Category: Education

Exploring the Dynamics of Russia’s Kyykka Sport Federation | June 06 2026, 13:49

DID YOU KNOW that in Russia there’s a Russian Skittles Sport Federation with a president, a first vice-president, and a regular vice-president. All in blazers. There’s a presidium, and it has a chairman of the commission on international relations. There’s an entire apparatus for the president of skittles sport with three advisors and a responsible secretary. They hold conferences, at least in 2018 and 2020. They have a skittles march, music by A. Roshchin, lyrics by V. Avdeev, I. Vinogradsky. There are 18 regional departments and 28 regional federations with their own hierarchy.

The website has a section “Anti-Doping”. Interesting, doping in skittles sport… There’s a subsection called “methodological recommendations”. Also, their charter talks about online skittles competitions. Imagine that, online!

In 2024 there was a World Championship in Skittles Sport. Apparently, they are supposed to hold it every three years. And it had a Grand Closing. Apart from Belarus, athletes from Germany and Kazakhstan participated in the world championship. From Germany, in addition to Sergey, Vitaliy, and Konstantin, there was Eugen Schlein, or simply, Zhenya. From the development program for 2026-2029, it turns out that players from Congo, Ghana, Guinea, and Ecuador are actively training now. In the selection criteria for the national team, there is a requirement for “game thinking”. To be admitted, you need to come with a certificate, oh, a certificate of passing the anti-doping education from an institution, whatever that means.

At the world championship, the disciplines are “classical skittles” and “European skittles” (and separately Finnish ones). The goal in both is to knock the skittles out of the town. European ones appeared in Germany because the emigrants from the USSR were told that it’s not customary here to throw three-kilogram stones and were given lighter ones.

In short, it’s all serious.

Mastering Cross-Posting: From Facebook Frustrations to Dual Blogging Excellence | May 23 2026, 14:28

I have perfected the cross-posting from Facebook to my two blog sites [which almost no one visits] – beinginamerica.com and raufaliev.com. When a new post is published on Facebook, a mechanism is triggered to translate the post into English, process attached images, generate descriptions for them, create a title based on the text of the post and descriptions of the images, generate tags from the same basis, record the post in turso db – this is a cloud database, free up to certain limits, create embeddings via openai, record in qdrant cloud – this is also a cloud database, but vector-based, and finally, upload images to wordpress via API, and publish the post in English and Russian via API.

All would be well, but of all the APIs, the silliest one is Facebook’s. Firstly, for pages like mine, transitioned to New Experience, it’s almost impossible to use most of this API. Well, it’s possible, but you have to spend a long time proving to Facebook that you really need it, by showing startup documents, demonstrating the application, etc. Obviously, they are reluctant to deal with something that takes content out of their system. In addition, the token that gives access to the latest messages is relatively short-lived (possibly a few weeks), and it needs to be obtained anew through a browser only. So, any automation requires regular attention, otherwise it breaks.

If you mess up and don’t offload the latest posts through this Facebook Graph API in time, they just disappear from the list of recent ones and that’s it, no more API access to them. The only way is to request an archive download from Facebook. This download is also rather silly – it requires a lot of transformations and removing unnecessary stuff. For example, in the file containing posts, which I process, for some reason there are links that I sent in comments without accompanying text. And the comments are in a separate file!

To assign tags, I had to solve a separate challenge. Here’s the thing: there are about 10,000 posts over all time. That’s a big chunk, and you can’t build tags from it because it doesn’t fit into the contextual window of the LLM. But you need to. So, I did this: a script takes random posts from the 10,000 in such a volume that their total size is just below the specified limit in tokens, and at the end of this block, it adds the prompt “generate the most common tags for me, 30 pieces” (I simplify the prompt used). In the end, I ran this 10 times and got 10 sets of tags with 30 pieces each, generated for different slices of the database. That made 300 tags, some of which are complete duplicates, while others are synonyms and closely related in meaning. All this is fed into the LLM, and we get a list of tags and a hierarchy of tags. Now we have a limited set of tags that reflect the 10,000 posts as closely as possible. Turns out, that in almost 20 years on Facebook, my breakdown is as follows:

