Web agency Postlight wrote about their engineering culture, and what a breath of fresh air it is to read this list in the midst of the current “hustle til you drop” culture:

The positivity of our internal communication fosters a team that is warm and easy to engage with for our clients. Our clients are shocked by how easy it is to communicate with our engineers. No knee-jerk reactions. Just a friendly, two-way, conversation with professionals.

Or, as we phrase it in our Wildbit values, “As a team, we support each other to do the work of our lives.”

Liz Pelly’s Streambait Pop is a fascinating look at the “Spotify sound” and other changes in pop music brought about by streaming:

The Spotify sound has a few different variations, but essentially it’s a formula. “It has this soft, emo-y, cutesy thing to it,” Matt says. “These days it’s often really minimal and based around just a few simple elements in verses. Often a snap in the verses. And then the choruses sometimes employ vocal samples. It’s usually kind of emo in lyrical nature.” Then there’s also a more electronic, DJ-oriented variation, which is “based around a drop … It’s usually a chilled-out verse with a kind of coo-y vocal. And then it builds up and there’s a drop built around a melody that’s played with a vocal sample.”

The really interesting part to me is how it’s a sound that’s essentially designed to make you forget about it, so that you just keep streaming endlessly:

The chill-hits Spotify sound is a product of playlist logic requiring that one song flows seamlessly into the next, a formula that guarantees a greater number of passive streams. It’s music without much risk—it won’t make you change your mind. At times, these whispery, smaller sounds even recall aspects of ASMR, with its performed intimacy and soothing voices. When everyone wants your attention, it makes sense to find reprieve in stuff that requires very little of it, or that might massage your brain a bit.

After I read this article I went through my Spotify playlists and counted how many of them had the word “chill” in it. Let’s just say I’m too embarrassed to tell you…

But moving on, I think this “inoffensiveness” in music is one of the reasons I’ve started to listen to so many more genres over the past few years. I now like music that feels like it just doesn’t quite sit right. Any artist or band that combines a little discomfort with a lot of skill has my attention. Just one recent example that comes to mind is Double Negative by Low. I still don’t really know what it is. But I know it’s something really special.

A lot of words are being written about AI and machine learning these days, so it’s sometimes hard to know what to pay attention to. M.C. Elish and danah boyd’s Don’t Believe Every AI You See is one of those essays that I would consider essential reading on the topic. On the ethics of artificial intelligence:

When we consider the ethical dimensions of AI deployments, in nearly every instance the imagined capacity of a technology does not match up with current reality. As a result, public conversations about ethics and AI often focus on hypothetical extremes, like whether or not an AI system might kill someone, rather than current ethical dilemmas that need to be faced here and now. The real questions of AI ethics sit in the mundane rather than the spectacular. They emerge at the intersections between a technology and the social context of everyday life, including how small decisions in the design and implementation of AI can create ripple effects with unintended consequences.

And on the supposed “neutrality” of machines:

[There is] a prevailing rhetoric around AI and machine learning, which presents artificial intelligence as the apex of efficiency, insight, and disinterested analysis. And yet, AI is not, and will not be, perfect. To think of it as such obscures the fact that AI technologies are the products of particular decisions made by people within complex organizations. AI technologies are never neutral and always encode specific social values.

As Kevin Kelly also pointed out years ago in his book What Technology Wants, technology is never neutral. It possesses the collective values of its creators. And that’s where things so often go wrong. A great resource on this topic is Sara Wachter-Boettcher’s book Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech.

The Doist team makes some great points in their article Best Practices for Building and Managing a Remote Team, including something we’re embracing very strongly at Wildbit as well:

Managing a remote team effectively is not about monitoring the amount of time your team members spend online (in fact, that’s a great way to kill employee autonomy and motivation). It’s about building and supporting a team that doesn’t need to be micromanaged in the first place.

We wrote more about our approach to remote work here.

You might think that A rocker’s guide to management sounds like one of those “What X can teach us about Y” articles. And it is, I guess, on the surface. But it’s just so well done. As an intro:

The history of rock groups can be viewed as a vast experimental laboratory for studying the core problems of any business: how to make a group of talented people add up to more than the sum of its parts. And, once you’ve done that, how to keep the band together.

The article is less about management, and more about the different ways that teams can be structured, as well as the good things and pitfalls of each approach.

Mike Monteiro isn’t always everyone’s cup of tea, but I really like his views in Design Ethics & the Truth About Who Designers Really Work For. In short, designers need to work for users:

When you hire me as a designer, I do not work for you. I may practice my craft at your service, but you haven’t earned the right to shape how I practice that craft. One, you don’t want me designing at your level, you want me designing at mine, which means you don’t get to pull the strings. I do. Two, you’re hiring someone who performs a service, not a servant. There’s a difference. I’m not there to do your bidding, I’m there to solve a problem or reach a goal that we agreed upon.

