Category: facebook

Preserving the Essence of Soviet Animation | December 13 2025, 15:05

In Soviet times, there was a great school of animation that led the world for many decades. If you search on YouTube for “Vovka in the Land of Far Far Away”, it almost exclusively shows restorations 🤮, and at the same time, it shows the same disgusting restorations of heaps of other cartoons, all made in the same style (vectorization, black outlines). If you go to Wikipedia, it will display a screenshot from the restoration, not from the original 1965 cartoon. The original can be found, for example, by searching “vovka in the land of far far away madina gazieva”, but searching “vovka in the land of far far away soyuzmultfilm 1965” shows nothing at all.

They really broke the internet.

P.S. By the way, “two of a kind, fulfilling wishes,” and “good enough” resonate very much with today’s ChatGPT 😉

Harnessing GPU Power Beyond Machine Learning: A Data Processing Experiment | December 13 2025, 01:16

Torturing my supercomputer. Illustration that the GPU is not just for machine learning and some complex math.

My script takes a thick English dictionary (Webster) and multiplies it by 30, creating a list of 12 million words. Then, the algorithm looks through all 12 million words and replaces all the vowels with asterisks using regex. To add more load, a “word length” column is added, and then we take words longer than 10 letters and find the most frequent (top 5).

So, in Python this is

df[‘masked’] = df[‘text’].str.replace(r'[aeiou]’, ‘*’, regex=True)

df[‘len’] = df[‘masked’].str.len()

res = df[df[‘len’] > 10][‘masked’].value_counts().head(5)

and this code is executed first through the main processor, then through a GPU.

The main processor (I have the top-tier Intel i9 285k) completes this task in 24 seconds, while the Nvidia RTX 5090 does it in 0.51 seconds. That’s a 46 times difference!

[Pandas CPU] Top Patterns:

masked

s*r w. sc*tt. 23280

s*r t. br*wn*. 23220

j*r. t*yl*r. 16140

bl*ckst*n*. 10860

b***. & fl. 10830

Name: count, dtype: int64

[Pandas CPU] Computation Time: 23.5596 sec.

Transferring data to GPU…

Transfer complete in 1.16s

— Running Benchmark: cuDF GPU —

[cuDF GPU] Top Patterns:

masked

s*r w. sc*tt. 23280

s*r t. br*wn*. 23220

j*r. t*yl*r. 16140

bl*ckst*n*. 10860

b***. & fl. 10830

Name: count, dtype: int64

[cuDF GPU] Computation Time: 0.5108 sec.

TOTAL SPEEDUP: 46.12x

Misadventures in AWS: Misusing aws-nuke for Configuration Exports | December 12 2025, 16:29

Just for laughs. I asked Gemini how to export the entire AWS configuration for local analysis, and they recommended using the aws-nuke command for permanently deleting everything, but if you add a dry-run flag, you’ll get the configuration… and someone actually follows such advice 🙂 and then we wonder

Two Weeks on Linux: From Mac to ArchLinux+KDE Bliss | December 12 2025, 16:24

Two weeks on Linux, wildly satisfied. After a Mac. I specifically have a setup of ArchLinux+KDE/Plasma 6.5. Everything here is customizable. For instance, I made a program from scratch in half an hour (no lie, thirty minutes) using Gemini that translates selected text to English or corrects errors if the selected text is already in English when ScrollLock is pressed. There seems to be an app for every situation in life, at least in my field. Everything flies (even though this is an Intel i9 285K/64Gb). I just enter a folder that contains 470,000 files, and it opens instantaneously. I’ve never seen anything like this anywhere else. I launch IntelliJ Idea, and there is practically no delay between clicking the icon and the editor being ready with the loaded project. All devices connected perfectly, unlike with the Mac, for which there are simply no drivers for my HP LaserJet 1018 and I need to perform tricks.

Now I occasionally switch to a Mac, and it drives me crazy that the hotkeys are different. Of course, they can be reconfigured for Mac, and probably I will do that. Muscle memory builds up, and switching quickly doesn’t work out. I miss iMessage a bit – I’m used to writing and responding to messages from the computer. Apple iMusic works, through a browser.

Overall, the impression is very good so far.

