- [MUSIC]
- >> Hello, and welcome to the Data Journalism Podcast.
- I'm Scott Klein.
- >> I'm Alberto Cairo.
- >> And I'm Simon Rogers.
- >> This week, we're joined by a returning guest, Ben Welsh from Reuters.
- Ben is a long time proponent of open source and
- published lots of great open source projects.
- And so he and I paired up a couple of months ago to take a look at
- open source activity and news and came out with a piece on the source blog from Open News.
- Since then, I have been tracking open source activity in news and
- I've actually launched a newsletter at openjournalism.news.
- And frankly, I've been watching open source activity in news a lot since then.
- And there's a lot to be happy about, a lot of encouraging activity.
- A lot of really good repos coming out, a lot of good open source activity.
- And I've gotten a lot of great feedback from the community on the work.
- So this interview, we talk kind of much more generally about open source activity in news.
- >> Amazing, and the thing about Ben is that I don't think modern data journalism
- would look the same without him.
- He does such incredible work around opening up data and making it more available.
- And it was a real pleasure to spend time with him.
- He is actually a kind of regular fixture at Nycar where we recorded this interview.
- And he often does this thing like the full Nycar where he basically presents every
- day constantly.
- So he's a bit of powerhouse.
- We were kind of lucky to have some time with him.
- >> Ben is also a returning guest.
- >> Yeah. >> He visited the, yeah,
- the podcast more than four years ago.
- So it was great to hear what he's been up to since then.
- He has a new job.
- And as always, I found his observations about where the industry is going,
- where we are, and where we are heading.
- I think that they are spot on.
- >> Okay, let's get into it.
- [MUSIC]
- All right, we are live from Nycar in Indianapolis.
- First time ever, and we're so glad to be here with Ben Welch.
- You've got a new job since we last spoke.
- Tell us what you've been up to at Reuters.
- >> Thank you for having me.
- It's a pleasure to be back here at Nycar and talking to you guys again.
- I now live in New York City, money making Manhattan,
- where I walk to work every day at Three Times Square and
- work at the Reuters Newsroom.
- Reuters is an international news agency with more than 2,700 journalists
- all around the planet, and one node in that big network is right there in New York,
- where we cover markets, economics, business, and really anything else.
- And I lead a small team who are working on automating data for breaking news.
- >> Tell us a little bit more about that.
- What does that actually entail day to day for you?
- >> Sure, if you think about what's going on on Wall Street,
- as the charts move up or down, there's an opportunity to cover that with data.
- And a traditional approach would have been to assign somebody to go and
- download a spreadsheet and reformat it and stick it into a charting tool.
- And then get an editor to look at it and push it through.
- And that worked great, it just wasn't necessarily the quickest thing and
- can only cover so many topics at one time, your throughput was limited.
- And what we really see is an opportunity for automation to really attack that type
- of breaking news coverage of market moving information in a way that gives us
- greater speed and scale to talk like the corporate denizen that I am.
- And so if you think about it, that data that's coming out of the Korean stock
- market, the oil prices, the whatever else, the futures market is updating minute by
- minute and what our team has done is set up hundreds of automated pipelines that
- are drawing from all these different data feeds identifying when new records are
- available and automatically publishing charts that are ready to go within seconds.
- And so when the oil prices ran wild on Sunday night after the attack on Iran,
- we had the live chart up on the homepage within minutes before the tech story
- about the increase was even ready to show that and
- then it can develop live as the new numbers come in.
- We're trying to take that approach to as many different topics as we can,
- not just commodities and markets, but also public opinion polls.
- We routinely publish one of the broadest American public opinion polls.
- And by automating the charts in coverage,
- we're able to just get it out quicker and faster.
- So on that Sunday, we had the first public opinion poll of what Americans thought
- of the attack on Iran and again, the chart was ready before the tech story
- because we had it all wired up.
- >> So there are two really exciting things about it, at least.
- One is that you didn't create your own graphics stack,
- you didn't kind of invent the entire thing.
- But you're in fact wrapping it around data wrapper,
- which is something that really lots and lots of newsrooms are now using.
- I know the New York Times has switched back to if they're still using that code.
- And it's become one of maybe two with Flourish data wrappers become one of
- the two sort of main graphics engines that newsrooms are using.
- Which leads me to the second thing that's exciting is that you actually also
- publish these pipelines as open source code, right?