More importantly, designers work for all users, not just the ones who look like them…

And your job, the glorious job you signed up for when you said you wanted to be a designer, is to support all of these people. Make sure none of these incredible voices get lost. And to fight against those who see that brilliant cacophony as a bug and not the greatest feature of all time.

You are our protection against monsters.

Karn Bianco asks What if video games could help fight climate change? No, really:

One of the first in a new line of climate change-focused games was survival game Eco. Developed by Seattle-based Strange Loop Games, this massively ambitious civilization simulator includes everything from detailed ecosystems and realistic climate modeling to player-driven government and economies. Players work together in a shared online world where every action impacts the environment around them. Laws to restrict or encourage almost any kind of behavior can be suggested and enacted to test their effects on the world.

This is a really neat development. I also like the ideas about how this could translate to long-running simulation games like SimCity:

Imagine a SimCity that puts people (not cars) and sustainability (not endless growth) first. Where designing a city that’s easy and safe to navigate on foot or by bike is not just possible but rewarded, with lower pollution and healthier citizens. Where constructing energy-efficient buildings provide long-term in-game benefits like lower energy demands, as well as a chance to learn about real world trends like passive housing.

Jonathan English’s article on mass transit in America starts off sad, and just gets worse from there:

One hundred years ago, the United States had a public transportation system that was the envy of the world. Today, outside a few major urban centers, it is barely on life support. […]

At the turn of the 20th century, when transit companies’ only competition were the legs of a person or a horse, they worked reasonably well, even if they faced challenges. Once cars arrived, nearly every U.S. transit agency slashed service to cut costs, instead of improving service to stay competitive. This drove even more riders away, producing a vicious cycle that led to the point where today, few Americans with a viable alternative ride buses or trains.

It’s important to note that this is not an accident. It is a direct result of urban design:

The story of American transit didn’t have to turn out this way. Look again at Toronto. It’s much like American cities, with sprawling suburbs and a newer postwar subway system. But instead of relying on park-and-ride, Toronto chose to also provide frequent bus service to all of its new suburbs. (It also is nearly alone in North America in maintaining a well-used legacy streetcar network.) Even Toronto’s suburbanites are heavy transit users, thanks to the good service they enjoy.

Likewise, in Europe, even as urban areas expanded dramatically with the construction of suburbs and new towns, planners designed these communities in ways that made transit use still feasible, building many of them around train stations. When cities like Paris, London, and Berlin eliminated their streetcar networks, they replaced them with comparable bus service.

Some further reading that digs into pedestrians and cars a little more:

• The Hell of Being Carless in America’s Suburban Sprawl
• There Are Better Ways to Get Around Town
• American cities are designed for cars—which makes life worse for everyone

Nicholas Carr discusses the importance of using the right terminology when we talk about how companies use our data in his essay I am a data factory (and so are you). On the problems with the “data mining” metaphor:

Data does not lie passively within me, like a seam of ore, waiting to be extracted. Rather, I actively produce data through the actions I take over the course of a day. When I drive or walk from one place to another, I produce locational data. When I buy something, I produce purchase data. When I text with someone, I produce affiliation data. When I read or watch something online, I produce preference data. When I upload a photo, I produce not only behavioral data but data that is itself a product. I am, in other words, much more like a data factory than a data mine. I produce data through my labor — the labor of my mind, the labor of my body.

On extending the “data factory” metaphor to the platform companies:

The platform companies, in turn, act more like factory owners and managers than like the owners of oil wells or copper mines. Beyond control of my data, the companies seek control of my actions, which to them are production processes, in order to optimize the efficiency, quality, and value of my data output (and, on the demand side of the platform, my data consumption). They want to script and regulate the work of my factory — i.e., my life — as Frederick Winslow Taylor sought to script and regulate the labor of factory workers at the turn of the last century. The control wielded by these companies, in other words, is not just that of ownership but also that of command. And they exercise this command through the design of their software, which increasingly forms the medium of everything we all do during our waking hours.

The factory metaphor makes clear what the mining metaphor obscures: We work for the Facebooks and Googles of the world, and the work we do is increasingly indistinguishable from the lives we lead. The questions we need to grapple with are political and economic, to be sure. But they are also personal, ethical, and philosophical.

This brings up a point I haven’t given much thought to. It’s not just that platforms use the data we create to further their business interests. It’s that they are also invested in having us create a very specific kind of data. Data that can be as useful as possible to advertisers. That changes our behavior and gives rise to the prevailing wisdom that people are not being authentic on social media.