Morning reading 📷
Geese in the fog 📷
I’ve never worked at FAANG so I don’t know what I’m missing. But I’ve hired (and not hired) engineers from FAANGs and they don’t know what they’re doing either.
For beginners, the most lucrative programming language to learn is SQL. Fuck all other languages. If you know SQL and nothing else, you can make bank. Payroll specialtist? Maybe 50k. Payroll specialist who knows SQL? 90k. Average joe with organizational skills at big corp? $40k. Average joe with organization skills AND sql? Call yourself a PM and earn $150k.
My job is easier because I have semi-technical analysts on my team. Semi-technical because they know programming but not software engineering. This is a blessing because if something doesn’t make sense to them, it means that it was probably badly designed. I love the analysts on the team; they’ve helped me grow so much more than the most brilliant engineers.
A lot of progressive companies, especially startups, talk about bringing your “authentic self”. Well what if your authentic self is all about watching porn? Yeah, it’s healthy to keep a barrier between your work and personal life.
Kite running 📷
Venkatesh Rao has a new subscribers only post out through his Substack, and it’s one of the most interesting things I’ve read in a while. The core idea is the importance of the studio organizational form to the post-COVID reboot, and in particular the “maker studio” where platform technology and other innovation enables a single person to get busy building and creating.
The Instapot is just a slightly fancy pressure cooker with some electronics and automation for safety. Pressure cookers are over a century old, but fell out of favor in the West because they were perceived as dangerous. They continued to be used in the developing world where consumers are both more comfortable with risk, and the upside for quicker cooking of common foods (beans and lentils) is high enough to make it worthwhile. But the small increase in safety and convenience through the integration of electronic smart controls has suddenly made pressure cooking attractive again at developed world levels of consumer risk tolerance. A clear indicator — my American-bred wife has always been too scared to use my low-tech Indian pressure cooker, and used to outsource things like cooking beans to me. But once we bought the Instapot, she was willing to do pressure cooking on her own.
The situation is the same in text media. If you do your accounting right (and this is a big, ongoing debate), a subscriber-based indie publishing activity built around Substack is about a tenth of the cost in time/money/skills acquisition/relationship management as one built around WordPress.
You can go from publication idea to functioning publication in about 20 minutes with no human contact. It’s an Instapot type effect. A small and relatively trivial expansion of the feature set creates a large increase in consumer-grade production capability, primarily via elimination of dependence on 1:1 human relationships.
Coffee lessons ☕📷
Friday night sky 📷
Occasionally I look at my wife’s beautiful iPhone 12 and think I’ll switch back. Here’s what keeps me loyal to my Pixel 5: the fact that I only charge it every other day, and the always on display.
Close encounter 📷
Togetherness first, battle second. In love, war should be peace by other means.
S. G. Belknap in Issue 23 of The Point.
Long weekend sky 📷
From an anonymous book review of Natasha Dow Schüll’s Addiction by Design: Machine Gambling in Las Vegas, hosted on Astral Codex Ten:
Before I read this book, I had an unsubstantiated theory for why people gambled: it’s because every gambler thought he would be the one to beat the odds. In other words, people gambled to earn money. Sure, gamblers knew that most other gamblers lose money, but that just means that gambling is a high-risk high-reward activity. Gamblers were willing to bear the risk in order to have a shot at the reward.
When it comes to machine gamblers, my theory is completely incorrect. People who spend hours and hundreds on machine games are not after big wins, but escape. They go to machines to escape from unpredictable life into the “zone.”
The primary objective that machine gambling addicts have is not to win, but to stay in the zone. The zone is a state that suspends real life, and reduces the world to the screen and the buttons of the machine. Entering the zone is easiest when gamblers can get into a rhythm. Anything that disrupts the rhythm becomes an annoyance. This is true even when the disruption is winning the game.
Lake views 📷
I was fooling around recently with some NHL stats visuals, and decided to update them tonight while watching the Caps-Bruins game (wouldn’t it be great if both teams lost?). Connor McDavid and Auston Matthews have had amazing seasons, and I was interested in putting their career success to date in the context of some of their peers. This was pretty easy, thanks to the folks at Quant Hockey.
