Global Leaders in the Sneaker Market | February 11 2025, 22:05

Today we went shopping for sneakers, and I decided to investigate which countries are currently the world leaders in sneakers.

Overall, no surprises—the US is in the absolute lead. Germany and Japan are notable. The rest are catching up.

American brands—at least 9 of them: Nike (+Converse), New Balance, Brooks, Saucony (+Merrell), Reebok, Skechers, Vans, Hoka. Purely sport-wise, probably 7 from the list.

Japanese—Asics, Mizuno.

German—Adidas, Puma (by the way, both founded by the Dassler brothers, yet they are competitors). Swiss—On. Korean—Fila.

Of course, production is all in China, Vietnam, Indonesia.

Personally, I’ve been buying almost exclusively Asics for a long time. They are very comfortable, although the design is so-so, a mere pass.

By the way, want an interesting fact you probably didn’t know? The thin layer of felt on the sole of Converse sneakers was added (at least as of 10 years ago—it was added) not for functional reasons but for economic ones. Footwear with a fabric sole was subject to lower customs duties when imported compared to footwear with a rubber sole because it was classified as slippers. And the duty was reduced from 37.5% to 3%.

Who else from other countries – are there any brands that are very noticeable and popular in your markets, and have yet to make it to the US?

A Walk Through the Pentagon: A Glimpse Inside America’s Defense Headquarters | February 11 2025, 21:23

Today, I walked through the corridors of the Pentagon.

The Pentagon is the headquarters of the United States Department of Defense, located in Arlington. It is the second-largest office building in the world, built in the shape of a pentagon.

There will be no photos because they asked to leave phones and even Apple Watches at the entrance. But honestly, there’s not much to capture. It’s not that the spectacle is utterly dreary, but overall, 90% of the corridors (and there are 28 kilometers of them) look almost the same as 90% of the corridors in any American university. That is, everything is clean, bright, tidy, and that’s it. The only difference is that at a university, you find bulletin boards with interesting things on the walls, but in the Pentagon, there are no boards in the corridors, everything is hidden. Everything else is the same. Endless doors of heightened dreariness with numbers and code locks, some corridors adorned with patriotic installations. I’m sure there’s a lot of interesting stuff behind many of these doors, but to enter many of them, you need to leave your phone out in the corridor (and I remind you, I left mine at the entrance).

About 26,000 people work in the building. About a third of them are civilians, the rest are military. Although the Pentagon is located in Arlington, Virginia, it has a Washington address — 1400 Defense Pentagon, Washington, DC 20301-1400. It’s said that the Pentagon has six Washington ZIP codes, and that the US Secretary of Defense, the Joint Chiefs of Staff, and each of the four branches of the armed forces have their own ZIP code (like 20301, 20318, 20310, 20330, 20350, and 20380).

The building was constructed in 1943, so at that time they built separate restrooms for blacks and whites due to segregation. Of course, it’s not like this anymore.

Since 26,000 people work in the building — that’s essentially the population of a small town, and parking there is quite limited (large, but still insufficient), there’s a metro station serving the Pentagon that’s practically unnecessary for anything else. Inside the perimeter, there’s everything needed to last until the end of the workday — Subway, McDonald’s, Dunkin’ Donuts, Panda Express, Starbucks, Sbarro, KFC, Pizza Hut, and Taco Bell, pharmacies, even a Best Buy.

From an architectural perspective, it’s a very interesting project. Look, with such a number of people and such expanses, you can get from any point to any point in no more than 10 minutes. No elevators, just wide corridors and stairs. Even in some emergency evacuations, rescuing people would be much easier. Although, of course, there was a sad experience in 2001 — remember, the plane hijacked by terrorists crashed into the building. Then, a hundred and fifty Pentagon employees died, and of course, everyone on board that plane.

Around the Pentagon is Crystal City — a typical city with shopping centers and multi-story residential complexes of varying degrees of luxury, and on the other side is Arlington National Cemetery, where 400,000 people are buried.