- So newsrooms could adopt this and be able to, I mean,
- not that many newsrooms need to be as quick as Reuters.
- But newsrooms could adopt these to be useful for
- their communities in different ways, right?
- So one thing we released last year that has been super useful to us, and
- I suspect will be to others, is a Python library that kind of acts as a wrapper or
- a helper for automatically making charts with data wrapper.
- So most of us encounter data wrapper in our browser as a point and
- click interface where with relative ease you can make a really great chart, right?
- But it also has an API that allows a computer or
- a machine to make a chart by posting into that system the instructions for
- how to make it.
- But to do that requires a good amount of technical know how and
- you kind of have to decode the kind of dozens of little options that configure,
- make the bar this color, set the axis this format.
- And most of that's undocumented.
- So what we've done is written a Python library that fully maps out all of those
- options for eight or ten of the data wrapper chart types and
- fully documents how they all work.
- And so now with a very small amount of Python code, you can really easily
- configure and automate a line chart, a bar chart, a column chart.
- And that's now open source and available for anyone.
- And we also took it one step further, not to get too nerdy here,
- but I feel like this might be the venue.
- >> This is the venue for this.
- >> Yeah, we connected that to what's now called a model context protocol server,
- which is kind of an emerging standard for making a kind of pluggable system for
- connecting outside data sources and tools into an AI chat bot like OpenAI or Claude.
- And because of the way the Python code is written,
- it's actually kind of perfectly suited to kind of connect with the chat bot.
- I don't know if folks have done this, but there's an emerging standard as well for
- sort of teaching the bot how to deal with different data structures.
- And the system that we use to help ourselves make the chart is the same
- system that the AI use, so we've released an MCP server.
- It's still experimental, but it can be plugged into any chat bot.
- And you can then say, hey, chat bot, here's a spreadsheet.
- Could you make me a bar chart?
- And boom, it'll talk to DataRapper and bring it right back.
- >> That's so helpful.
- I mean, the charts that AI tends to make have not been great.
- Certainly nothing to the level of what DataRapper is capable of.
- So that's exciting, I'm gonna try that.
- >> Yeah, I just talked to the Politico crew here at the conference, and
- they're installing it in their local newsroom too, which is pretty exciting.
- >> This is gonna lead into what we're really here to talk about.
- But why do you make Open?
- Why make it available to everybody?
- Why isn't it like secret, commercial, confidential information?
- >> Well, I think we have a lot to benefit from making it open.
- Within days of me releasing it, I received a contribution from a developer
- in a newsroom in Germany who I've never met, who contributed support for
- a DataRapper map and automating how to configure that that we hadn't done yet.
- And so by inviting other people into the project,
- we got something back out of it, right?
- And so to me, there's definitely the reciprocal part of it.
- For me, I think open source really demands a higher standard of coding and
- quality, just even knowing it's gonna be public.
- Sometimes it pressures you to make it a little better.
- And I think that that's what an editor I used to have liked to call good pressure.
- And so I think that it puts a little good pressure on it there.
- And I just think it's also the right thing to do, not to sound too corny.
- We're not releasing the stuff that really makes our system break news and
- beat everybody else.
- We're releasing one component of it that I think is kinda shared across the field.
- And then I think I don't really see much of a downside for us in doing it.
- >> So what Ben is talking about and Simon is talking about is a piece that Ben and
- I published earlier this year that came out of some of the research that I've been
- doing for years and Ben joined and was doing some physical analysis over the last
- year that was really looking at the prevalence of open source activity
- in newsrooms in America, and really kind of chasing a hunch that we had.
- A sort of feeling we had that the activity has really tailed off in recent years.
- And it actually turned out that I had a data set that was ready to go on this
- because I had, back when X was called Twitter, I had a data set of all of
- the newsrooms open source repository, their open source accounts on GitHub.
- And I was chatting with Ben one day and we decided to take a look at the,
- use this data set to take a look at the activity to see if it really had
- tailed off.
- Ben, do you wanna talk just for a second about kind of what we found and
- what the piece is about?
- >> Yeah, so we took Scott's list of I think about 350 news organizations on
- GitHub and wrote a little Python code to go download all the repositories that
- have been made by those organizations ever.
- Just to kind of build a data set of all open source activity,
- at least on GitHub, by this kind of very large broad set of news orgs.
