Episode Summary
Jeremy Bowers is the Director of Engineering at the Washington Post who’s currently leading a team that’s focused on the 2020 presidential election. Prior to joining the Post, Jeremy held many other news-focused roles, including working at the New York Times as a senior editors for news applications and a senior software engineer; serving as a news applications developer at NPR, and wearing many hats at the Tampa Bay Times, including news technologist and online operations specialist.
Join Corey and Jeremy as they explore the common engineering dilemmas newsrooms face, how data visualizations in newspapers have evolved over the centuries, how the Washington Post uses data to examine trends and test hypotheses—like whether Texas will become a blue state in the next election, what the hardest part about bringing data to the surface in a newsroom is (hint: it’s not data ingestion or cleanup), how a lot of reporting is the same as it was 30 or even 40 years ago, why Jeremy thinks reporters won’t be replaced by robots, how newsroom technology has evolved over the last decade, and more.
Episode Show Notes & Transcript
About Jeremy Bowers
Jeremy Bowers is an Engineering Director for the Newsroom Engineering team at The Washington Post. Previously, Jeremy was the Senior Editor for News Applications on the Interactive News Team of The New York Times, where he led a team focused on writing software for elections, Congress and the Supreme Court. Jeremy was also a news applications developer on the NPR Visuals team and a Senior Newsroom Developer at The Washington Post.
Jeremy Bowers is an Engineering Director for the Newsroom Engineering team at The Washington Post. Previously, Jeremy was the Senior Editor for News Applications on the Interactive News Team of The New York Times, where he led a team focused on writing software for elections, Congress and the Supreme Court. Jeremy was also a news applications developer on the NPR Visuals team and a Senior Newsroom Developer at The Washington Post.
Links Referenced:
- Twitter: @jeremybowers
- LinkedIn: https://www.linkedin.com/in/jeremyjbowers/
- Personal site: jeremybowers.com
- Company site: https://www.washingtonpost.com/
Transcript
Corey: Hello and welcome to Screaming in the Cloud with your host, cloud economist Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.
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Welcome to Screaming in the Cloud. I'm Corey Quinn. I'm joined this week by Jeremy Bowers who's an engineering director for the newsroom engineering team at The Washington Post. Jeremy, welcome to the show.
Jeremy: Thanks, Corey. It's good to be here.
Corey: It's great to have you on the show, but it does feel a little surreal. It's oh, you work at an actual journalistic outlet. Cool, do you want to come on my nonsense podcast and talk about computers for a while? And first took a fair bit of courage to ask that question. The response was, "Absolutely." Which oh great, you folks are human beings, too. You put your pants on two legs at a time just like the rest of us.
Jeremy: You know, you keep making these assumptions about pants and I'm not 100% sure that you've ever put them on ... OH, yes. Right. Of course we put them on two legs at a time.
Corey: Oh, yes. True thought leaders jump into their pants.
Jeremy: Exactly.
Corey: So you're an engineering director, which most people can wrap their heads around what that means if they're listening to a show like this. But for the newsroom engineering team, what is that?
Jeremy: Well, any newspaper of sufficient size, or even media company will run into these problems where they have reporters who are working on beats that are really heavy in data, or they have folks who need tools built for them to help them get to stories that they can't get to. And many organizations kind of deal with this on ad hoc basis. When I used to work at The St. Petersburg times in Florida, now the Tampa Bay Times, don't ask. We would handle this with like a small group of like one or two people who were like self taught programmers. But The Post being a large news organization has an actual legitimate engineering team. This is the kind of thing we deal with. So when we have reporters who are trying to make sense of a voter file, which is like a gigantic spreadsheet, that's 384 million rows long and about 600 columns wide. Or if they're looking at Federal Election Commission data that has campaign finance disclosures.
It's just not something that they can look at with their eyes and make sense of it. They need software written for them, and so that's what the team does.
Corey: That sounds like it's almost the quarter of the size of a typical month's AWS bill.
Jeremy: We won't even talk about the size of our AWS bill.