Stages of Understanding Scientific Papers | December 10 2025, 19:38

As I periodically read scientific papers on my topic, I will try to articulate the levels of understanding the truth.

Level 0: “Read Later Folder” Downloaded the PDF, the title sounds genius, the abstract seems like the solution to all my problems. The file is forever buried in the ~/Downloads/Papers/ToRead folder.

Level 1: “Sumerian Cuneiform” Don’t understand anything at all. Random symbols, the Greek alphabet is over. “Orthogonal extrapolation of cognitive entropy within a quasi-stationary discourse inevitably induces a bifurcation of transcendental synergism.” Such materials really lower self-esteem. Most often from this level, you either fall back to zero, or gradually move to the second level.

Level 2: “Illusion of Competence” The Abstract is clear, the Introduction reads like a good detective story. But as soon as the main section starts, the text turns into a pumpkin. I can’t paraphrase it in my own words, only in general phrases: “Well, they trained a neural net… kind of.”

Level 3: “Formulas where needed and where not” The Abstract is clear, the first half of the article is also okay (architecture, pictures). But then comes formula (4), where “magic” happens. I take the authors’ word for it that equation (3) leads to (4) because, of course, I won’t check it. Beyond that — sheer horror and belief in a miracle.

Level 4: “Goldfish Effect” While reading — everything is crystal clear. The logic is solid, conclusions are obvious, the authors are smart. I close the tab, someone asks me, “What was the article about?” — and I freeze. My mind goes blank. If you take away the paper, I can’t reproduce even the idea because there essentially isn’t an idea, there is a process.

Level 5: “Armchair Expert” Everything’s clear, I can retell the essence over a beer. I know that Input transforms into Output, but the “black box” inside is still black. Give me a computer, I wouldn’t be able to reproduce even the skeleton because, it turns out, the article lacks half of the important stuff.

Level 6: “Critic-Practitioner” Everything is clear, I can recount, understand how to reproduce (even without their code). I see where they cut corners. I definitely know that the “state-of-the-art” result is achieved only thanks to a lucky seed or dataset and this strange trick in preprocessing, mentioned in the footnote on page 12.

Level 7: “Deconstructor” Hooray, I’ve understood everything and implemented it myself. It works worse than in the article, but I know why. However, I understand this work better than the second author (who just made charts). I see that all this complex mathematics over 5 pages boils down to two paragraphs in the middle.

Level 8: “Nirvana” The article is trivial. The idea is secondary, it was all in the ’90s with Schmidhuber, just named differently. Formulas are overcomplicated for importance. I can write the same in 10 lines of code and it will work faster. Reject.

If anything — I’m stuck somewhere between 2 and 4.

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.

Nostalgia and Innovation: The Story of Starchat.ru | December 09 2025, 23:41

2003. We had a chat, my creation, Starchat.ru, where people constantly hung out and communicated with each other. It had a Java applet! Nobody even remembers what that is nowadays, probably. Initially, some programmer I found on the internet wrote this thing, who then disappeared, and I took over the support.

Just for laughs, I made a bot that you could chat with by simply sending it a private message. It was always online, and not everyone realized that it was a bot. When the robot received a message, it searched through massive chat logs for messages that contained the most words from the query and had some response. A response is the next message directed at the user by someone (like “Vasya: oh just go you know where!” is a response to Vasya’s message). In the chat interface, you had to click on a message and then reply to it. In the presence of several options (and there were always several options, given the traffic of chatters), a random one was chosen.

It turned out to be a robot that very amusingly answers questions. If you ask it what its name is, it always replies with different names but appropriately, with emojis and suffixes, often swearing. Also, the bot always gave adequate responses to standard questions like “where do you live” or “how old are you.” Since there was a huge history, and they talked about everything in general, it was hard to find a question to which the system did not give an interesting/correct/funny answer.

So, the bot had an interesting side effect. If you start swearing at it offensively, it begins to swear back even more offensively. And generally, it often reacts inadequately to attacks and reproaches. Simply because in real conversations, a polite question is answered politely, and a rude one — of course, rudely. The audience had a lot of fun with this bot.

It was especially interesting to read the bot’s logs afterward. People there didn’t understand that it was a robot. They asked it questions, quarreled and made up with it. It was fun)