Tag Posts

==================================================

#Russia 3412

#Thoughts 3146

#Tech 3105

#Culture 2765

#Hobbies 2726

#AI 1603

#Science 1367

#Software 1358

#Travel 1298

#Learning 1138

#Society 1050

#Nature 958

#Education 915

#Business 902

#Art 894

#Programming 889

#Humor 840

#History 807

#Gadgets 750

#Moscow 713

#USA 614

#Cinema 567

#Webdev 493

#Music 476

#Sports 473

#Mindset 443

#Auto 400

#Books 386

…

and so on. This list includes both tags from the limited list and tags that the LLM appointed to content simply because it didn’t find anything suitable in the limited one.

Tags from the limited list became categories on the site. The rest of the tags + these just became regular wordpress tags.

As for image search. I had two ideas on how to do it. The first – OpenCLIP. It’s pretty straightforward but requires hosting the model somewhere. Easy on my machine, but inconvenient to start it each time, plus I planned to move the migrator to a cheap server on Amazon. It’s also okay to calculate in cloud models, but you have to pay a bit, which is yet another dependency. But the main thing – it works quite well without it. I generate descriptions for images using OpenAI, which is used for translating into English anyway, and then create embeddings using a large model. So far, all search tests are a great success. Especially when there’s text on the image, and it’s a big question whether OpenCLIP would have interpreted it successfully.

In the end:

1) wordpress raufaliev.com – free

2) wordpress beinginamerica.com – free

3) turso db where all posts are stored – free

4) qdrant cloud where embeddings are stored – free

5) openai for translation and image descriptions – not free, but inexpensive (cost $30 for post processing over a year).

I attach two screenshots – how the search by images works, and by texts, as well as the migrator dashboard.

Celebrating a Milestone: Rauf Aliyev’s Programming Qualification from 1994 | March 21 2026, 13:54

Mom sent it. This was given to me when I graduated from school. The education was quite good back then, at least. Part of the science classes were conducted at the institute.

When Cosines Defy Reality: Humor in the Trenches of Science and War | March 11 2026, 22:00

“Comrades cadets, in wartime the value of cosine can reach 2, and in exceptional cases, when the situation on the fronts demands it, even 3!”

My Ambitious 2026 Plan: From Galapagos Travel to Academic Achievements and Creative Pursuits | January 20 2026, 04:44

My plan for 2026:

– Travel to the Galápagos Islands, Ecuador for a week (summer)

– Finish and release a book on Information Retrieval (also summer, progressing slowly, first couple of chapters are already written. Already spent about 50-100 hours on this, the easy part)

– Release at least one scientific paper, probably on Data Mining (spring). Ideally, submit it somewhere to a journal (challenging). Already spent about 30 hours on this topic, a lot left to do.

– Make a step towards a PhD. Find professors, visit universities, understand the cost and assess my capabilities and resources.

– Continue studying fundamental mathematics and not die (linear algebra, calculus, probability theory, statistics, classical ML). In 2025, I spent about 200-400 hours on this topic.

– Continue studying Deep Learning and reach the “can teach” level. In 2025, I spent about 100-200 hours on this topic.

– Continue studying Data Mining/NLP.

– Update my book on RecSys, releasing version 2.0 with updates and corrections (autumn 2026)

– Make noticeable progress in painting and playing the piano. Specifically, learn Schubert’s serenade (Ständchen, D 889) completely and create at least one canvas that I wouldn’t be ashamed to give as a gift.

Unveiling Scientific Misnomers: A Cross-Cultural Exploration | January 14 2026, 04:46

Today I was surprised to learn that the Coriolis force is pronounced as CoriolIs force, not coriOlis force as we were taught in school. I started to investigate what else was wrong, and discovered something amazing.

It turns out what we called Gay-Lussac’s law is known as Charles’s Law in the rest of the world, and what we called Charles’s Law is known throughout the world as Gay-Lussac’s Law.

The Cartesian coordinate system here is Carthesian. Cartesius is just the Latinized name of René Descartes.