- And then when we started analyzing and looking at it, we found that not only
- was Scott's hunch that activity was going down right.
- It was, the trend was much more dramatic than we had expected.
- If you just take a look at something like the number of new open source
- repositories, that number peaked at 2,000 new repositories or
- more than that in the year 2016, and it has just been in a consistent and
- really quite dramatic decline since then.
- Each year stepping down to fewer and fewer with a kind of a low point reached
- in 2024, I think of around only 400.
- So we've seen just in less than a decade, an 80% drop in the number of open source
- repositories published by news organizations.
- Which is a, as a long time data journalist, I said, wow,
- that sounds like a nut graft to me.
- >> Yeah, yeah, and it wasn't just open source repositories.
- We wanted to, that's an easy thing to count.
- And so it was a good proxy in that sense, if not an imperfect proxy like all
- proxies are.
- But we also looked at posts to the nikar-l mailing list, so we're at nikar.
- So I think it's just natural for us to talk about the nikar-l list.
- And there was a time when the nikar-l listserv was the must read email list for
- data journalists.
- I remember when I let it buzz my phone when a new nikar-l post would come.
- I would get a notification on my phone cuz I wanted to read it as soon as it came
- out, and we found that there was a similar decline on the nikar-l mailing list.
- I think that was down 89% from its peak at around the same time.
- So really a culture of sharing that really peaked in 2016 has tailed off
- considerably, not just in the stuff that's easy to see, but
- I think in the stuff that's kind of harder to measure as well.
- >> Definitely, I think one of the things that always attracted me to data
- journalists originally was this culture of sharing.
- Because often data journalists, you're kind of isolated where you are.
- This idea you're part of a bigger community, but actually also that it can
- make your work better because people are gonna come back to you and
- improve what you do, just like the example you gave.
- So obviously, the peak is 2016.
- That is a different world now in terms of data journalism.
- Do you wanna talk about how different that was?
- How data journalism's changed since then?
- >> Well, how different was in terms of just number of outlets?
- We have BuzzFeed doing a ton of data journalism.
- 538 were incredibly prolific contributors to GitHub.
- >> Without a doubt, the first thing that came to our mind,
- we're asking the question, what could explain this?
- Why has this gone down so much?
- And I think if you compare today to then,
- the thing everybody who's paying attention thinks of first is economics, right?
- We just have all these places that were large contributors.
- You just listed them.
- And if they go out of business, they can't keep contributing, right?
- It's kind of a pretty basic explanation.
- And I think that's just true, you know what I mean?
- You definitely see in our data set that there's a strong cohort of places.
- They're just gone, and that accounts for some, but not all of the decline.
- And that's where it started to get interesting for
- us in kind of reporting out the data set.
- Cuz what we saw is that there were other cohorts or
- other groups of news organizations within the data who are also declining,
- who didn't have those economic problems.
- You know, I'm not here to name names or call anybody out, but
- we can all think of the very successful news organizations of the Internet era.
- And if you look for them in our data set, you're gonna see they're dropping too.
- So it can't all be the money, right?
- So something else must explain it.
- And that's what set Scott and I off on a little bit of a reporting expedition.
- You wanna share what we did, Scott?
- >> Yeah, so as journalists, as data journalists, we didn't just stop at
- looking at the data and publishing what the data findings were.
- We wanted to talk to people to see if they had a hunch about what changed and
- why the numbers had gone down.
- So we talked to more than a dozen people.
- These were practitioners, people who we felt were big advocates for
- open source or just big open source releasers back in the day.
- And talk to them about, first of all, did they agree with the hunch?
- We let them know what the data showed.
- And asked them to help us understand what changed in their newsrooms.
- What were their motivations for open sourcing in the first place?
- And did those motivations go away?
- What changed about their workplaces that might have led to a decline,
- a sort of zeal to open source everything.
- >> And in addition to economics, we really heard two other major themes kind of come
- up in different ways from different perspectives as we went through it.
- One was the idea of technological maturity, you might say, or
- like the we fixed it theory.
- And several people we interviewed said one reason you might see less open source
- is we just don't need it as much.
- Some of the problems have been solved.
- You look at the explosion of open source in that period is associated with
- the development a lot of web frameworks for publishing data on the web.
- Be it Django or Sveltekit or what you name it, D3.
- And now that those problems are quote unquote solved,
- maybe there's less need, right?
