This plea for help is specifically for non-coding, but still deeply technical work.
I guess the best start would be to have a person to organize volunteers.
As of May 2023, 65% of the Ukrainian refugees that left Ukraine starting February 2022 and decided to stay in Poland found a job—so, within around a year, as opposed to 5-6 years as in the article. Cultural similarity here is likely making it much, much simpler. For those who want to read more about the situation of Ukrainian refugees in Poland, this report by Polish National Bank (Narodowy Bank Polski, NBP) might be useful: https://nbp.pl/wp-content/uploads/2023/05/Raport_Imigranci_EN.pdf (in English!), there is a lot of interesting details.
From my experience, despite all the citogenesis described in other comments here, Wikipedia citations are still better vetted than in many, many scientific papers, let alone regular journalism :/ I recall spending days following citation links in already well-cited papers to basically debunk basic statements in the field.
Good question! I quickly found this table, though this is yearly statistics only: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3510019201
One reason (among many) is that employment in American companies is less stable than in Europe with strong employment laws. Twitter could not do the same type of layoffs in Europe, with stories like this one being pretty common. But this safety net has a cost, and the cost is a part of total employment cost for employers. Whether the safety net is worth it for employees in IT, that's another matter—but it can't not be taken into account because of the law.
BTW, in some European countries there is a strong culture of IT workers doing long-term contractor work exactly to trade off employment laws for (usually quite a lot) higher wage.
He likely couldn't "just" do it. The synchronization overhead for federation is large, and with the amount of data Reddit has, you'd have to put a lot of effort into writing efficient code to handle that. Or pay for a lot of servers doing it.
BTW, it would be interesting to see whether current lemmy codebase could handle it as well…
I found it crazy useful to study old, established, mature technologies, like relational databases, storage, low-level networking stack, optimizing compilers, etc. Much more valuable than learning the fad of the year. For example, consider studying internals of Postgresql if you're using it.
Given these criteria, ggplot2 wins by a landslide. The API, thanks to R's nonstandard evaluation feature, is crazy good compared to whatever is available in Python. Not having to use numpy/pandas as inputs is a bonus as well, somehow pandas managed to duplicate many bad features of R's data frame and introduce its own inconsistences, without providing many of the good features¹. Styling defaults are decent, definitely much better than matplotlib's, and it's much easier to consistently apply custom styling. Future of ggplot2 is defined by downstream libraries, ggplot2 is just the core of the ecosystem, which, at this point, is mature and stable. Matplotlib's activity is mostly because that lack of nonstandard evaluation makes it more cumbersome to implement flexible APIs, and so it just takes more work. Both have very minimal support for interactive and web, it's easier to just use shiny/dask to wrap them than to force them alone to do web/interactive stuff. Which, btw, again I'd say shiny » dask if nothing but for R's nonstandard evaluation feature.
Note though that learning proper R takes time, and if you don't know it yet, you will underestimate time necessary to get friendly. Nonstandard evaluation alone is so nonstandard that it gives headaches to people who'd otherwise be skilled programmers already. matplotlib would hugely win by flexibility, which you apparently don't need—but there's always that one tiny tweak you would wish to be able to do. Also, it's usually much easier to use the platform's default, whatever publishing platform you're going to use.
As for me, if I have choice, I'm picking ggplot2 as a default. So far it was good enough for significant majority of my academic and professional work.
¹ Admitably numpy was not designed for data analysis directly, and pandas has some nice features missing from R's data frames.
At such scale, a scraper wouldn't be necessary, that's easily doable by humans involved in these communities—with a human touch as well.
Yes, many times. And I recall using the technique manually back when I was working with Subversion many, many years ago. No fun stories though, sorry, it's just another tool in a toolbox.
So far I've been following recommendations from this person: https://old.reddit.com/r/NewMaxx/comments/16xhbi5/ssd_guides_resources_ssd_help_post_your_questions/