In our textbooks, the law of conservation of mass is called the Lomonosov-Lavoisier Law (what enters the chemical reaction = mass of the substances formed). In the rest of the world, it is exclusively the Law of Lavoisier (Lavoisier’s Law). Lomonosov got included here only because “whatever is taken from one body is added to another”.

Also, it turns out that if you have to explain Pythagoras’ theorem to someone in English, without a hint, it’s absolutely impossible to guess that it’s Pythagoras. Greek names are generally a mess. Thales here is pronounced as Teelis.

For some reason, in physics Roentgen is called RentgEnom, although it’s Röntgen with the emphasis on ö.

In Russia, a trapezoid is a quadrilateral with two sides parallel and two not. In the USA, our trapezoid is known as Trapezoid, and the word Trapezium here refers to a quadrilateral with no parallel sides at all. In the UK, it’s the opposite. Our trapezoid is Trapezium, and the “skewed” quadrilateral is Trapezoid.

Comparing US and Russian Higher Education Systems through Credit Hours | December 10 2025, 17:35

Regarding education in the USA and the USSR/Russia. My degree in the USA is evaluated as a Master of Science degree in Computer Science. My younger colleagues say that a Russian university degree is rarely recognized as a Master’s these days, and often hardly qualifies even for a Bachelor’s. I decided to look at the numbers and was very surprised.

To earn a bachelor’s degree in the USA, you need to spend about 2000 hours in classrooms/laboratories. In terms of credits, this equals 120 credit hours. One credit usually equals 1 hour (50 minutes) of lectures per week for a semester (15 weeks). Laboratory work has a different coefficient (often 2–3 hours in the lab count as 1 credit), so the actual number of classroom hours is slightly higher (closer to 2000+).

So, my diploma states that I spent 7908 hours in classes over five years. That’s four times more than the typical student in the USA. Based on the numbers, it turns out that I spent about 2000 hours on math, physics, and English alone over five years, with a total of 42 subjects.

A colleague shared that in his Russian bachelor’s diploma there are 3140 academic hours, which is twice as less. And can you share how many hours are in your diploma?

Year of graduation, university, specialty, and the number of hours? I’m curious about the range of variation.

Navigating Complexity: The Challenge of Wikipedia’s Expert-Driven Content | November 26 2025, 01:06

Wikipedia has one big problem. Well, or we have it with Wikipedia. If you go to almost any Wikipedia page about a relatively complex mathematical or physical concept, you often suddenly don’t want to read it any further. Formally everything is correct there, but the explanation is given through concepts, often even more complex than the concept being explained. Besides, there is often a lot of unnecessary information — what is formally/academically/taxonomically part of the topic, but essentially “pollutes” the first impression.

This problem arises because the authors of Wikipedia (often mathematicians) prioritize rigor and completeness rather than didactics and comprehensibility.

In the English-speaking environment, this is sometimes called “Drift into pedantry”. Articles are often written by experts for experts, not for those who are trying to learn the subject from scratch.

Let’s take, for example, a “tensor”. Imagine a student who has heard that tensors are used in machine learning (Google TensorFlow) or physics and wants to understand the essence.

What the reader expects (intuition): “A tensor is a table of numbers (or some sort of data container) that describes the properties of an object and correctly changes if we rotate the coordinate system”

What Wikipedia provides: “A tensor (from Latin tensus, ‘strained,’ as per the classical layout of mechanical stress at the sides of a deformable cube, see illustration) — is a layout (arrangement in space) of numbers (components), used in mathematics and physics as a special type of multi-index object, possessing mathematical properties.” The article immediately starts listing ranks, covariance and contravariance of indices. This is formally correct but it “pollutes” the first impression.

The illustration at the very top is captioned like this: “Mechanical stress, deforming a cube with faces perpendicular to the coordinate axes, in classic elasticity theory is described by the Cauchy stress tensor, which links 2 indices: the normal vector to the face with the stress vector T (force per unit area); there are 3 directions of normals and 3 directions of stress components, which gives a 2nd rank tensor 3×3 — consisting of 9 components.”

Formally — not a single error. In fact — it’s a wall of text that requires knowledge of linear algebra just to read the definition.