Corey: One can only imagine. It's interesting because it's sort of a business bubble problem that business analysts and BI folks deal with all the time. But given what you do and how you present it to the world, you take it a step beyond. It's not just about sorting through vast quantities of data and turning that into pretty reports in PowerPoint. But you then have to take it a step further and present that as something that someone who's not in a business environment or has the context to absorb with potentially an MBA equivalent level of understanding, but people in middle schools, back when ... once upon a time when, I'm dating myself now, we would have to take out clippings from various newspapers for current events day and go in and give a small presentation on it.
And I don't know if calc kids do that today, but there was always a, "Have something data oriented" was periodically something they tried to wind up having us talk about. But being able to deliver complex distilled outcomes from data to a middle school reading level has got to be a phenomenal challenge that most business folks don't I guess have the luxury of not having to worry about.
Jeremy: You know, this is one of the things that we struggle with a lot. Early attempts at data visualization from the 1800s were using relatively complex stuff.
Corey: Oh, like back in the 1800s?
Jeremy: Yeah. Legitimate 1800s.
Corey: Oh, early days of Microsoft Excel.
Jeremy: Exactly. Yes, right. So they were writing their VB script. William Playfair was writing Visual Basic to try to generate these complex charts and stuff that, you know, for people who had exceedingly low reading levels for the general population. He was trying to get rather complex things into their heads. I think the thing that we have now is we understand that our readers might be sophisticated if they can put their full brunt of their attention against something. But we understand that we're competing for attention with so many other things. And so the majority of what we're trying to do isn't just to distill it down to make it easier to understand, but to make it quicker to understand so that you can instantly get the thing that you need to know about the story. And typically that's what we're using the data to do.
Corey: Do you have an example of a story or a series of stories that you spent a significant amount of time on? I mean, I have to imagine you're not sitting there doing the deep dive distillation of data for, "And that's who won the Grammy's this morning." Or actually, that's a terrible example, because I'm sure there's data that goes into it.
Jeremy: There's excellent data.
Corey: Yeah. And that's the enormous pile of data that talks about, oh, I don't know, something dumb some political figure said this morning.
Jeremy: You know, I'll tell you, the thing that we've been spending a lot of time with, so my team focuses mostly on elections and it means that we spend a lot of time with our politics staff, our national politics staff. And a thing that we're learning is that this is a lot of reporters, it's their first time really working with large data sets on their beats. A lot of them come from other beats inside the paper and they're working on the campaign for the first time and they suddenly have to start explaining things like how the shape of the American electorate. Or what are these trends that we should know about campaign finance donations?
And these are legitimately difficult questions to answer, even if you're a real nerd and good at this. And it's particularly difficult if you've just come from sports or from features and now you're writing stories about how campaigns are operating. And so the vast majority of the stuff that we end up working on is trying to find ways to integrate the sort of complex stuff that we get from these data sets and into reporters like Daily Work Flow. So a good example of this would be we have reporters who want to go write about the state of Texas. They would like to write a story about what's happening in Texas and is it likely that Texas is going to turn blue or purple in the 2020 election. And so in typical years, like our reporters might go down and write a story about Austin, because there's a lot of democratic voters in Austin. But we do a quick look through the voter file and we can point out that a story about Texas turning blue shouldn't focus on voters in Austin, because there's not a lot of new voters in Austin.
If you want to talk about Texas changing colors in the next election, you want to talk about the Houston suburbs where there's been a huge influx of Latinx and democratic voters that are relatively new to the state and they are not voting republican. So these are trends. It's basically updating the priors that reporters are holding to help them write better anecdotes when they go to write an anecdote. So if we're going to send a reporter to Texas, we won't send them to Austin, we'll send them to Houston.
Corey: So how does that data get intelligently surfaced to the newsroom? I can see having to just run the following set of SQL queries is all well and good, but having had conversations with a fair few reporters over the course of my career, most of them did not spend most of their time dealing with databases. And if they did, they were really sad all the time.
Jeremy: Right? I mean, this is .. Actually, it's probably the hardest part of the job. You would think that maybe the hardest part of the job would be data ingestion and cleanup or the sense making steps that we take to sort of find trends. But if the only thing that we can build for a reporter is like a dashboard that we have to ask them to come look at and like stare at pie charts for a couple hours, they're just not going to include that information in their story and then we're all kind of ... it's a lesser version of the story that surfaces.