Exploring Sous Vide: Adding to My Kitchen Gadget Collection | February 11 2025, 02:55

Well, now I’ve finally gotten around to sous vide. As a result, the kitchen’s electrical gadgetry involved in cooking now includes the Power Quick Pot electric pressure cooker, the Ninja air fryer, the Crock-Pot slow cooker, and now the Anova sous vide. Made my first steaks, they turned out awesome, but next time instead of 150 F (65C) I’ll set it to 140F (60 C).

Alphabet Recall: A Simple Technique for Remembering Forgotten Words and Numbers | February 11 2025, 02:23

I have a life hack for recalling a forgotten word that works quite reliably in my case. Maybe, it will work for yours too.

It involves listing the letters of the alphabet, trying to recall that specific word by asking myself “does it start with A? B? C?”. And on the letter that the word actually starts with, I remember it entirely.

For instance, today I needed to recall a band from the 90s. I remembered nothing. No song titles, nothing I could quickly find by Googling. But I had a certain “picture” in my head. Probably, if I had struggled a bit more, I would have come up with search queries that would lead me where I needed, but I pulled out this technique and started going through the letters.

And as I was going through A, B, C, … at the letter K I remember — “Karmen”!

Sometimes, rarely, a “second pass” is necessary. Of course, it doesn’t always work, but on the other hand, if there’s absolutely no system, it’s unclear how to recall anything at all. This system exists, it’s a starting point, and it quite often works.

And as for remembering short numbers, to later recall them more easily, I mentally draw a zigzag line navigating the keypad of a button phone. This results in a visual squiggle, which serves as an additional mnemonic to the numbers. True, unlike the first approach, I use this one rarely, because in life, there’s rarely a need to remember and then recall numbers.

Bridging Brain Functions and Language Models through Predictive Processing | February 09 2025, 21:39

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I’ve been thinking that understanding how large language models (LLM; like ChatGPT) function explains how our (at least my) brain probably works, and vice versa—observing how the brain functions can lead to a better understanding of how to train LLMs.

You know, LLMs are based on a simple logic—choosing the appropriate next word after N known ones, forming a “context”. For this, LLMs are trained on a gigantic corpus of texts, to demonstrate what words typically follow others in various contexts.

So, when you study any language, like English, this stage is inevitable. You need to encounter a stream of words in any form—written or spoken—so that your brain can discover and assimilate patterns simply through observation or listening (and better yet, both—multimodality).

In LLMs, the basic units are not words, but tokens—words and often parts of words. After processing this vast corpus of texts, it turned out to be straightforward to find simply the most common sequences, which of course turned out to be somewhere full words, and sometimes parts of words. So, when you start to speak a foreign language, especially with a system of endings, you begin to pronounce the beginning of a word, and your brain at that moment boils over the “calculation” of the ending.

When we read text or listen, we actually don’t analyze words letter by letter, because very often important pieces just disappear due to fast or unclear speech, typos. But the brain doesn’t need to sift through all the words that look or sound like the given one, it needs to understand whether what is heard or seen matches a very limited set of words that could logically follow the previous one.

It’s a separate story with whole phrases. In our brain, they form a single “token”. That is, they are not broken down into separate words, unless you specifically think about it. And such tokens also appear in the stream not accidentally—the brain expects them, and as soon as it hears or sees signs that the phrase has appeared, the circle of options narrows down to literally 1-2 possible phrases with such a beginning, and that’s it—one of them is what was said or written.

But the most interesting thing is that recent research has shown: the human brain really works very similar to LLMs. In the study “The neural architecture of language: Integrative modeling converges on predictive processing”, MIT scientists showed that models that better predict the next word also more accurately model brain activity during language processing. Thus, the mechanism used in modern neural networks is not just inspired by cognitive processes, but actually reflects them.

During the experiment, fMRI and electrocorticography (ECoG) data were analyzed during language perception. The researchers found that the best predictive model at the time (GPT-2 XL) could explain almost 100% of the explainable variation in neural responses. This means that the process of understanding language in humans is really built on predictive processing, not on sequential analysis of words and grammatical structures. Moreover, the task of predicting the next word turned out to be key—models trained on other language tasks (for example, grammatical parsing) were worse at predicting brain activity.

