The art of simplicity

Yesterday I talked about a problem we encountered when trying to inject a scoped service into ASP.NET Core middleware. The middleware used was convention based middleware, one of the ways to construct middleware in ASP.NET Core. With convention based middleware there isn’t a base class or interface to implement. The only rules your middleware class needs to apply to are: It has a public constructor with a parameter of type RequestDelegate . It contains a public method named Invoke or InvokeAsync . This method must: Return a Task . Accept a first parameter of type HttpContext . Convention based middleware is registered with a singleton lifetime, so you can only constructor injection for the dependencies with a singleton lifetime. For transient and scoped dependencies you need to add extra parameters to the InvokeAsync() method: There is a log of magic going on when using convention based middleware and it shouldn’t be a surprise that pe

A colleague contacted me with the following problem; when running his ASP.NET Core application it failed with the following error message: Cannot resolve IApiLoggingService from root provider because it requires scoped service NHibernate.IInterceptor In this post I walk you through the different steps we took to investigate the issue and explain how we solved it. But before I dive into the problem itself I first want to give some background info on dependency injection in ASP.NET Core and service lifetimes. Dependency injection and service lifetimes ASP.NET Core supports the dependency injection (DI) software design pattern, which is a technique for achieving Inversion of Control (IoC) between classes and their dependencies. Registration of a dependency is done in the built-in service container, IServiceProvider . Services are typically registered at the app's start-up and appended to an IServiceCollection . Once all services are added, you use BuildServiceProvider to

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