Perhaps one of the biggest difficulties in setting up a Linux system for desktop/home use is the fragmentation of the ecosystem, with many different options claiming to get you from point a to somewhere in the vicinity of point b, each with their subtle differences (and at least a few “gotchas” along the way). An easy example: in 2020, you’d think there would be an easy answer to getting a trackpad/touchpad up and running with support for multi-touch gestures at least on par with the experience on Windows and macOS – after all, it’s been 12 years since Apple made multi-touch popular with 2008 MacBook Air.
I have a soft spot in my heart for rust and a passionate distrust (that has slowly turned into hatred) for interpreted, loosely typed languages, but it’s hard to deny the convenience of being able to bang out a bash script you can literally just write and run without having to deal with the edit-compile-run loop, let alone create a new project, worry about whether or not you’re going to check it into version control, and everything else that somehow tends to go hand-in-hand with modern strongly typed languages.
A nifty but scarcely known rust feature is that the language parser will ignore a shebang at the start of the source code file, meaning you can install an interpreter that will compile and run your rust code when you execute the
.rs file – without losing the ability to compile it normally. cargo-script is one such interpreter, meaning you can
cargo install cargo-script then execute your source code (after making it executable,
:! chmod +x %) with something like this:
It’s been a while since we first released our SecureStore.NET library for C# and ASP.NET developers back in 2017, as a solution for developers looking for an uncomplicated way of safely and securely storing secrets without needing to build and maintain an entire infrastructure catering to that end. Originally built way back in 2015 to support secrets storage in legacy ASP.NET applications, SecureStore.NET has been since updated for ASP.NET Core and UWP desktop application development, and now we’re proud to announce the release of SecureStore 1.0 with multi-platform and cross-framework support, with an updated schema making a few more features possible and official implementations in C#/.NET and Rust.
Microsoft’s official documentation on adding custom model binding providers to convert between (typically) a string and a custom type for complex model binding in ASP.NET Core as of .NET Core 3.1 goes something like this:
- Create an
IModelBinderfor your class and use
[ModelBinder(BinderType = typeof(MyModelEntityBinder)]to decorate each and every binding site, e.g.
public async Task<IActionResult> OnPost([ModelBinder(BinderType = typeof(MyModelEntityBinder)]) MyModel model), which provides the runtime with the type information it needs to instantiate the model binding provider and convert the input to a model.
- Optionally create an
IModelBinderProviderclass and register it with the ASP.NET Core host to provide the type information ahead-of-time (once and for all), so that you can instead use the barebones and much shorter decoration at each model binding site instead:
public async Task<IActionResult> OnPost([ModelBinder] MyModel model)
The latter is significantly easier on the eyes and far less error prone… but where does the type registration take place? Per the linked documentation, the recommendation is the following in
One of the nicest things about ASP.NET Core is the availability of certain singleton models that greatly simplify some very common developer needs. Given that (depending on who you ask) one of the two hardest problems in computing is caching1, it’s extremely helpful that ASP.NET Core ships with several models for caching data, chief of which are
IDistributedCache, added to an ASP.NET Core application via dependency injection and then available to both the framework and the application itself. Although these two expose almost identical APIs, they differ rather significantly in semantics.2
Have you ever needed to compare the contents of two files (or other streams), and it mattered how quickly you got it done? To be frank, it doesn’t normally come up in the list of things you may need on a daily code-crunching basis, but that rather depends on what kind of programs you tend to write. In our world, let’s just say it’s not an uncommon task.
At a first blush, it would seem to be no harder than comparing two arrays. A pointer reading from each file, compare bytes as you come across them, and bail when things differ. And it would be that easy if you were to use memory-mapped files and let the OS map a file on disk to a range in memory, but that has some drawbacks that may not always be OK depending on what you’re trying to do with the files (or streams) in question. It also requires having a physical path on the filesystem that you can pass in to the kernel, and it unduly burdens the kernel with some not insignificant workloads that aren’t (in practice) subject to the same scheduling and fairness guarantees that user code would be, and they can tend to slow down older machines significantly1.
Especially under Windows ↩
- German Swiss (QWERTZ)
- International Spanish
NeoSmart.Collections release announcement, we had need of an updated logo for Microsoft’s .NET, and were unable to find something corresponding to .NET Standard.
For those of us that were around when C# was first introduced, this is probably the logo that most represents the .NET Framework:1
I tried extremely hard to find this in a higher resolution or even just losslessly resized, but to no avail. Give me a holler if you have something better we can preserve! ↩
NeoSmart Technologies is pleased to announce the immediate availability of its open source
NeoSmart.Collections library/package of specialized containers and collections designed to eke out performance beyond that which is available in typical libraries and frameworks, by purposely optimizing for specific (common!) use cases at the cost of pretty much everything else.
In many regards, the data structures/containers/collections that ship with pretty much any given framework or standard library are a study in compromise. Language and framework developers have no idea what sort of data users will throw at their collections, or in what order. They have no clue whether or not a malicious third party would ultimately be at liberty to insert carefully crafted values into a container, or even what the general ratio of reads to writes would look like. The shipped code must be resilient, fairly performant, free of any obvious pathological cases with catastrophic memory or computation complexities, and above all, dependable.
It isn’t just language and framework developers that are forced to make choices that don’t necessarily align with your own use case. Even when attempting to identify what alternative data structure you could write up and use for your needs, you’ll often be presented with theoretical 𝒪 numbers that don’t necessarily apply or even have any relevance at all in the real world. An algorithm thrown out the window for having a horrible 𝒪 in Algorithms and Data Structures 101 may very well be your best friend if you can reasonably confine it to a certain subset of conditions or input values – and that’s without delving into processor instruction pipelines, execution units, spatial and temporal data locality, branch prediction, or SIMD processing.