If this is true, then the key to fluent reading and speaking in a foreign language is precisely training predictive processing. The more the brain encounters a stream of natural language (both written and spoken), the better it can form expectations about the next word or phrase. This also explains why native speakers don’t notice grammatical errors or can’t always explain the rules—their brain isn’t analyzing individual elements, but predicting entire speech patterns.

So, if you want to speak freely, you don’t just need to learn the rules, but literally immerse your brain in the flow of language—listen, read, speak, so that the neural network in your head gets trained to predict words and structures just as GPT does.

Meanwhile, there’s the theory of predictive coding, asserting that unlike language models predicting only the nearest words, the human brain forms predictions at different levels and time scales. This was tested by other researchers (google Evidence of a predictive coding hierarchy in the human brain listening to speech).

Briefly, the brain works not only to predict the next word, but as if several processes of different “resolutions” are launched. The temporal cortex (lower level) predicts short-term and local elements (sounds, words). The frontal and parietal cortex (higher level) predicts long-term and global language structures. Semantic predictions (meaning of words and phrases) cover longer time intervals (≈8 words ahead). Syntactic predictions (grammatical structure) have a shorter time horizon (≈5 words ahead).

If you try to transfer this concept to the architecture of language models (LLM), you can improve their performance through a hierarchical predictive system. Currently, models like GPT operate with a fixed contextual window—they analyze a limited number of previous words and predict the next, not exceeding these boundaries. However, in the brain, predictions work at different levels: locally—at the level of words and sentences, and globally—at the level of entire semantic blocks.

One of the possible ways to improve LLMs is to add a mechanism that simultaneously works with different time horizons.

Interestingly, can you set up LLM so that some layers specialize in short language dependencies (e.g., adjacent words), and others—in longer structures (e.g., the semantic content of a paragraph)? I google it, and there’s something similar in the topic of “hierarchical transformers”, where layers interact with each other at different levels of abstraction, but still, it’s more for processing super-long documents.

As I understand it, the problem is that for such, you need to train fundamental models from scratch, and probably, this does not work well on unlabelled or poorly labelled content.

Another option is to use multitask learning, so that the model not only predicts the next word, but also tries to guess what the nearest sentence or even the whole paragraph will be about. Again, google search shows that this can be implemented, for example, through the division of attention heads in the transformer, where some parts of the model analyze short language dependencies, and others predict longer-term semantic connections. But as soon as I dive into this topic, my brain explodes. It’s all really complex.

But perhaps, if it’s possible to integrate such a multilevel prediction system into LLMs, they could better understand the context and generate more meaningful and consistent texts, getting closer to how the human brain works.

I’ll be at a conference on the subject in March; will need to talk with the scientists then.

Unpacking Hidden Data Collection in Mobile Apps | February 08 2025, 16:20

I recently stumbled upon an intriguing study on the Timsh org website, where the author dissected how applications collect and transmit your data. The experiment employed an old iPhone device and intercepted traffic. A certain random application was installed on the phone for the experiment—it was Stack by KetchApp. The author intercepted the traffic and observed what was transmitted from the application to the outside world. A lot of data was transmitted, even when answering “no” to the question “Allow tracking?”.

Specifically, the IP address (which allows your location to be determined via reverse DNS), approximate geolocation (even with geolocation services disabled),

device model, battery charge level, screen brightness level, amount of free memory, and other parameters.

The data does not go to the company that created the application, but rather to various third parties. That is, these third parties collect data from most of the applications on your phone, and the data flows occur every time the application operates.

The author writes about two major groups of players – SSP and DSP.

SSP (Supply-Side Platforms) include those that collect data from the application—Unity Ads, IronSource, Adjust. There are also DSPs (Demand-Side Platforms), which manage advertising auctions, such as Moloco Ads, Criteo.

Advertisers gain access to the data through DSPs. Data brokers—aggregate and sell data. For example, Redmob, AGR Marketing Solutions. The latter sells databases that include PII, such as name, address, phone number, and even advertising identifiers (IDFA/MAID).

