The art of simplicity

At one of my clients, Seq is used as the main tool to store structured log messages. If you never heard about Seq before, on their website they promote Seq like this: The self-hosted search, analysis, and alerting server built for structured logs and traces. It certainly is a great tool, easy to use with a lot of great features. But hey, this post is not to promote Seq, I want to talk about a problem we encountered when using it. A developer contacted me and shared the following screenshot: Instead of the structured log message itself being logged, we got the error message above for a subset of our messages. The problem was that the structured log message contained a large JSON object exceeding the default limit of 262144 bytes. Although you could wonder if it’s a good idea to log such large messages in general, for this specific use case it made sense. So you can override the default for a specific application by setting the eventBodyLimitBytes argument when configurin

Microsoft announced last month, the .NET Smart Components, an experimental set of AI-powered UI components that can be added to your .NET apps. They asked us to give these components a try and share our feedback. And that is exactly what we are going to do in this blog post. Right now we have 3 components available: Smart Paste Smart Textarea Smart Combobox If you want a good overview of the available components, check out the video from Steve Sanderson: Although the video shows some compelling use cases for each of these components, we have to start somewhere. So in this post I’ll focus on the Smart Combobox as it doesn’t require any language model backend. Here is the main use case that Smart Combobox tries to solve: The problem with a traditional combobox is that if there are a lot of options available, it can be a challenge to select the right item from the list. With Smart Combobox,  the component uses semantic matching to find an item from the list t

In this post I want to introduce you to Ollama . Ollama is a streamlined tool for running open-source LLMs locally, including Mistral and Llama 2. It bundles model weights, configurations, and datasets into a unified package managed by a Modelfile. Ollama supports a variety of LLMs including LLaMA-2, uncensored LLaMA, CodeLLaMA, Falcon, Mistral, Vicuna model, WizardCoder, and Wizard uncensored. Installation To install Ollama on Windows, first download the executable available here: https://ollama.com/download/OllamaSetup.exe Run the executable to start the Installation wizard: Click Install to start the installation process. After the installation has completed Ollama will be running in the background: We can now open a command prompt and call ollama: Download a model Before we can do anything useful, we first need to download a specific language model. The full list of models can be found at https://ollama.com/library . Here are some examples: Model

There is a lot of hype around platform engineering these days. The concept of creating platform teams and building platforms to deliver software at scale is not new as companies like Google, Facebook, Netflix etc. have a long history of building developer platforms. And also the much quoted Team Topologies book by Matthew Skelton and Manuel Pais was already released in 2019. Some wonder if this is just a fancy new name for existing practices and if there is a risk that these developer platforms become a bottleneck, exactly what they are supposed to alleviate.  Some dare even say that we made today’s development landscape so horrendously complex that we need these platforms to hide all this complexity. No matter if you think Platform Engineering is overhyped or not, before you go on this journey I would like to share the following quote: If your users haven’t build something that surprised you, you probably didn’t build a platform I saw this quote in the Build abstracti

There are a lot of concepts and papers that have influenced the way I design and build software. One concept that certainly should be on the list is 'Autonomous Computing' as introduced by Pat Helland more than 20 years ago. If you have never heard about this concept, I would recommend to first read this paper before you continue reading my blog post. Back? Let’s continue. In the paper Pat introduces the concept of Fiefdoms and Emissaries and Collaborations . Fiefdoms refer to independent, autonomous components within a computing system. Each fiefdom is responsible for a specific aspect or function of the overall system. They operate independently and have their own decision-making capabilities. Emissaries are entities that facilitate communication and coordination between fiefdoms. They act as messengers, relaying information and requests between different components of the system. Emissaries enable fiefdoms to interact and collaborate without direct dependencies o

As a follow-up on the presentation I did at CloudBrew about Azure Static Web Apps I want to write a series of blog posts. Part I - Using the VS Code Extension Part II - Using the Astro Static Site Generator Part III  – Deploying to multiple environments Part IV – Password protect your environments Part V – Traffic splitting Part VI – Authentication using pre-configured providers Part VII – Application configuration using staticwebapp.config.json Part VIII – API Configuration Part IX – Injecting snippets Part X – Custom authentication Part XI – Authorization Part XII -  Assign roles through an Azure function Part XIII -  API integration Part XIV – Bring your own API Part XV – Pass authentication info to your linked API Part XVI(this post) – Distributed Functions As I mentioned in a previous post, when creating an Azure Static Web App, you get a managed function for free. However where the static part of your Azure Stat