The Beacon API is used to send an asynchronous and non-blocking request to a web server. The request does not expect a response. Unlike requests made using XMLHttpRequest or the Fetch API, the browser guarantees to initiate beacon requests before the page is unloaded and to run them to completion.

A Jujutsu tutorial that requires no previous experience with Git or other version control systems.

This web app displays notes and slides in separate windows,
keeping both synchronised.
It accepts simple, double-width, or double-height PDF presentations:

I have a lot of thoughts about the design of compiler intermediate representations (IRs). In this post I’m going to try and communicate some of those ideas and why I think they are important.

Extra is different than More. Extra is finishing those two screens, but then researching a new library for form validation that might reduce the boilerplate code. Or it's learning ways to protect against common security vulnerabilities from data entry. These little off-ramps from the main highway of Normal Work could be dead-ends and not have any practical value to the project. But they might also be important contributions. And that's the thing with Extra. While the tangible value to the project is uncertain (it could be nothing this time or it could be something), the value to you is real.

There are a lot of guides about how to use async Rust from a "user's
perspective", but I think it's also worth understanding how it
works, what those async blocks actually mean.

When people say Rust is a “safe language”, they often mean memory safety.
And while memory safety is a great start, it’s far from all it takes to build robust applications.
Memory safety is important but not sufficient for overall r…

Reading through the Emacs 30 NEWS file and picking
out the stuff I think is the most interesting.

A fun, hands-on guide to writing your own compiler for a real-world programming language.

Most functional languages rely on some garbage collection for automatic memory management. They usually eschew reference counting in favor of a tracing garbage collector, which has less bookkeeping overhead at runtime. On the other hand, having an exact reference count of each value can enable optimizations, such as destructive updates. We explore these optimization opportunities in the context of an eager, purely functional programming language. We propose a new mechanism for efficiently reclaiming memory used by nonshared values, reducing stress on the global memory allocator. We describe an approach for minimizing the number of reference counts updates using borrowed references and a heuristic for automatically inferring borrow annotations. We implemented all these techniques in a new compiler for an eager and purely functional programming language with support for multi-threading. Our preliminary experimental results demonstrate our approach is competitive and often outperforms state-of-the-art compilers.

Official website of the Pimalaya project.

In this article, you will learn how to install and configure OpenBSD to reduce its network activity over clearnet

There’s plenty of information out there on how to scale Django to handle numerous requests per second, but most of it…