What data is sent? For instance, that Stack app from KetchApp sent to Unity Ads the geolocation (latitude, longitude), IP address (including server IPs, for example, Amazon AWS), unique device identifiers: IDFV (identifier for a specific developer) and IDFA (advertising identifier), as well as other additional parameters like the model of the phone, battery level, memory status, screen brightness, headphone connection, and even the exact system load time.

At DSPs, a RTB (real-time bidding) system exists for selling information. Data is transferred from the app via SSP (such as Unity Ads), and then to DSP (such as Moloco Ads), where auctions are held in real time to display relevant advertising. At each stage, data is transmitted to dozens, if not hundreds, of companies.

Yes, by answering “I do not want to share data,” you only deactivate the sending of IDFA (advertising identifier), but other data, such as IP address, User-Agent, and geolocation, and all these phone model and free memory, are still transmitted. Combined, they serve as a fingerprint at the moment, just like the advertising identifier. If desired, applications can still identify you by many parameters: IP address, device model, OS version, fonts, screen resolution, battery level, time zone, and other data, as they receive this information from hundreds of other places. Another question is that “end applications” do not need this, it is not free, but those who show you ads need this, and they have this info. And, of course, various special services can easily access it if necessary.

If you use several apps from one developer, the IDFV identifier allows linking data from all the apps.

Perhaps it’s not a secret at all, but almost every app sends data to Facebook (Meta) without asking for the user’s consent. That is, if you have Facebook on your phone, then bingo, any data from any other apps begin to be tagged with your profile, even if you have forbidden sharing information in those apps.

Companies exchange user data with each other. For instance, Facebook exchanges information with Amazon, Google, TikTok, and mobile SDKs (such as Appsflyer, Adjust) perform cross-linking of users between different services because such exchanges enhance the value and quality of information immediately for all participants.

Meanwhile, it turned out that Unity, which actually deals with 3D engines for games, primarily earns from selling these collected data. Specifically, in 2023, they had revenue from this direction amounting to $2 billion (“Mobile Game Ad Network”). In 2022, Unity absorbed IronSource — another giant of mobile advertising. IronSource deals with analyzing user behavior and optimizing monetization, as well as selling data to advertisers. Now, Unity through LevelPlay can manage not just ad placement but also data aggregation, selling them to other companies.

A significant portion of mobile games are created on Unity, especially free-to-play games. This allows Unity to have access to data from millions of devices globally, even without explicit user consent. Developers often do not realize how deeply Unity tracks data in their games.

Conclusion: disabling ads or prohibiting tracking at the OS level is just a minor obstacle. Data about you is still being collected, analyzed, and transmitted to hundreds of companies.

See the link below

Luck Over Talent: Decoding the True Drivers of Success | February 08 2025, 00:51

A lengthy post on how to achieve success! For free! No registration or SMS required! I just stumbled upon a scientific study proving that the role of chance in success is greater than that of talent. And this resonated with my belief that successful people are successful because they are lucky, not because they are extraordinarily talented, smart, or unusual. Rather on the contrary, they are so because they’ve been lucky. Note, not because they are “lucky ducks,” but because they’ve been lucky. These are different things.

Let me argue this. There’s a study “Talent vs Luck: the role of randomness in success and failure,” authors Alessandro Pluchino, Alessio Emanuele Biondo, and Andrea Rapisarda. Yes, the funny part is that Alessandro received the Ig Nobel Prize for this work (“a symbolic award for scientific discoveries that ‘first make people laugh, and then make them think'”). They used agent-based modeling to analyze the contributions of talent and luck to success.

As initial data, they took supposedly objective things: talent and intelligence are distributed among the population according to the normal (Gaussian) distribution, where most people have an average level of these qualities, and extreme values are rare, while wealth, often considered an indicator of success, follows the Pareto distribution (power law), where a small number of people own a significant portion of the resources, and the majority owns only a small share.

Further, the authors developed a simple model in which agents (1000) with varying levels of talent are exposed to random events over the hypothetical 40 years, which could be either favorable (luck) or unfavorable (misfortune). Each such event affects the “capital” of an agent, serving as a measure of his success.