It’s as if you asked “What is an apple?”, and you were responded with: “An apple is a fruit of plants from the subfamily Amygdaloideae or Spiraeoideae, featuring an epicarp, mesocarp, and endocarp, often participating in Newton’s gravitational experiments.”

On one hand, it seems like with the emergence of LLM, Wikipedia is no longer necessary. There are conditional LLMs like ChatGPT, which essentially paraphrase everything that is in Wikipedia in the required form. But they do it because they were trained on Wikipedia, and undoubtedly Wikipedia was given much more weight during training than other internet junk. If there was no Wikipedia in the training set, it would be much more difficult. Meanwhile, Wikipedia is constantly edited, and LLM and Google use it exactly when answering questions.

Therefore, on the one hand, it seems to me that it is high time for Wikipedia to transition to generating on the basis of expert-curated data and packaging knowledge in the required format, for example, in the form of questions and answers. On the other, the whole idea of encyclopedia master-data for LLM/RAG is lost.

The paradox is that LLM is, in essence, the only “interface” that was able to read these pedantic definitions of Wikipedia, “understand” them (through thousands of examples of code and articles) and translate them back into humane language. Wikipedia has become an excellent database for robots, but a poor textbook for people.

Rediscovering the 1986 “Chemical Trainer”: A Pioneer in Interactive Learning | November 23 2025, 15:55

At my home in Kolomna, I have a book called “Chemical Trainer” from 1986. I have never seen anything like it before or since.

The material of each of the 54 programs is divided into many small, very short sections, or categories. At the end of each category, one or more questions are posed. This is done to check whether the content of the category is truly understood. For each answer, there is a place in the book to jump to in order to see if the answer is correct. If the answer is wrong, it describes why and asks a new question. If correct — you move further in this quest.

These Germans in 1986 created an interactive textbook even before it became fashionable.

Metchnikoff: Beyond Science and Survival | November 13 2025, 04:53

I was reading Metchnikoff’s biography (don’t ask why I ended up there) and thought about how much can fit into one life. He wasn’t just a scientist, but rather like a saga:

His elder brother Ivan was the prototype for Leo Tolstoy’s “The Death of Ivan Ilyich.” Another brother, Lev, was a prominent anarchist, sociologist and fought in Italy alongside Garibaldi. Metchnikoff himself tried to end his life twice: the first time after the death of his first wife (who, sick with tuberculosis, was carried to the church on a chair). He took morphine but survived. The second time was when his second wife Olga fell critically ill with typhus. He deliberately inoculated himself with relapsing fever. Fortunately, both survived. However, the Grim Reaper with his scythe only came after his third consecutive heart attack.

The dude graduated from university at 19 as an external student. I.M. Sechenov himself recommended him for a professorship. But Metchnikoff was “blackballed” (rejected) by one vote. In protest, Sechenov resigned along with him.

He founded the first bacteriological station in the country at that time in Odessa. But due to an employee mistake (they spoiled the anthrax vaccine) an entire flock of sheep died. After this scandal, he left Russia. The station — on Leo Tolstoy Street.

In Paris, he was immediately taken under the wing of Louis Pasteur (the father of pasteurized milk), who supported his theory and gave him a lab in his institute. There, Metchnikoff worked for 28 years, becoming the deputy director.

While studying cholera at the Pasteur Institute, Metchnikoff proposed a theory that not everyone who comes into contact with the pathogen gets sick. He suggested that it’s all about… (of course) the gut flora. To prove it, he deliberately drank a culture with cholera vibrios. Nothing happened (it would have surely happened to you, Metchnikoff thought)

In the end, he received the Nobel Prize for the discovery of phagocytosis (cellular immunity). He is also “the father of gerontology” — Metchnikoff was the one who proposed the theory that to achieve longevity, one must combat bad bacteria in the gut with probiotics. Now, they say, gerontologists around the world drink sour milk on May 15th remembering Metchnikoff.

He died in Paris, and his ashes are kept in the library of the Pasteur Institute.

Also, in the English Wikipedia he’s Élie Metchnikoff. Not easy to guess.

In the photo, Metchnikoff and Leo Tolstoy are discussing immunology.