So like the first thing that we think about is all right, so when we find something interesting in this data set, how are we going to get this to a reporter so that they can make, so that this can be a first class part of their story. So the majority of the time, we spend a lot of time trying to figure out how to turn it into words rather than into something visual, although we do have a pretty good relationship with our graphics desk, but that's a whole other conversation. But for our reporters, we've focused on these little newsletters that we can send them. We have a lab that's working on the elections. This professor, Nick Diakopoulos from Northwestern University is hanging out with us for the fall. And one of the things that he's working on is lead generation. Not like what you would think from business intelligence, but literally helping reporters write leads to their stories.
So he has this little piece of natural language processing, but like reads in a bunch of data and produces like essentially a tip sheet for a state that shows off things like here's places where a lot of new voters have registered, or here's places where that voter registration is weird or interesting in some way. Here's places where the vote history has changed significantly in the last two years. And these are really great ways to sort of get a reporter a little fact or two that they can use when they go out and do their reporting. And so it's not going to be a story that you look at and go, "Hey, that's a story about data." It just is a standard newspaper story that might feel anecdotal to you, it's just that it's the correct anecdote instead of the wrong one.
Corey: That sounds like an incredibly challenging problem, but it also feels like it's directly aligned with a lot of the modern day snake oil. Sorry, it is more upscale than that. The modern day serpent grease-
Jeremy: Yeah. Appropriate.
Corey: ... that is artificial intelligence/machine learning/math if you're not trying to scam VC money out of people.
Jeremy: Yes, right.
Corey: How, I guess, when I think about a newsroom and its technology, my immediate mental leap is to typewriters and notebooks, and people who buy pencils by the gross. And it's a very old timey type of mental image that's largely informed by comic books and cartoons when I was a kid. I'm going to assume the technology has evolved somewhat if for no other reason that I listen to, for example The Washington Post podcast in the shower most mornings. I don't wind up opening the window and getting smacked in the face by some paperboy hurling a ... sorry, paper child, paper youth as the case may be, hurling a newspaper into my face. So there's obviously been significant technical changes that have hit the journalism profession in the last 30 years. But it's strange I think on some level, the collective consciousness hasn't really caught up with that.
Jeremy: It's funny that you should bring that up about having a street urchin hurl a wadded up piece of newsprint at you in the shower, because it sort of illuminates this problem that we have in journalism, which is that there was a lot of technological thinking and engineering thinking that went into changing how we present information to our readers, but comparatively little that goes into how we change the way our reporters actually report on stories. And so you're joking about typewriters, and honestly a lot of reporting is not that different than it was in the 1980s or 1990s. The advent of the Internet is a thing that we definitely have to have in like that reporters use Lexus Nexus and things like that to do searches on people.
But truthfully, there's a whole lot of change that's happened in say in other fields where like reporting has just sort of lagged behind. So a thing I think that I'm pretty excited about doing is like attempting to take some lessons that we might've learned at other places, minus the serpent grease, of course, and try to bring some of that sense making to our reporters. Now critically, I think that it's basically impossible or a poor task for us to try to replace our reporters with say a fleet of robots who do all of the reporting work, produce a story and then put it out on the Internet. That would be-
Corey: That's called a content mill.
Jeremy: Exactly. I still think that there is, I still think that reporters have these pattern matching skills that are more or less ineffable. And I think it would be remarkably difficult if even impossible to replicate. But there are certain things that they do on a daily basis that would make your head spin if you saw the wasted skills. When I worked at The New York Times, we had this Pulitzer Prize winning legal reporter and every morning he would wake up and he would refresh the supreme court website to see if any new audio transcripts had been published yet. It took him about 10 or 15 minutes to crawl to all the different pages on the supreme court's website to see if anything new had popped up, and then he would brush his teeth and come into work, and then he'd check again. After a couple meetings, he would check again, do a couple phone calls, check again.
And I just found this to be like a breathtaking waste of an incredibly highly powerful mind to spend those extra minutes pressing F5 on SupremeCourt.gov. So we built him a little bot. And all the bot does is tell him when a new transcript has been filed and it drops it off to him in Slack, and then he can click on it and read it.
