Today we celebrate the eleventh birthday of the Go open source release. The parties we had for Go turning 10 seem like a distant memory. It’s been a tough year, but we’ve kept Go development moving forward and accumulated quite a few highlights.
Source: Eleven Years of Go, an article by Russ Cox.
Every now and then I get questions on how to work with git in a smooth way when developing, bug-fixing or extending curl – or how I do it. After all, I work on open source full time which means I have very frequent interactions with git (and GitHub). Simply put, I work with git all day long. Ordinary days, I issue git commands several hundred times.
I have a very simple approach and way of working with git in curl. This is how it works.
Source: This is how I git, an article by Daniel Stenberg.
You’re processing a large amount of data with Python, the processing seems easily parallelizable—and it’s sloooooooow.
The obvious next step is switch to some sort of multiprocessing, or even start processing data on a cluster so you can use multiple machines. Obvious, but often wrong: switching straight to multiprocessing, and even more so to a cluster, can be a very expensive choice in the long run.
In this article you’ll learn why, as we:
- Consider two different goals for performance: faster results and reduced hardware costs.
- See how different approaches achieve those goals.
- Suggest a better order for many situations: performance optimization first, only then trying parallelization.
Source: Optimizing your code is not the same as parallelizing your code, an article by Itamar Turner-Trauring.