In The Machines Are Coming, Zeynep Tufekci talks about the kind of tasks that are being automated by machines:

Today, machines can process regular spoken language and not only recognize human faces, but also read their expressions. They can classify personality types, and have started being able to carry out conversations with appropriate emotional tenor.

Machines are getting better than humans at figuring out who to hire, who’s in a mood to pay a little more for that sweater, and who needs a coupon to nudge them toward a sale. In applications around the world, software is being used to predict whether people are lying, how they feel and whom they’ll vote for.

This is not a new topic. Back in 2012, Kevin Kelly proclaimed in Better Than Human: Why Robots Will — And Must — Take Our Jobs:

It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation.

At the end of last year Claire Cain Miller wrote for the New York Times that As Robots Grow Smarter, American Workers Struggle to Keep Up:

Although fears that technology will displace jobs are at least as old as the Luddites, there are signs that this time may really be different. The technological breakthroughs of recent years — allowing machines to mimic the human mind — are enabling machines to do knowledge jobs and service jobs, in addition to factory and clerical work.

Who knows if this fear is going to turn into reality or not — there are lots of counter-arguments as well (For example, Nicholas Carr has a really interesting historical perspective in Should the Laborer Fear Machines?).

Still, I find the discussion fascinating — especially as it relates to the balance of power in workplaces. Tufekci continues:

Machines aren’t used because they perform some tasks that much better than humans, but because, in many cases, they do a “good enough” job while also being cheaper, more predictable and easier to control than quirky, pesky humans. Technology in the workplace is as much about power and control as it is about productivity and efficiency. […]

This is the way technology is being used in many workplaces: to reduce the power of humans, and employers’ dependency on them, whether by replacing, displacing or surveilling them.

Maybe that’s the real cause for concern here. Not that jobs might go away (although that’s certainly worrisome too), but that power will continue to shift to employers and away from employees.

Back in 2012 I wrote the following about a blind spot I’ve noticed in Agile development:

Problem solving involves not just iteration, but also lots of variation. This often requires time to get it wrong a few times, which doesn’t fit comfortably with the concept of release dates. See, the problem with integrating Agile and UX is not that designers want to hang on to “slow and heavy documents,” “big upfront design”, or whatever you want to call it. The problem is that each iteration further solidifies the chosen path, and there is no time to stop and ask if you’re going in the right direction.

All of that came flooding back when I read Jeff Patton’s Common Agile Practice Isn’t for Startups, in which he puts a slightly different spin on the issue that Agile is not very good at helping us figure out what to build. His solution is a product discovery process (something that’s obviously near and dear to my heart as well). He places the discovery process in the context of a different kind of velocity than is usually measured in Agile—trying to learn as much as possible about customers and the product:

There’s something very different about this process loop: the primary measure of progress during discovery isn’t delivery velocity, it’s learning velocity. And sadly, we can’t measure it in features or stories completed. And, even worse, we can’t plan two weeks of it in detail because what we learn today can and should change what we do tomorrow.

He goes on to describe a Nordstrom process:

Notice the Nordstrom Lab still uses time-boxes, 1 week in this case. But, they didn’t start the time-box by predicting how much they’d deliver, but with learning goals in mind. Then they iterated around the build-measure-learn loop as fast as they could.

The post is hard to quote from, so really, just go ahead and read it. It’s a very interesting approach to making discovery part of a regular Agile process.

Elea Chang takes on the “full-stack employee” idea in a great critique called The Full-Stack Employee and The Glorification of Generalization:

Hidden inside that “full-stack employee” manifesto is the idea that tech equals work and work equals life. Despite all the talk of learning and growing, the full-stack employee is primarily focused on conquering domains within the tech industry. But there have always been ways to impact the world outside the workplace. Unfortunately, the continuous pursuit of professional skillsets tends to diminish the boundaries between work and everything else, leaving you with less and less time to actually grow as a human being.

I’m very much in agreement with this. Many companies still go out of their way to reward people who work extra long hours, even if that comes at the expense of time spent with family (or, as Elea points out, volunteering outside of work).

Two recent articles made me think again about how weird journalism and publishing has become because of the internet and social media. In Instagram’s TMZ Jenna Wortham describes a very successful celebrity gossip “site” (what should we call these things now?) that exists primarily on Instagram:

Angie explained to me that Instagram perfectly suited her vision for The Shade Room: image-centric and interactive. For her purposes, Instagram was the equivalent of WordPress. When she started the feed a year ago, her goal was to accumulate 10,000 followers in the first year. She accomplished that in only two weeks. Angie started by posting about people at the bottom of the celebrity hierarchy (minor reality stars, mostly) and worked her way up to bigger names, building her loyalties slowly. Eventually, readers started sending her tips and videos via Instagram’s direct-messaging feature. Now, The Shade Room has more than half a million followers on Instagram alone.

Of course, this “business” is one decision by Instagram away from total collapse, but for now it’s an amazing success story.