Corey: RSS. You invented RSS.
Jeremy: Exactly. That's the thing is this is the lowest of low hanging fruit. It's practically touching the ground. There's so many of those cases where a lot of the tooling that we build, it does not feel technologically superior. It feels like some real lightweight stuff but in truth we get a lot of mileage out of just getting the lowest fruit. And in particular, helping reporters out with little things like that means that they will be more trustworthy, our team will be more trustworthy to them when we start working with them on things that are more sophisticated and require honestly more trust on the part of the reporter that the changes that we're making to the reporting process aren't bunk.
Corey: I have to ask given this is the Screaming in the Cloud podcast, how has cloud technology impacted what you personally I guess, and newsrooms collectively do, if at all? I feel like I need to throw the "if at all" in there, but let's not kid ourselves. I don't think there's any industry that hasn't been touched by this.
Jeremy: It's a pretty standard story. I worked at The Post in 2011 and 2012, then took a short break at The New York Times and at NPR in between before coming back in the last April.
Corey: Oh, a good detox. Small publications.
Jeremy: You know, I did get to tell my friends at The New York Times that I was leaving to go back to my hometown newspaper and I don't think any of them found that very funny.
Corey: No, I imagine they would not have.
Jeremy: The post in 2011 and 2012, when we were running election results, we were running them on physical servers that were located inside the building, like just around the corner from my desk. I could actually go to the server room and look at it if I wanted to. We have five servers that we ran millions of page views off of, and we did not have root access to those servers, because a kind soul in New Jersey had decided that we did not deserve to have root access to them. So on the night of the New Hampshire primary in 2012, we were running varnish on our own servers because we didn't have access to the Post CDN at that time. We ran out of file handles, so our Apache instances that were running on those servers slowly throttled themselves to death, because they couldn't open any new network sockets.
And as a result, we were down for about 15 or 20 minutes and not showing results pages to the world. And that was the time when, basically right after that election was over, I slept in the next day. I didn't go into work, but the Thursday after that, I walked in and I said to my boss, "That's it. We're going to the cloud. I am tired of all of this physical server baloney. We can rent servers. We can buy as many of them as we want for like six hours and then we can just turn them all off."
Corey: Less on the election side, but it also seems to me just from a perspective of what actual real newspapers do with protecting sources and whatnot, you're one of the few people who can say that information security does have people's lives on the line.
Jeremy: Yes, absolutely. And there are definitely cases where we step back from doing things in the cloud. We do a lot of document analysis, which requires us to do OCR. And a lot of the good cheap OCR in the world is cloud based, which requires us to upload potentially hundreds of gigabytes of PDFs up to either Amazon or Google to apply their OCR software to it. So one of the things that I worked on at The Times and that I'm working on here at The Post is a large local system for OCRing and transcribing documents, so that if we have things that are really sensitive, we don't have to think twice about where we're putting them.
And sure, we could solve some of those problems with cryptography, but that really feels like and now you have two problems sort of situation, where just having like a beefy server here with like 96 cores that can just rip through a bunch of tesseract and turn a bunch of PDFs into actual texts that our reporters can look at, that feels like a thing that is okay for us to say, "Maybe this one doesn't have to end up in US East One."
Corey: It seems like it's one of those areas where there's a number of companies that, "Oh no, what we do is so secret. It's our secret sauce and our problems are so special and beautiful and unique that no one can handle these as well as we possibly could." And oh, so what did you spend the most time on last year? Oh, replacing failed hard drives. Good, good. That sounds like a differentiated thing that everyone should be focusing on.
Jeremy: A core competency.
Corey: One question that I think is probably going to be on a number of folks' minds is The Washington Post is owned by a patron for lack of a better term, Jeff Bezos, the founder and CEO of Amazon. Is there any business relationship between The Washington Post and Amazon other than one might expect for, "Oh, we buy pencils off of them" or "We use them for cloud services"? Is there editorial oversight? Is there, "Oh, you can use whatever you want as long as it's AWS, because that is owned by the same owner"?
