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Ah, just the day before yesterday it was 26 degrees…

Tomorrow I need to choose one of these books, and I’m torn which one. This is my plan for the year. Who has read what?

I encountered an interesting and quite complicated topic. There are three words: motherland, fatherland, homeland. Translations—motherland, fatherland.

It turns out that “motherland” is a pseudo-Russianism, existing only at the intersection of Russian and English languages.

The word it translates, “motherland,” generally corresponds to the English “homeland” in how and when it is used—except that it is used slightly more often, so sometimes when translating from Russian to English, it is better to rephrase it as something like “our land” or “his/her native country” depending on the context.

The most important thing is that this is not a special term for Russia; it can refer to anyone’s motherland anywhere.

So where did the word “motherland” come from?

Apparently, it was a rare English synonym for “motherland” that then became associated with Russia thanks to the images of the Motherland during World War II; the expression, in turn, became a method by which domestic Soviet wartime propaganda solved the problem of addressing pre-Soviet patriotism without mentioning the country as “Russia.”

From here arose an interesting thing: “motherland” began to be taught in Russian schools as a common English equivalent to “motherland,” along with other quirks of Russian school English, such as “library named after Lenin” (for what in more authentic English would simply be “The Lenin Library”).

And then it was re-imported into the English language as this illusory “word that Russians use to denote Russia as a homeland,” which, again, has no real equivalent in the Russian language.

As a result, you will hear both Russians and non-Russians use “motherland” in English, but the meaning isn’t quite the same.

A Russian using “motherland” is likely a beginner to intermediate English speaker, mistakenly thinking that it is a common English equivalent for “motherland,” i.e., “Homeland.” Similarly, a beginner English speaker whose native language is German might call a mobile phone a “handy.”

Ultimately, “motherland” was born from interlingual confusion, perceived in Russia as a common English word denoting the homeland, and outside Russia as the equivalent of some special Russian word that does not actually exist.

By the way, the word “fatherland” is generally associated with the Nazi regime in English-speaking environments, where it was used particularly often.

(Learned from Reddit)

Interestingly, only 1.3% of the U.S. population is engaged in farming. These farms produce goods worth $200 billion. Out of that, $177 billion is for export. In other words, 1.3% of the population feeds themselves and the remaining 98.7% and there’s still an eightfold reserve left.

I looked at the data for Belarus – 7.2% of the population exports goods worth $6.6 billion. In Russia in 2015, it was 9% and $25 billion.

Re-reading Sapiens, I’ve reached the chapter where Yuval talks about the various horrors of animal husbandry for meat and dairy. Did a quick Google on meat farms with ‘Happy’ in their name. There’s a ton of them! Here’s one, for example. The photo shows a piglet that couldn’t be happier. To the right, some merchandise, it says I like my butt rubbed, and my pork pulled.

In the USA, 100,000,000 pigs are slaughtered every year. A pig’s pregnancy lasts 3 months, 3 weeks, and 3 days, producing 8-12 piglets who stay with their mother for one and a half to two months (in the USA – 1-2 weeks), and then are sent to a separate place. Or not, as in the photo. This cycle is repeated 3-5 times with the mother, after which they say goodbye to the sow forever. The piglets are sent to their deaths six months after birth.

Interestingly, it wasn’t enough for Americans to have miles, pounds, yards, ounces, gallons, and Fahrenheit—turns out, taxi drivers used to charge 10 cents for each additional 1/9 mile. Reminds me of the story where they reduced milk packages from 1 liter to 900 grams.

A very interesting text about Machine Learning and ChatGPT in particular by Stephen Wolfram, the creator of WolframAlpha. Overall, it’s really the very basics, but there are interesting details flying between them, especially in the second half. Plus, it’s excellently presented. A good link to refresh knowledge, and for those not in the loop — get

his blog is very insightful

This week it will be plus 26 °C.

There has been no snow this winter

Please provide the text you would like to have translated to English.

In the last post, I showed my (non) workspace. Here, I’ll talk about my “invention” for transferring references onto the canvas. I recorded the video about two months ago when there was still no oil paint. Well, now it’s the same, just with oil.

So, how it works. I attached a phone holder so that the phone faces the drawing. Back then, two months ago, I ran a special app, EpocCam Pro, which broadcast everything captured by the phone to my computer. The new MacOS has this feature built-in, so EpocCam Pro is no longer needed. On my computer, I made a simple program in Python/OpenCV “on the fly,” which takes the camera image and “projects” a semi-transparent reference image onto it. Since the camera looks at the canvas at a slight angle, there also happens to be a perspective correction.

In separate windows, a black-and-white image from the camera is displayed, allowing me to see my drawing not only reduced but also — separately — in monochrome.

That’s not all. I got carried away and created a feature to separate the image into layers based on brightness. You define the number of layers, and you can “project” onto the view from the camera all the dark spots (like from the range of 85-100%), then switch to all the less dark spots (like from the range of 75%-85%), and so on up to the brightest spots (0-10%). Theoretically, you can draw layer by layer, starting from the darkest and ending with the lightest. This might be something to try as well. If it works out, I’ll share. This trick doesn’t really take off with colored images, but with monochrome, it indeed does. Something else could possibly be devised for colored images. For example, separation by colors.

All in all, I made this thing a few months ago, and only tried it out today on oranges (see the previous post). Essentially, it helped me transfer the outlines onto the canvas faster and more accurately than I would have without any automation. Specifically for oranges, there’s practically no benefit, as there’s no place to make a mistake. Whether I would have drawn a lemon a bit more to the left or right, no big deal. But if it were something complex, it could theoretically be useful. I should try it with a portrait, for instance.