or subscribe with
Join 19,000+ readers for one email each week.
Digests » 341
this week's favorite
It has been a while since I actively developed in C#. I mostly worked with C# and .NET during the 3.0 to 4.5 days and I did async/await work very early on, so I skip over that as well. After a job change, I didn’t touch C# for actual work. I mostly just watched the development from the sidelines via news. Today, I take a short look at some features. I will skip a lot and just add some of my highlights tour.
We have recently looked a lot more into ML.NET which will result in a range of new features on elmah.io. While the documentation from Microsoft is good, it is split up into multiple pieces which can make it hard to figure out how to build a real-world example with ML.NET. In this post, I will show you one of the pieces that I believe are missing: how to train and retrain a model.
We take many environmental factors for granted when it comes to running our .NET applications. Information about the operating system may seem insignificant for folks deploying to rigorously maintained target environments. Still, for folks who publish desktop client software, the luxury of choosing the destination is not an option. This post will be using .NET to determine the operating system and architecture our .NET application is currently running within. We’ll also resolve the SDK version our app is utilizing. Finally, we’ll run our .NET code under three different operating systems to see the results: macOS, Windows, and Linux.
My goal was to find the fastest low-level CSV parser. Essentially, all I wanted was a library that gave me a string for each line where each field in the line was an element in the array. This is about as simple as you can get with a CSV parser.
Here are some of the most popular NuGet packages used in ASP.NET Core applications. Use these packages to get a running start on your project.