Jeremy: You know, I have to say, early on even before Jeff Bezos bought the company, our need to go to the cloud in some way predated that quite a bit. And actually, the guy who is now our CIO used to be the CTO. His name is Shailesh Prakash. He had come over from Microsoft via Sun Microsystems, so he's a computing OG. And he basically got here to The Post, looked at this litter of hardware that we were running in multiple data centers including one in our own building, and he had said, "This is ridiculous we can't be doing this," to your point about changing out had drives, like isn't really a core competency of ours to pull servers out of racks and make network cables all day long. Probably not, right?
So there was this big push just about the time that I was getting ready to leave The Post in like 2012 to move basically everything to the cloud. And at the time, basically only AWS existed. I think there were some other sort of lightweight solutions that were available. I think there was Linode and like a handful of other things like that, but AWS had basically scaled, like large scaled kinds of things that we could use, like tooling that we could use. So we made the call, we moved to AWS. It was grand. I left to go to The New York Times. The Times of course made a huge move from AWS to Google while I was there, I think in 2017 or thereabouts. And so that was like the, I have to tell you that was one of the most breathtakingly difficult things that I have ever worked on. It's like trying to learn all the new idioms. That's basically the same thing only slightly different, between two huge cloud computing giants.
You know, when I come back to The Post, it was basically like going home a little bit, getting comfortable again with AWS. But you know, to answer the question more specifically, I think that if we ever felt the need to move along, I don't know that I would ever have a problem with that. Our relationship with AWS is basically, "Please fix this problem. We're a really good AWS customer," and not "Please fix this problem. We're going to call Jeff in the middle of the night and have him nuke it from-"
Corey: You can threaten almost anything you want, it turns out.
Jeremy: Only I could, yes.
Corey: You can threaten anything. It doesn't mean you actually have to be able to follow through on it. I mean, I swear. My entire escalation point for most of these things is simply Twitter. Start tweeting something obnoxious and there you go.
Jeremy: Well, we beg our AWS reps when they come to town. We have our punch list of things that we're desperate to get fixed. Web sockets and a handful of other little things like this, but you know, they know when they come here, they're basically just going to deal with us the same way that they deal with any other customer. Except I think we're slightly nicer to them.
Corey: Well, from my perspective, that seems like a relatively low bar.
Jeremy: Yeah, fair. Fair. I don't think that they get like the ... I don't think that that's a job that I could ever do, travel between clients. Basically all you get to do is hear them, hear folks be upset about like what isn't working this week.
Corey: And you have no direct impact on anything that needs to be fixed, because effectively, "Hi, I'm your account manager, I'm here for you to abuse," more or less, and it turns out that my understanding of how large companies work is fatally flawed. I'm a five person company, so I assume every other company is, too. I assume when Jeff Barr, AWS's chief evangelist isn't frantically writing blog posts, he's building the service he's about to write a blog post on. I assume everything he writes about he built himself single-handedly, because why wouldn't he. It turns out companies don't actually work that way. So you're very often having to cajole folks internally to get status updates, to get information about what's going on in a timely manner to customers and in a way that doesn't inspire blind panic.
Yeah, so it turns out that regions down and we're not entirely sure why, and when I called the team all I heard was screaming and then the line got disconnected, so I don't really know what to tell you. Huh, why's our stock down 40%? Yeah, it's one of those areas where messaging is important, message discipline is important. And outcomes are always challenging. On some level, I feel like that is aligned in some ways with the role that journalism has to play. In many cases, the journalist does not get to become part of the story. It's about understanding the other players and telling a story about what's going on. Storytelling is one of those, I think disappearing arts as we sort of descend down the well into page views and click bait and trying to drive outrage more or less instead of actual journalism.
Given the hot button issues that a lot of your reporting tends to focus on, collectively as a group and what you're working on specifically, that extra care has to be taken not just to avoid conflicts of interest or anything in that vein, but rather the appearance of conflicts of interest.
Jeremy: Yeah, absolutely. A thing that I ... I wasn't a journalist in high school or college. And honestly until maybe even a few years ago, it was really hard to even think of myself as like working ... I worked for journalism organizations, but I did not self identify as a journalist. And even now I think like my team, even though they literally sit in the newsroom and we literally work on election results, there still feels in my head like there's a little disconnect. But in truth, we're working on something that is what I would consider to be like the family jewels of The Washington Post. Elections coverage is practically sacrosanct around here.
