I have used Alpine Linux at work and I don't like the fact that they only have one version of each package in their repo. First of all, that creates a risk that a given version is bad and I cannot go back to a known good version. Also, some times I explicitly want to use an old version of a package. New versions change and remove features.

…and Python, Java, and GoLang.

At least with Java, many of the cornerstone packages have a corporate sponsor maintaining them.

I would be curious what the daily exercises are going to be. Is it just a 24 part tutorial on the etiquette around creating and contributing to open source projects?

I was a bit surprised that deque is implemented as a linked list and not, for example, a ring buffer. It would mean that index reads would be constant time (though insert and delete at an index would be linear time), the opposite of using a linked list.

pip is a perfectly usable package manager and is included in most python distributions now. Is it perfect? No, but it is good enough for every team I have been on.

C++ is unique in that it is wildly dominant in its niche. I am sure that any developer who has worked with another object oriented, manually memory managed, systems programming language (are there any other popular ones out there?) should have no trouble picking up C++.

Did you find the answer to your question, and if not, could you explain it better?

Also, a quick tip: if you are using Python 3, you don't need to join your variables before passing them into print. print accepts any number of arguments, converts them to strings, and prints them as a single line separated by spaces (which is exactly what your code seems to be doing).

The point of using a cache is to have data in memory and not on disk. From what I can tell, Postge Unlogged tables are still written to (and read from) disk. It is just that the write is done in an unsafe way.

The article explains how one would go about benchmarking performance but forgets to actually include performance metrics. Luckily they link to another write up that does. Using an Unlogged table vs. a regular table reduces write times about 45% and gives you about 3 times as many transactions per second. It is not nothing but it is probably not worth the code complexity vs. writing directly to a persistent table.

Even the "no persistence" behavior of a cache is not strictly true: an unlogged table is only truncated if Postgre is shut down unexpectedly (by kill -9 the process or by killing the VM). If you restart if you shut down the process in a controlled manner, the unlogged table is properly persisted and still has data when it starts.

In my opinion, Python is still missing one key feature: the removal of the Global Interpreter Lock, which is finally starting in Python 3.13.

We tried to ask our interview question of ChatGPT. After some manual syntax fixes, it performed about as well as a mediocre junior developer, i.e. writing mutithreaded code without any synchronization.

Don't misunderstand, it is an amazing technical achievement that it could output (mostly) correct code to solve a problem, but it is nowhere good enough for me to use. I would have to carefully analyze any code generated for errors, rewrite bits to improve readability (rename variables to match our terminology, add comments, etc), and who knows what else. I am not sure it will save me much time and I am sure it will not be as good as my own code. I could see using an AI to generate sophisticated boiler plate code (code that is long, but logically trivial).

The immediate use for this that jumps out at me is batch processing: you take n inputs and return n outputs, where output[i] is the result of processing input[i]. You cannot throw since you still have to process all of the valid input.

This style also works for an actor model: loosely coupled operations which take an input message and emit an output message for the next actor in the chain. If you want to be able to throw an exception or terminate prematurely, you would have to configure an error sink shared by all of the actors and to get the result of an operation, you so have to watch for messages on both the final actor and the error sink.

I knew basic CLI commands (such as cd and ls) for a while, but did not do learn much more. Some things have helped me grow my skills:

• Necessity: Some times I need to do something on a VM or container that does not have a graphical interface installed. Some utilities only have a command line interface and not a graphical client. My only option is to Google how to do it. The more I do it, the less I have to Google and the more focused my searches become (instead of searching for "How to do x", I search for "How to do x in utility").
• Learning from others: For many tasks, I follow internal or external guides, which typically use CLI commands. Often I look at how my coworkers accomplish tasks and pay attention to what commands they use. Then, when I have time, I look up any new commands I saw and decide if they will be useful for me too. Lately, I have been doing code reviews that involve shell scripts. Those are especially nice, because I can take my time, going line by line, and understand what each command does.
• Keep notes: Every time I find a command that I think I will need again, I copy it into a text file (and I have many such text files). It also makes it easier when I need to run the command with slightly different arguments (a different commit id or something), I can just edit the command in my editor (with searching and undo) and paste it in to my terminal with all the flags and arguments correct.