The Unix philosophy states that we ought to design programs that “do one thing well”. Nevertheless, the current trend is to design huge monoliths with multiple unrelated functions, with web browsers at the peak of that horrifying journey. However, let’s consider something else.
Does the same philosophy hold for algorithms and file formats? Is it better to design formats that suit a single use case well, and swap between different formats as need arises? Or perhaps it is a better solution to actually design them so they could fit different needs?
Let’s consider this by exploring three areas: hash algorithms, compressed file formats and image formats.
Continue reading “A format that does one thing well or one-size-fits-all?”
I’ve noticed that SyncThing didn’t seem to preserve modification timestamps for the photos and videos transferred from my phone. Since this messed up sorting by date, and the timestamps are present in filenames, I’ve made a trivial script to set mtimes appropriately.
Continue reading “A script to set file timestamps based on filenames”
I’ve recently written a thin wrapper over emerge that I use in my development environment. It does the following:
- set tmux pane title to the first package argument (so you can roughly see what’s emerging on every pane)
- beep meaningfully when emerge finishes (two beeps for success, three for failure),
- run pip check after successful run to check for mismatched Python dependencies.
Continue reading “My thin wrapper for emerge(1)”
Downstream testing refers to the testing of software package done by their redistributors, such as Linux distributions. It could be done by distro-specific CI systems, package maintainers or — as it frequently is the case with Gentoo — even distribution users.
What makes downstream testing really useful is that it serves a different purpose than upstream testing does. To put it shortly, upstream testing aims to ensure that the current code of the package works in one or more reference environments, and meets quality standards set by the package authors. On the other hand, downstream testing aims to ensure that a particular version of the package (possibly an old one) works in the environment that it will be used on, or one that closely resembles it.
To put it another way, downstream testing may differ from upstreaming testing by:
- testing a different (possibly old) package version
- using a diferent (possibly newer) Python version
- testing against different dependency versions
- testing against an environment with additional packages installed (that may interfere unexpectedly)
- testing on a different operating system, architecture, hardware, setup
While these may sound inconvenient and sometimes cause false positives, they have proven in the past to detect issues that went unnoticed by upstream and that could have broken production setups. Downstream testing is important.
Unfortunately, many test suites make assumptions that cause problems for downstream testers. Some of them can be worked around easily, others can not. In this article I’d like to discuss a number of these issues.
The most common way to represent hexadecimal (or any other base > 10) numbers is to use the first letters of alphabet for the extra digits. However, this doesn’t work well for my brain that insists that since A is the first letter, B is the second letter… then A = 10 + 1, B = 10 + 2… so I keep having to remember to shift this by one, and judging by the responses to my toot about it, it seems that I’m not alone.
I don’t think that I’ve made any useless invention that people would randomly find and say “oh, hey, what a nice unrealistic idea”. It’s time to make one! I present to you: the X-alpha hexadecimal notation!
Continue reading “X-alpha hexadecimal notation”