Digests » 212
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this week's favorite
Whether you want to look at what your code is doing ‘under-the-hood’ or you’re trying to see what the ‘internals’ of the CLR look like, there is a whole range of tools that can help you out. To give ‘credit where credit is due’, this post is based on a tweet, so thanks to everyone who contributed to the list and if I’ve missed out any tools, please let me know in the comments below.
This is part 2 in a series of posts on using .NET Core platform intrinsics. Part 1 discussed the implementation of SHA-256 using specialized instructions available in the ARMv8 Cryptography Extensions. Part 2 will describe the accelerated implementation of AES encryption using hardware instructions that are part of the same instruction set.
ML.NET is an open-source and cross-platform framework and available as NuGet package. You can check the code here. It was originally developed in Microsoft Research and it is used across many Microsoft products like Windows, Bing, Azure, etc. One very cool thing about this framework is that it can be extended to add machine learning libraries like TensorFlow, Accord.NET, and CNTK. Before we dive into the details of this framework let’s have a brief introduction to Machine Learning and type of problems that it solves.
In this post, we are going to write about what we consider to be the best practices while developing the .NET Core Web API project. How we can make it better and how to make it more maintainable.
I got a call that on newer hardware some performance regression tests have become slower. Not a big deal. Usually it is a bad configuration somewhere in Windows or some BIOS settings were set to non optimal values. But this time we were not able to find a setting that did bring performance back to normal. Since the change was not small 9s vs 19s (blue is old hardware orange is new hardware) we needed to drill deeper.