The second article continues the media’s fascination with Buzzfeed. From Adrienne LaFrance and Robinson Meyer long and very interesting The Eternal Return of BuzzFeed:

BuzzFeed is a successful company. And it is not only that: BuzzFeed is the rare example of a news organization that changes the way the news industry works. While it may not turn the largest profits or get the biggest scoops, it is shaping how other organizations sell ads, hire employees, and approach their work. BuzzFeed is the most influential news organization in America today because the Internet is the most influential medium—and, in some crucial ways, BuzzFeed demonstrates an understanding of that medium better than anyone else.

And this:

Culturally, economically, even politically: BuzzFeed is so influential because it is still in ascendance. We don’t yet know how big this publication will get, how sweeping and lasting its effects on the American media sphere will be. “We’re still really small,” Peretti insists. “You have Disney and Viacom and Time Warner—the really big media companies are giant compared to us.” But BuzzFeed’s growth has been relentless in recent years. It shows no signs of slowing. Peretti is deliberately and aggressively building his company to be big. “The Internet isn’t for small companies,” he said last year.

It’s hard not to admire the way Buzzfeed understands how the internet hive mind works. Let’s not forget that they were the first publication to figure out what the internet is really for:

Benedict Evans wrote a characteristically brilliant analysis in What does Google need on mobile? Here’s a taste of his conclusion about Google’s challenge going forward:

The key change in all of this, I think, is that Google has gone from a world of almost perfect clarity—a text search box, a web-link index, a middle-class family’s home—to one of perfect complexity—every possible kind of user, device, access and data type. It’s gone from a firehose to a rain storm. But on the other hand, no-one knows water like Google. No-one else has the same lead in building understanding of how to deal with this. Hence, I think, one should think of every app, service, drive and platform from Google not so much as channels that might conflict but as varying end-points to a unified underlying strategy, which one might characterize as ‘know a lot about how to know a lot’.

Don’t miss this article, the whole thing is great.

Marty Cagan continues his excellent product autonomy series by discussing what happens when teams get large enough to split up their code bases. In Autonomy vs. Ownership he describes his preferred way of dealing with the situation where a team needs a change in a different codebase to get one of their features implemented:

The alternative model is informally known as the “open source” model although to be clear this is not about open sourcing your code, it’s just called that because this is how much of the open source community operates. In this model, if the drivers team needs a change to the riders team’s code, then they could either wait for the riders team to do it, or they can actually make the change themselves, and then request that the riders team review the change, and include it if they’re okay with it (known as a “pull request”). This means that you are telling the software management system that you’ve made a change to the software, but the owner of that software needs to review the changes before they are actually approved and incorporated.

I’m a long-time Pinboard fan, and from the moment I became a paid user I couldn’t shake the feeling that it is one of the most underrated services out there. It’s basically the center of my personal internet. I have years of articles tagged and cached, and available immediately whenever I need to remember something. For me, it represents the best of what technology has to offer as an “external brain”¹.

But it’s even more powerful than that. I recently started wondering if Pinboard could become more central in my blogging workflow as well. My flow when I find an article I want to write about used to be two steps: (1) save to Pinboard, and then (2) start a new text file (with an excerpt from the article) and start writing.

Since I don’t always have time to write immediately after I read something, the disjointedness of these two steps means that I forget to post articles sometimes — or that I can’t remember which part I want to write about. So I needed a way to save Pinboard links for later, in a way that lets me pick up writing whenever I have time.

The solution I came up with isn’t rocket science, but it has made such a big difference to the way I write that I wanted to share it here. The key is a simple IFTTT recipe that takes any new link I save to Pinboard and creates a Markdown-formatted text file that I can use to start writing whenever I want to.

Here is a link to the IFTTT recipe: Post any new Pinboard link to a new text file in Dropbox.

And this is what it does:

I always put a pull quote in the “Description” field when I save a Pinboard link, so the recipe creates a text note with a Markdown-formatted URL, the pull quote, and space for me to add a title, slug, and excerpt once I’m ready to post to the site. Putting the note in a Dropbox folder means I can continue typing and editing on any device — I use Editorial on iOS and nvALT on Mac.

As for posting… I still haven’t found a mobile blogging platform that works for me, so even though I write many posts on my iPhone or iPad, I still post exclusively from MarsEdit. So I also went one step further and made a Keyboard Maestro macro (download here) that transfers the text from nvALT to MarsEdit as soon as I’m ready to post.

You know, the internet is pretty cool sometimes.

Interesting thoughts from Steven Levy in What the Apple Watch Means for The Age of Notifications:

Done right, notifications are a wonderful Feed of Feeds, weeding out the stuff you really need to see from all the usual chaff in the stream.

But it’s hard to do this right when every single app wants to send you notifications. Even given that the system will limit itself to notices worthy of instant notice there are just too many notifications elbowing their way into what should be a narrow passage labeled, “Stuff I absolutely need to see.”

This decreases the value of all notifications.

Gmail has tried, but no one has really figured out the algorithms required to figure out what qualifies as “Stuff I absolutely need to see.” This is the holy grail of notifications at the moment.