Why one busy service collapses while another keeps working, and how to build the second kind.

I was walking my dog when it occurred to me that Hayao Miyazaki’s films feel like a love letter to ordinary women. They pay close attention to the work of caring for others and how easily we take that care for granted. In Nausicaä, the heroine tries to understand a natural world that others see as hostile, risking her life to protect those she loves. In Spirited Away, Chihiro gradually learns to care for the people around her. At first, she’s upset about moving and caught up in her own worries, which seems fair enough for a child facing a new school and an unfamiliar life. Then she has to work, overcome her fear, and help people she barely knows. I like how those attachments develop, even when the first encounters aren’t especially welcoming. ...

Intro At the end of the year, I looked through the books I’d read and thought about how much the list said about my life. Like the other things we spend time on, books reflect our interests and concerns. Judging by this list, people and my own habits occupied me more than technology this year. Perhaps I was partly distracting myself from other things. But some of what I read helped me look at those things differently, too. ...

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. ...

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.

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. ...