The peerset for all of these visuals is Top 10 Active Goal scorers, plus McDavid and Matthews. To start, let’s take a look at cumulative career goals progressing along the x axis from the first season played to the most recent one (players with longer careers will have longer trend lines):
The first obvious takeaway from this chart is just how much of a goal-scoring beast Ovechkin is; also interesting to note the exceptional careers of Marleau and Thornton. But because of the number of trend lines it’s a little hard to pull out how McDavid and Matthews’ careers-to-date scoring compares, so let’s restrict the visual to the first 6 seasons for each player (to match McDavid’s career so far):
It’s easier to tell, here, exactly how special Matthews’ goal scoring is: he’s outpacing everyone other than Stamkos and Ove (Incidentally, this also clearly demonstrates what a phenom Stamkos was and is).
Goal scoring is only part of the story - we can also look at total points. So let’s reproduce the first two visuals, but looking at cumulative points instead:
To me the amazing thing this visual captures is now neck-in-neck the careers of Crosby and Ovechkin have been… and also how much of an offensive powerhouse McDavid is, when assists are factored in. That only gets clearer when we focus on the first 6 seasons of each career:
If you’re interested, you can find the code for these visuals at this link: rentry.co/scoring
Crosswords after lunch 📷
Fiddled with blog themeing this evening, and emoji navigation makes me happier than I expected :)
Reading the paper 📷
By letting people choose their own office adventures, employees can gain back some of what’s sorely missing in American work culture: self-determination. Need to plow through a task that will take you a full day? Stay home. Need to talk through some plans with a few co-workers? Everyone goes in. Kid got the sniffles? Expecting a delivery? Have dinner plans near the office? Do what you need to do to manage your life. Being constantly forced to ask permission to have needs outside your employer’s Q3 goals is humiliating and infantilizing. That was true before the pandemic, but it’s perhaps never been as clear as it is after a year in which many employers expected workers not to miss a beat during a global disaster unlike anything in the past century.
Saturday morning 🍕 📷
It is tricky to encode both absolute and percentage variables in the same visual.
Consider the main chart in this recent Upshot piece concerning Q1 2021 GDP figures. The story is about which sectors are doing better than expected and which are doing worse. They get at this in a fairy nifty way, by comparing actual results for Q1 to hypothetical Q1 results had all sectors grown at a 2% annual rate since Q4 2019. Given this, the main chart focuses on the percentage difference between the real Q1 and the hypothetical Q1. This is a wholly defensible choice.
What the chart doesn’t tell you is any information about the absolute size of each sector. In some cases that information is highly relevant. So in this visual, I thought about ways that you could potentially encode both without changing the basics all that much.
The Upshot story uses the Advance Estimate GDP data released by the Bureau of Economic Analysis. The BEA has an API and an R package that goes along with it but based on a cursory look I don’t think they’ve made the most recent data for Q1 2021 available through the API yet, so I downloaded the excel file (direct download link). I fudged the analysis a little bit: the key comparison in the Upshot relies on growth from Q4 2019, which isn’t in the file above, so I just used Q1 2020 (which is). As a result the numbers in the visual below aren’t 1:1 with the Upshot chart, but they are directionally consistent.
What I landed on was a visual I don’t think I’ve ever built before: a bar chart where the length of the bar encodes the percentage change (like the original) and the width of the bar encodes the absolute size of the sector (specifically, Q1 2021 billions of dollars, seasonally adjusted at annual rates).
In this case there isn’t a ton of variance in the data: Health care is noticeably bigger than other sectors (and Entertainment noticeably smaller) but otherwise there aren’t huge swings from sector to sector.
If you’re interested in the code you can find it all here: rentry.co/beagdp
Going for a walk 📷
I don’t think I have ever learned a lesson in my life. I don’t watch somebody make a mistake and conclude, well, I’ll make sure I don’t do that, then. We pretend that we can learn lessons like this because the alternative is to face the music: to accept that most of what we do in our human lives is driven by some deep, old compulsion we can neither understand nor control, and that when it comes upon us, all we can do is hold on to the wrecked boat and pray. Or laugh, depending on our personalities.
From Savage Gods by Paul Kingsnorth. 📚