Result: Though a certain level of talent is necessary to achieve success, it is often not the most talented individuals who become the most successful, but those with an average level of talent who experience more fortunate events. There is a strong correlation between the number of fortunate events and the level of success: the most successful agents are also the luckiest.

My observation of how the world works completely agrees with these conclusions. You just need to do things so that you’re more fortunate. That’s it. Don’t try to be the smartest—it doesn’t help as much as the following things do:

1) Being in environments where important events occur. Silicon Valley for startuppers. New York for financiers. Hollywood for actors. If an environment increases the chance of meeting “key” people, it makes sense to place oneself in that environment.

2) Creating more points of contact with the world and maintaining them. Running a blog, writing articles, giving interviews. Attending conferences, participating in communities. Calling and writing to acquaintances and semi-acquaintances, especially when such calls and letters are potentially important to them. Expanding the number of contacts—even if 99% are useless, 1% can change your life.

3) Increasing the number of attempts. The more projects, the higher the chance that one of them will “hit.” The best example – venture funds: they invest in dozens of startups, knowing that success will come from only one. Artists, writers, musicians create hundreds of works, knowing that only one will become a hit.

Unfortunately, for this point, you need to love your work. So choose a task where attempts are enjoyable.

Organizational psychologist Tomas Chamorro-Premuzic in his book “Why Do So Many Incompetent Men Become Leaders?” asserts that luck accounts for about 55% of success, including such factors as the place of birth and family wealth. This is true, but since you are sitting on Facebook on an iPhone with a cup of coffee and not herding cows in a loincloth in Africa, you already have pretty good initial conditions.

From here, an interesting conclusion — is it necessary to study at a university to achieve success in life? Look at the points above. Being in the right environment, creating more points of contact, increasing the number of attempts. Out of these three points, two work better in the case of face-to-face learning, while the third does not work well because the university consumes 4-5 years of life (and the university is one attempt). But the other two criteria are very important—during the period of study, the average student interacts with hundreds of peers, who can make a significant contribution to the likelihood of this student’s success.

But sitting at home with books for five years does not meet any criteria. Online education lies somewhere in between, see for yourself, it varies, but it’s closer to the option of “sitting with textbooks.”

The authors of the study confirmed the concept of “The Matthew Effect.” This is from the Bible: “For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath.” (Matthew 25:29). They explain why success accumulates even if it is initially random:

People who are fortunate in the early stages receive more resources, opportunities, and attention. This, in turn, increases their chances for new fortunate events. As a result, those who were initially in a better position continue to build on their success, while the rest lag behind.

This explains why wealthy people often receive profitable investments, popular artists become even more popular, and less known ones remain in the shadows, and companies that “hit the stream” attract more customers and resources than their less fortunate competitors.

That’s why success also requires following the principle of “Fake it till you make it.” Successful people often exaggerate their skills or achievements, and then catch up to the proclaimed level. Society easily forgives and quickly forgets such things, but when they work (and they often do), the person no longer really needs them. There’s also a self-fulfilling prophecy—the idea that if a person states something as a fact (even if it’s an exaggeration), they and those around them start behaving as if it’s true, and eventually, it becomes reality.

There’s also the principle of “there’s no harm in asking” (It doesn’t hurt to ask). The principle is that if the likelihood of success is increased by asking someone a question (“can you raise my salary starting in March or put me in charge of that project”), then it’s worth asking. You never know unless you ask.

And one more thing. Act now, apologize later. Actions speak louder than words. As you know, being at the right time in the right place not only involves the right place (this is the first point from my list), but also the right time. Therefore, just do it. People who don’t dream but act never end up homeless on the street because they rushed.

And finally. Time is a finite resource. There was a good idea about the sheet with squares—google “90 years of life in weeks.” You can color the lived weeks and look at the remaining ones.

So, in summary.

