The last year, maybe year and a half, I was thinking about this one thought — that Hayao Miyazaki’s movies are sort of a manifestation, a love letter to regular women who demonstrate strength not through exercising power or physical abilities, but by drawing it from love and care for the people and things they care about the most. In Nausicaä, we see a little girl who believes in the friendly intent of nature, standing between her people and a seemingly hostile world, establishing peace through sacrificing herself for the things she loves. Similarly, in Spirited Away, Chihiro takes care of her family and her friends, starting as a person who doesn’t care much about anything but herself. She is a child, after all — moving from one city to another, new school and so on. Why would you expect any different? But then she grows through hard labor and a bunch of obstacles. She demonstrates care for people you wouldn’t expect her to care about — Lin, the girl she met in the bathhouse who became her friend, then even Yubaba’s baby, then Haku. They were all a little hostile towards her at first, and still that pulling desire to help, to save the day. ...

Examples of applying the POSIWID lens. Introduction In management cybernetics, there is a well-known formulation: “the purpose of a system is what it does” (POSIWID). It is commonly used as a practical way to analyze complex systems—organizations, institutions, social practices, and recurring patterns of behavior. The idea is to set aside declared goals, missions, and explanations, and instead observe the actions and effects a system reliably produces over time. Not isolated decisions or stated intentions, but stable patterns—especially those that become visible under stress, growth, or conflicting incentives. ...

Intro Another year is coming to an end. People summarize their year and make plans. Reflecting on mine, I once again noticed that books — like many other things we choose and spend time on — tend to reflect who we are at a particular moment in life rather accurately. This year, that mirror suggests I was more concerned with people and personal development than with technology. Perhaps that’s self-deception, or a way of avoiding other topics. It doesn’t really matter. Books are just books. ...

Recently, I’ve often heard that hierarchies are bad, supposedly representing nothing but inequality and oppression. This topic unexpectedly surfaces in seemingly unrelated contexts. I encountered it in a book about startup culture, my children brought it up from school, and there was tension in chats on unrelated topics whenever structures resembling hierarchies were mentioned. On one hand, I agree that some hierarchies can be built on force and suppression. On the other hand, I see them more as symbiotic structures rather than tools for power struggles. ...

Periodically, I contemplate a thought from the book “We Who Wrestle with God” by Jordan B. Peterson, which suggests that at the gates of Heaven, we are met by a flaming sword that will burn away everything unworthy of Heaven, everything imperfect. He also discusses how, in life, we encounter these flaming swords in the form of people who are so much better than us that we feel uncomfortable being around them. We avert our eyes from them and avoid them like the blazing sun, or we try to extinguish their light, for example, through criticism, devaluation, and even scheming. ...

Intro There are two topic that people rarely discuss when in comes to data annotation. First, that annotators are not just things that needs to be trained, and whose performance needs to be closely monitored but regular human beings like any one of ourselves as people who can get tried, or distracted. And second, how the data we push through those people influences their annotators’ performance. As I feel compassion, and deep respect for annotators I work with on daily basis I wanted to cover that topic in one of my writing but never had time to do so as well as bandwidth to conduct a proper study. With help from, AI and all researches who has done the ground work, I can at least share a short summary. ...

The book focuses and explores in depth the key traits of successful companies like Amazon, Apple, Microsoft etc. It differentiates four main areas: ownership, openness, speed, and science. From all four openness is mentioned as the key to all other features as it naturally enables them. For myself, I summarize the book as two aspects: environment, and speed. The company needs to focus on creation of an environment where ideas can move freely, basic needs like access to code (including changing the code), data, documentation, support easily available. In addition to that negative feedback not only welcomed but actively sought so any issues can be discovered as soon as possible so they do not become too large of a problem. This requires an atmosphere where people are not punished for mistakes and so there is no implicit incentive for covers up. ...

Intro As continuation of my attempts to regain control over my attention, two month ago I switched my phone to first black, and white mode, and then I applied the same filter at 30% on my laptops and then reduced the filter intensity to 20-30% on all my devices. I helped a bit, actually black, and white helps quite significantly but it is a bit tiering to use. So after the first month I’ve noticed the following changes: ...

I’ve always wanted to have a way to build data processing pipelines in Python using pipes, like this range(10) | F(is_odd) | P(lambda x: x * 2), instead of functions and generators and maps and loops. So I’ve tried … The idea is pretty simple: let’s create a class with implemented OR and ROR operators, the pipes. def __or__(self, other): other.source = self return other def __ror__(self, other): self.source = ( iter(other) if not isinstance(other, (str, bytes)) and hasattr(other, "__iter__") else other ) return self The tricky part was implementation of __next__ since I wanted it to be a lazy operation. After a few trials and errors I’ve ended up with a pretty simple approach where the wrapping class implementing the pipe will call next to its source, added by OR or ROR, apply a transformation and then return the result of the transformation. ...

a simple visualization of the difference between frequentist and Bayesian power and how effect size, noise (standard deviation), and sample size affect the results.