The Twitter growth conundrum

Note from the future: this post written in November 2016. A lot has happened to Twitter (or, Twitter/X) since then. But, the fundamental analysis of Twitter’s growth dynamics outlined in this post continues to hold true even 8+ years later.

Twitter is the public Internet company everyone loves to hate these days. It’s not growing. No one wants to buy it. And people are genuinely confused: what, exactly, is Twitter? Is it a social network? A “micro-blogging” platform? A “live events destination”? A social data company?

Twitter 2011–2015 user growth.

I am one of Twitter’s active users, tweeting on topics such as analytics, Python programming, and the media industry, in which I work. In my day-to-day dealings with journalists, editors, social media managers, audience development folks, and others in the media industry, it’s clear Twitter has a special position among the professional class of media raconteurs.
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The value of money in a technology career

Michael O. Church wrote an essay awhile back called “Why programmers can’t make any money.” The post is no longer on his website — for some strange reason — but you can have a look at the archived version here.

If you don’t wish to read his post, this quote will give you the summary.

When the market favors it, junior engineers can be well-paid. But the artificial scarcities of closed allocation and employer hypocrisy force us into unreasonable specialization and division, making it difficult for senior engineers to advance. Engineers who add 10 times as much business value as their juniors are lucky to earn 25 percent more; they, as The Business argues, should consider themselves fortunate…!

I empathize with his thoughts, but I have struggled — for years, now — to understand the author’s conclusion.

If we want to fix this, we need to step up and manage our own affairs. We need to call “bullshit” on the hypocrisy of The Business, which demands specialization in hiring but refuses to respect it internally. We need to inflict […] artificial scarcity.

I decided to (finally) publish this response today because I have seen artificial scarcity play out in another industry; my wife is a medical doctor in the US. Are we to believe that programmers should establish artificial scarcity in the same way that doctors have — with political organizations like the American Medical Association and credentialing via something equivalent to medical school and board certification?

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The 3 best headphone options for programmers

Apple just announced that the headphone jack is going the way of the dodo, but as programmers, we know better. The headphone jack is our reprieve from cantankerous office banter, our salvation from your office mate’s obsession with cat videos, and our gateway to productive coding flow.

For those of us who still believe in the simplicity and beauty of the good old auxilliary audio input, here are three headphone options that I’ve field tested extensively and can vouch for quality and convenience.

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Simple and Universal: A History of Plain Text, and Why It Matters

My first computer did not have a web browser. But I remember using my first web browser (Mosaic) with some awe. It was the late 90’s and though the web was not nearly as beautiful or functional as it is now, it worked. It was clear — even to my young self — that there was something very special about it. The web had separated text from paper.

Mosaic wasn’t much to look at, but its potential was inspiring nonetheless.

You see, before the web, we were already exploring alternative forms of text, in the context of the “desktop publishing” revolution, whose killer app — especially in media circles — was QuarkXPress. However, even desktop publishing still had a conceptual model of printed pages of text. The “output” of desktop publishing tools was almost always a paper printer. The “publishing” part of “desktop publishing” was “printing”. QuarkXpress and Adobe InDesign fought an epic battle to determine who could generate the best-looking paper, most efficiently.
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An async kind of pair programming

Can pair programming be done in a way that is compatible with async communication?

async_jam

Pair programming is described by the original c2 wiki as a process in which “two engineers participate in one development effort at one workstation”. It would seem the process is inherently synchronous, at least as originally described and practiced.

I experimented with pair programming at my first industrial programming job at Morgan Stanley. It was 2006-2008 and two fads were happening in parallel: “agile” software management techniques and “extreme programming”, with a particular emphasis on test-driven development with Java.

I occasionally found pair programming to be effective, but noticed my results varied wildly depending on the engineer I paired with and the problem we worked on. Some people really enjoyed the “brain swarming” of having two heads attack a problem. Other people found it cumbersome and interruptive. Some problems seemed so indivisible that it always ended up that one person drove, and the other person merely watched. In the end, I couldn’t really say whether I benefited from it, despite many hours of experimentation.

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Simple Lego Blocks for Big Data

Data engineers should abstract their code in the most lightweight way possible to facilitate downstream integration in a large-scale data system.

You want lego blocks, not puzzle pieces.

lego_blocks

The creators of the C programming language once famously said, “first make it work, then make it right, and, finally, make it fast.” This adage still applies today.

The difference is, we have tools to take working code and validate that it is right against reams of data. Many of these tools can also be used to make the working, right code run really fast across a cluster of machines, possibly even in real-time, as the data comes in.

But, making code work, then right, then fast, requires some discipline.

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Idiomatic Python Resources

Let’s say you’ve just joined my team and want to become an idiomatic Python programmer. Where do you begin?

Well, you can move up the learning curve quickly using resources from this blog:

I also have some good resources on web development with Python:

And on more advanced Python concepts, like dunders and functional programming:

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Programming: it’s weird

I read the Bloomberg piece, What Is Code?, an explanation of code artistry and programmer/hacker culture in 2015. I love this paragraph about “languages as liquid infrastructure”:

The point is that things are fluid in the world of programming, fluid in a way that other industries don’t seem to be. Languages are liquid infrastructure. You download a few programs and, whoa, suddenly you have a working Clojure environment. Which is actually the Java Runtime Environment. You grab an old PC that’s outlived its usefulness, put Linux on it, and suddenly you have a powerful Web server. Now you can participate in whole new cultures. There are meetups, gatherings, conferences, blogs, and people chatting on Twitter. And you are welcomed. They are glad for the new blood.

Java was supposed to supplant C and run on smart jewelry. Now it runs application servers, hosts Lisplike languages, and is the core language of the Android operating system. It runs on billions of things. It won. C and C++, which it was designed to supplant, also won. A lot of things keep winning because computers keep getting more plentiful. It’s weird.


Worse is better, is worse, is better, is worse, is better…

The 3 Best Python Books for Your Team

Python was the core programming language used at Parse.ly. It is a widely-used language with huge adoption among open source projects. It’s no wonder it’s one of the leading languages for software teams.

I’ve written a couple of blog posts with original material for learning Python, including “import this: learning the Zen of Python with code and slides” and “Build a web app fast”.

Newcomers to Python are often overwhelmed by the wealth of information, available online and in print, for the language. I am often asked by others, “What are the best books for my Python team?” I plan to answer that question with this post, by highlighting what I consider to be the three best Python books on the market today.

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Picking tech stacks

I realize now that one of the hardest parts of running a successful startup is “betting” on tech stacks that, 3 years out, will have a groundswell of community support around them.

It’s still shocking to me that when I chose each of the following technologies as a central part of Parse.ly, they were so new/immature as to not even show up on a Google search trends box, but are now very popular technologies.

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