2023#01 Readings
4 minutes read | 769 words by Ruben BerenguelI know, I have been silent for quite a while.
I’ve had reasons though. Since my last newsletter, I have relocated to Switzerland and have started working for Google as a Site Reliability Engineer (SRE), of the software engineer species (SRE-SWE). I guess this explains why I was focused on SRE topics, algorithms and learning during the last months of 2022: I was either interviewing or ready to jump into a new field.
I will no longer work in data, but since it’s a subject I have an interest on and I already follow many information sources I’ll probably still talk a bit about it. If you are curious to know why I have changed, feel free to drop me an email or contact me in LinkedIn.
Stable Diffusion 2.1, a very convoluted prompt involving Roy Lichtenstein, Google’s brand colors, Swiss mountains and after-editing in Affinity Photo
Data stuff
I can quite relate with the author of Goodbye, Data Science. He literally says The main reason I soured on data science is that the work felt like it didn’t matter, in multiple senses of the words “didn’t matter”. Instead, he went to data engineering, where I think he may be surprised to see how much it doesn’t matter at all.
Career, kind of
When deciding to change or accept jobs there is a metric that is sometimes overlooked: what are we being paid for. I have been keenly aware of it for the past 3 years or so (before was mildly oblivious, but knew there was something there). The explanation in The Rent Versus Buy of Career Growth is pretty clear-cut: your employer pays you for your time… not for your personal brand. And what you may get long-term in terms of market value needs to be factored as well in your income.
I liked one answer in a HackerNews post, Ask HN: What Are You Working on to Become a Better Programmer?. I won’t spoil by copying it verbatim, but it is about process, not tools, projects or open source.
From Gergely Orosz newsletter, Preparing for Promotions Ahead of Time. Very timely because I joined Google when performance conversations were taking place (not for me of course), and several of the recommendations here are widely shared. The gist? Write in advance what you work on, do and help others so in a year you have it written down.
Learning
Yet another post about how good deliberate practice is: The Machines of Mastery. What I found interesting here though is how Top Gun turns out to be a deliberate practice program.
This one is in Spanish, but I’ll translate the part I liked the most, Josep María Margall (I): “Tenía Fama De Tirador Pero Me Gustaba Más Otra Cosa”. Margall was a basketball player in the team of the city I was born in, Club Joventut de Badalona. He was an excellent 3 point shooter, and he became a trainer for Spanish basketball players (particularly those coming from the youth teams in Badalona, like Ricky Rubio or Rudy Fernández). The quote, translated:
But, if you go to a test and know all 20 subjects you go calmer and confident. If you only know 3, you are trying to win the lottery. When you know it, you can have nerves one day and miss, but eventually you’ll make it.
This is something I have personally applied for more than 20 years, taking university tests, preparing for interviews (hello Leetcode). Some things are under your control (what you prepare for), others are not (how sharp you are that day, who interviews you). Getting the most solid foundation in the areas you control is the best way to be confident.
The title of this one, How to Find Your Blind Spots is a bit misleading. It is about what is required to go to the next level in knowledge work (although applies anywhere expertise is required).