Success is determined by luck, not talent. Talent helps, but is often formed under the influence of success. Knowledge is useful, but experience is more valuable. Time is a finite resource. Planning doesn’t work, three things do:

1) being in an environment where important events occur,

2) creating more points of contact with the world and maintaining them,

3) increasing the number of attempts where luck might work.

Three principles:

1) Fake it till you make it

2) It doesn’t hurt to ask

3) Actions speak louder than words

The Paradox of Software Complexity and AI’s Role in Legacy Systems | February 07 2025, 14:30

It is fascinating to observe how, with increasing complexity and over time, software transitions into a state of being “a thing in itself”, where even the developers do not fully understand how it works, or more precisely, why it sometimes suddenly malfunctions, and prefer to minimally interfere with it, leading them to understand it even less over time, and it solidifies into what it is for years. This process is known as software rot or legacy paralysis.

However, bosses and the market demand development, so instead of fundamentally changing and improving something, developers add “bells and whistles” which grow alongside, rather than changing the core product. It’s well understood that diving into the core product might set you on a path leading to disappointments, deadline failures, layoffs, etc.

Interestingly, with the advent of AI, this problem will only intensify on one hand because the team will understand even less about how things work, but on the other hand, complexity can be managed better because AI can analyze complex matters more easily than a single biological brain.

For instance, AI could be used to create tests for existing code, as well as to perform anomaly detection and potential bug hunting, for creating documentation and explaining the code structure from simple to complex, and it might partly automate refactoring and detect performance bottlenecks.

I believe such AI solutions for working with legacy will soon be a major market.

Navigating Life with ChatGPT: My AI Assistant Addiction | February 05 2025, 21:04

So, I’ve developed a bit of a ChatGPT addiction. It has overtaken Google and Facebook and is slowly creeping into all areas of life.

(Specifically, I use not only ChatGPT because for certain needs we have to use an analog developed by our engineers on our internal corporate network, so everything below is not only about ChatGPT, but about AI assistants in general. But for personal needs, it’s only ChatGPT for me.)

(1) Over the last six months, I’ve probably created a couple hundred Python scripts for data processing. I didn’t write any of the scripts myself (although I could; ask me again in a year or two, I might no longer be able to). To write a script for processing data, I just clearly state what I need, then closely examine the result, and if I like it, I run it. If it doesn’t work, and something needs tweaking, I tweak it myself. If it’s completely off, I ask for it to be redone. Most often, I end up with what I need. Example: read a CSV, create embeddings for all lines, cluster them, then write the results in separate files with the cluster number in the name. Or implement some complex data grouping.

I must mention bash commands separately. For example, I can’t recall how to sort lines from a file by length using command line and get the longest ones. Or I’m too lazy to remember detailed syntax for awk or jq to process something from the files through a pipe, it’s easier to ask ChatGPT.

(2) Lately, I frequently translate between Russian and English using LLMs. Rather than writing something in English myself, it’s easier to write it in Russian, get the translation, and then throw it into an email. It’s simply faster. It’s not even about the proficiency in English – of course, I could write it all myself. It’s about how much time is spent on phrasing. The argument “it’s twice as fast and clearer” beats all else. A downside—my English isn’t improving because of this.

(3) Generally, I run nearly 100% of the English texts I write through various LLMs, depending on the type of text. I ask them to correct the grammar, then copy-paste the result wherever I need—into an email or a Jira ticket. It seems I will soon have an anxiety that I sent something unreviewed. Because they always find something to correct, even if it’s just a minor thing like a missing article or a comma.

(4) When I’m too lazy to read large chunks of English text, I frequently throw them into ChatGPT and ask for a summary—sometimes in Russian. Can’t do this for work because the texts are often from clients, but if it’s really necessary, I also have access to a local LLM.

(5) I’m increasingly validating various design decisions (not visual design, but software design) through ChatGPT/LLM. I ask for criticism or additions. Often, the results make me think about what needs to be improved or what assumptions need to be added.

(6) I also use it for summarizing YouTube videos. Just download the subtitles in TXT format through Youtube subtitle downloader, throw them into an LLM, and then you can request summaries or ask questions based on them. It really helps to decide whether to watch the video or not.

What are your usage patterns?