I have this, I'm lucky, I was blessed with this team of engineers, many of whom have never worked on elections before, but who are just working on side projects that were so clearly demonstrated an interest in this that I had no choice but to go thieve them from the directors that were running their teams. But they have all decided that, like as a team we decided that we were going to follow the newsroom standards for how we conduct ourselves during an election year, which means everyone on the team, we don't vote in primaries. We don't give money to political campaigns. We don't put up yard signs. And we do this not because it's like ... because we think that that'll make us fairer. We do it because it's critical that we understand our place and how people perceive the work that we do. And so it's not fair. It's not fair to say to my engineers, "You can't have an opinion about how our world works."
But it is fair for us to say about ourselves, "You know, we're going to do the same. We're going to treat ourselves the same way that like say a political journalist would.. and we're going to use those same standards of objectivity." I think that it is really great on the part of my engineers to sort of take this on and I think that it's kind of great on the part of The Post to trust us with those parts that are so clearly important to the organization.
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Corey: One of the hardest things that I tend to see on the Internet is whenever The Washington Post or The New York Times or someone else does, has a columnist or an op ed or writes an article in a way that the Internet, by which of course I mean Twitter, it's all the same thing these days, perceives to be a bad take, then suddenly the entire world goes nuts with, "I'm canceling my subscription." Okay. I get the outrage and I get wanting to send a message, but by the same token, there's an awful lot that's good and everyone writes a bad article or has a bad episode once in a while. Not this one, mind you. But at some point, okay, wow. With all these people canceling all the time, where do they ever have any customers? They must be out of business.
Then you do a little digging, wait a minute, this is the third time this year this person claimed they're canceling their subscription. So on some level, it feels like it's a big histrionic and it also feels on some level that you folks can't actually win. Where no matter what you write, you're going to wind up irritating some folks. Part of that is the element of speaking truth to power and part of it also increasingly feels like we're all living in our own bubbles, however we tend to surround ourselves. And I'm as guilty of that as anyone else. I tend to assume every random person I pass on the street has an active Twitter account, that they absolutely care about weird nomenclature from giant web services companies, and they have an unhealthy aversion to people mispronouncing acronyms.
It turns out that a lot of those are not factually true, and every time I encounter someone that shakes me out of that viewpoint, I have to stop and reevaluate. I do wonder how that winds up evolving over time, because it doesn't feel like it used to be quite this, I guess partisan. Then again, I feel like we've been saying that forever too and maybe it's just the world isn't changing, I'm just getting older and my perspective's shifting.
Jeremy: Right? I was just reading Poisoning the Press, which is a book about Nixon and his relationship with the press in the 1970s. Because you assume that everybody that you meet has a Twitter presence and cares about the mispronunciation of acronyms. I assume that everyone that I meet has a pile of unread books that's approximately three and a half years old and that they're barely making their way through them. So I have finally made my way through this book and the thing that struck me about it was it described a time that felt not super unfamiliar to our current one. It felt like adversarial relationship between the president and the press corps. It felt like real similar levels of like discontent. And one of the things I thought that was really intriguing about that was that those times in the mid and late 1970s was also like a really difficult time for newspapers.
There was like an advertising bust around that time that made things really difficult. It was like leading people to sort of question their business models. I recall that The New York Times around that time made the decision to break out a real sports section and focus on their features desk after a series of reader surveys indicated that these are things that people were interested in. And so the thing that I ... I don't think think I disagree with your general feeling about this world. A thing that I am sort of intrigued by is the sort of instant feedback that we get from people, histrionic or not. It's definitely a feeling that somebody has. And especially when it comes to things like elections. I really enjoy getting more or less instant feedback on things that we're trying out in various election nights. We had this Virginia election very recently where the state of Virginia just held elections for its house of delegates and its state senate, which are now controlled by democrats for the first time, the house, the senate, and the governorship are controlled by democrats for the first time.
This was an interesting opportunity for us to get to test out some new elections features and it wasn't the whole Internet that was excited about it. It was a lot of Washington Post subscribers in the Virginia and DC area, buy still it was really nice to get that sort of instant feedback about what people liked and didn't like. We take some of it with a grain of salt, like obviously there's folks who are going to be pretty upset. But on election night, it's not like we're getting people who are like, "We're going to cancel our subscription because your tables were right aligned." The stakes feel like they're a little bit lower for us, honestly.
