics of AI development. This model has demonstrated that we can train AI models more cost-effectively and in an environmentally friendly manner, without sacrificing performance. By leveraging innovative techniques, DeepSeek has shown that it's possible to achieve remarkable results without the exorbitant costs and environmental impact typically associated with AI training. I think this is good news for all of us. As a big believer in the advantages that LLMs has to offer, I always feel somewhat uncomfortable knowing the environmental impact that these models have both during training and execution. DeepSeek has shown us that a different path is possible, providing a better balance between productivity and (environmental) cost. My hope is that other AI players will now re-evaluate on how to move forward and start applying the same techniques ...