It's important to know that these numbers will vary based on what you're measuring, your hardware architecture, and how your particular Python binary was built.
For example, my M4 Max running Python 3.14.2 from Homebrew (built, not poured) takes 19.73MB of RAM to launch the REPL (running `python3` at a prompt).
The same Python version launched on the same system with a single invocation for `time.sleep()`[1] takes 11.70MB.
My Intel Mac running Python 3.14.2 from Homebrew (poured) takes 37.22MB of RAM to launch the REPL and 9.48MB for `time.sleep`.
My number for "how much memory it's using" comes from running `ps auxw | grep python`, taking the value of the resident set size (RSS column), and dividing by 1,024.
1: python3 -c 'from time import sleep; sleep(100)'
If it really is cargo culting, and the people buying the physical product are not keeping the manufacturers in check because they never play the vinyl, then I can see a potential situation where manufacturers ramp up to meet "demand" but at lower quality (improved profits).
The secondhand market becomes saturated with inferior pressings that are inevitably bound for landfills since they don't meet the quality/expectations of the people who actually play vinyl.
This doesn't make any sense; there's no craft here, where it's cheaper to press "bad" records vs "good" ones. You would literally need multiple production lines to intentionally execute this "strategy". Also a record cost next to nothing to make.
Writing go in yaml and forgetting everything else we learned software engineering. Proper ide's, being able to make abstractions, not copy pasting, structured templating and thus not string based templating, should I go on?
I also use Ollama for coding. I have a 32G M2 Mac, and the models I can run are very useful for coding and debugging, as well as data munging, etc. That said, sometimes I also use Claude Sonnet 3.5 and o1. (BTW, I just published an Ollama book yesterday, so I am a little biassed towards local models.)
The flip side is companies that are not active participants in the open source community (but know they use open source), are pinging all their engineering managers and asking "are we exposed to this!? how do you know!?".
So while it's useless noise to you, it's likely triggered by being on the receiving end of communications like "Hey, my boss is asking if $PROJECT is vulnerable because of a terrible article he read in $MAINSTREAM_MEDIA_PROPERTY?" times however many bosses are harassing their reports.
"I don't want to craft an email reply to every single person, just put up the no-op blog post and be done with it."
I've generally seen this with Unix installers from commercial software vendors.
You get a giant .sh file that displays a license, asks you to accept, then upon acceptance, cats itself, pipes through head/tail, into cpio to extract the actual assets.
Furthermore, the attacker covered their tracks on the initial payload with an innocuous paragraph in the README. ("Nothing to see here!")
bad-3-corrupt_lzma2.xz has three Streams in it. The first and third
streams are valid xz Streams. The middle Stream has a correct Stream
Header, Block Header, Index and Stream Footer. Only the LZMA2 data
is corrupt. This file should decompress if --single-stream is used.
The strings of `####Hello####` and `####World####` are there so that if you actually follow the instructions in the README, you get a seemingly valid result.
They're shell comments so it won't interfere with payload execution.
And lastly, they act as a marker that can be used by a later regex to locate the file _without_ referencing it by name directly nor using the actual Hello and World strings.
For example, my M4 Max running Python 3.14.2 from Homebrew (built, not poured) takes 19.73MB of RAM to launch the REPL (running `python3` at a prompt).
The same Python version launched on the same system with a single invocation for `time.sleep()`[1] takes 11.70MB.
My Intel Mac running Python 3.14.2 from Homebrew (poured) takes 37.22MB of RAM to launch the REPL and 9.48MB for `time.sleep`.
My number for "how much memory it's using" comes from running `ps auxw | grep python`, taking the value of the resident set size (RSS column), and dividing by 1,024.
1: python3 -c 'from time import sleep; sleep(100)'
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