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.
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.
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.
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.
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)
ifnot 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.
I like the idea I got from one of Joscha Bach posts that everything we hear is automatically executed by our brain like it is own thoughts even when we don’t pay attention to it. And that it is a security vulnerability that it opens a gate for all sorts of exploitations. I keep returning to this thought frequently and so I decide to put it on “paper”.
The summary of my thoughts on the topic. We have that loop hole that can be exploit in multiple ways from little verbal abuse, or manipulation to full-fledged propaganda attacks. The most devious part is that it can crawl under your skin even if you don’t even pay attention to it. It is probably even more contagious when you are not pain attention since your guard is off. You can resist certain ideas when you hear then once, twice, ten times but after a hundred of repetitions they will eventually get through unless there is something in you that will make those ideas completely unacceptable for you.