I recently read Concurrency in Go by Katherine Cox-Buday. In the “Queuing” section, there was a discussion of how we can use Little’s Law to predict our pipeline’s throughput, given sufficient sampling. I honestly wondered why I had not come across this simple idea before, after finishing that part. As I understand it, it can potentially be used in almost any situation where a queue is involved. Not just message queues, even things like physical queues. So I thought I’d write an intuitive explanation to help it stick and share the idea.

When I saw a cool animation, I used the browser's native screen capture API to get a high-quality screen recording, audio included.

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A cross-platform, high-performance and asynchronous web server for static files-serving ⚡

A personal code notes blog

How will this decade be remembered? Musically, it's unclear. In this special two-part report, Gabriel Szatan examines the forces fragmenting electronic culture today, and where we might go next.

Sorry if this isn’t the right category, I think it might fit better in brainstorming but I can’t post there. I’m working on an allocator API for Mojo, and I wanted to get thoughts and opinions from (1) other systems programmers and (2) from the community around what I think may be one of the only widely used allocator APIs. Most of my work is in places where the lack of async (or other facility to manage coroutines) causes some fairly substantial problems, so I haven’t been able to use Zig as m...

A small, reverse-engineered menu bar app that drives per-key RGB on Razer keyboards on macOS. Backgrounds free; effects keep the lights on for the person who built it.

View AI tools as a complement, not competition

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