But it is interesting and it's a difficult ... Elections are one of those places where a newspaper has like a real opportunity to kind of reach out and connect to readers. Everybody wants to know what happened or what's happening and who's winning and what's happening in our democracy. It's one of those places where we just have like a real opportunity to show trust and reward people's trust in us. And honestly, for me it's like the most exciting time to work is like on an election night when the results are flowing in and you're sitting in that room watching all the stuff happen, it's just like it's great. It's like being at the nerve center of a democracy.
Corey: It really is interesting seeing how some of the sausage gets made to some extent. I happened to be in New York City last weekend at the time of this recording and I walked past The New York Times building and holy crap, that thing's big. And then you remember there are bureaus scattered around the world as well and huh, it never occurred to me that that newspaper that shows up or the apps that constantly get refreshed have more than a couple dozen people working there. Turns out it's kind of a massive undertaking for any of this stuff at scale.
Jeremy: That's absolutely the case. You know, and the funny thing is like even here at The Washington Post, we had like an election night for us involved something like 30 people. About 15 or 20 of us huddled in a room, about 10 or 12 working remotely or via Slack. And that's just for an off year election in our backyard, a Virginia election and a Kentucky governor and a Mississippi governor. And then if you can scale that in your head to what super Tuesday or the New Hampshire primaries are going to look like, then you start to get a picture of like what it's like to be working in a place where the entire organization is just like laser focused on a single night and a single experience. It's just crazy. The energy in the building is palpable.
I mean, you wouldn't want to drag your feet on the carpet because you'd probably set off a thunderclap. It's a little crazy. But honestly, to me it's just like one of the reasons why I enjoy working for a newspaper, because it's rare ... Normally if you were going to go work at a newspaper, you would have to work at a place where you were not using a whole bunch of your technological savvy so to speak, a thing that I thoroughly enjoy about working at The Post is that I get to do, like work on hard engineering problems, but I also get to do it at a place where on an election night, basically everybody stops and is watching to see what just happened. Who did we just elect? What's happening? How are we going to explain this to readers? It's a good time.
Corey: It does feel like it's something that has a little bit more permanence than a lot of other things. But I'm not trying to talk smack about various software as a service companies or startups or whatnot. But The Washington Post, The New York Times, these are societal institutions that have been here since before most of us were alive and will presumably be here long after we're gone. We hope. I mean, you folks do have the permanent headline to the top of your website, "Democracy dies in darkness," which is just, you know, it's taking a while and the article isn't done yet, but I'm sure it's in progress.
Jeremy: It's so goth. Basically, The New York Times is "All the truth that's fit to print." And The Washington Post's is "Democracy dies in darkness." It feels like these are the two sides, the yin and yang if you will. I reward my employer for having the guts to go with the slightly grumpier, slightly goth-er tone. We're scrappy. Underdogs. Democracy dies in darkness.
Corey: Exactly.
Jeremy: Get your t-shirt.
Corey: Jeremy, thank you so much for taking the time to speak with me. If people want to hear more about your thoughts on these and other topics, where can they find you?
Jeremy: Well, I am on the tweets, because I am a person that you have bumped into before. So naturally I have a Twitter presence. I am @JeremyBowers on Twitter.
Corey: Excellent. And we'll put links to all of that in the show notes as well. Thanks again for taking the time to speak with me when you could've been doing literally anything else.
Jeremy: That's okay. When I'm done with this, I'm going to go to a meeting about making sure that we have the right campaign finance data available for reporters when the filing deadline hits in January 31st of next year.
Corey: Exciting. And I have no doubt you'll get there on time. Jeremy Bowers, engineering director at The Washington Post. I'm Corey Quinn. This is Screaming in the Cloud. If you've enjoyed this episode, please leave a five star review on iTunes. If you've hated this episode, please leave a five star review in iTunes.
This has been this week's episode of Screaming in the Cloud. You can also find more Corey at ScreamingintheCloud.com or wherever fine snark is sold.