- So that was one theory we heard from folks.
- And I've heard that concurred by others.
- And then a third theory we heard, and
- this especially came from the people in the large successful newsrooms, I would say,
- is the idea of kind of a cultural change that's happened in the winners of
- the internet era.
- It's articulated by one person we interviewed as saying when your team has
- moved to the center of the newsroom, it's no longer the boutique
- kind of experimental R and D nerds in the corner.
- But they're now part of the capital P product and the quote unquote main thing,
- right?
- Your bosses are different people with different outlooks, right?
- Your incentives are different.
- You're focused on meeting the OKRs and
- the quarterly goals, not kicking up something cool to see if the Internet is
- a real thing or not, right?
- And that that cultural change accounts for why, in their theory,
- why some of these large newsrooms have dropped.
- >> But while that's happened,
- there have also been like really interesting startups,
- things like The Pudding or City Bureau, Bellingcat,
- which are kind of pioneering new types of reporting.
- Do you think that's making a difference, Scott?
- >> Yeah, I mean, there definitely are newsrooms, especially places that are
- startups like The Pudding, Bellingcat, there are a few others that
- are really punching above their weight when it comes to sharing.
- So The Pudding puts a lot of things open source.
- There's a terrific newsroom in Harrisburg, Pennsylvania called Spotlight PA.
- I think they put their entire everything.
- I think they put things up on their GitHub before they publish it.
- I mean, they are really, really doing kind of keeping the old ways, in a way,
- doing really terrific work.
- >> Yeah, I think that's right, cuz our findings are really just in the aggregate,
- right, we're talking about news at all.
- But there are some real good news stories inside the data set when you dig down.
- And I just think we really can't say, in my opinion,
- enough good stuff about the success of the open source intelligence movement,
- as it's known, who I think are one, modeling excellence in journalism and
- hard work and creativity and inclusiveness, but also sharing.
- And I think that in data, my little niche, my little corner of that big curve,
- we would do well to pay attention to how they've been succeeding and
- try to emulate them.
- >> Yeah, I mean, data journalism was hardly new in the early 2010s, but
- it sort of felt like we were bringing something new to newsrooms.
- It was a lot of people coming from tech or from outside news were coming in,
- bringing some of the sort of open source fervor.
- These are people who worked at places that open sourced without even,
- you didn't need a recent open source, of course you open sourced everything.
- So I think there was a lot of energy coming in, it was a fairly new,
- at least the way we were practicing it, and thinking about it was fairly new and
- growing.
- And I think on some level, that's what OSINT is now,
- what open source intelligence is now.
- And I think, in a way, Ben talked about maturity.
- It's almost, it's a good thing that data journalists would have to come to
- a conference like NYCART to see people who really understood what they did.
- And to sort of gather meaning and standards and sort of edit each other in
- a way that they didn't necessarily get from their newsrooms.
- Where we all sort of worked in places where we had bosses that were very bright and
- very good journalists, but not necessarily people could help us with our Python
- problems or decide between pandas or R or whatever, right?
- And so we kind of turned to each other for that, and in a way,
- that's what created the culture of sharing.
- And now a lot of our newsrooms have become much more sophisticated,
- much more data literate.
- And we are turning inward, and we talk all about the inward shift in the story.
- Where there are actually people in our newsrooms that we can turn to,
- to get the answers to our Python questions and to edit our math,
- as Amanda Cox used to put it.
- And in a way, we kind of didn't, we didn't need each other quite as urgently.
- And so the culture of sharing became more of a conscious effort than the default.
- >> I love that you also included a piece about Django, because Django is so
- important and something that a lot of people perhaps don't know about now.
- But without it, the web would not look how it looks.
- Do you want to explain what Django is for people?
- >> Yeah, I mean, we've all used Instagram, unfortunately, right?
- And believe it or not, the first edition of Instagram when it was launched as
- a startup was built using an open source web development toolkit called Django.
- And that toolkit, which launched this billion dollar business that now
- dominates the attention of many Americans,
- was invented at a small local newspaper in Lawrence, Kansas.
- Where at the Lawrence Journal World, a team of ragtag nerds decided to build
- a better newspaper website, and in doing so, created a set of tools for
- building websites out of databases that then grew into one of the most popular
- ways to make a website of any kind.
- And it's just one of, I think, a whole list of stories and
- examples of where really significant and
- world changing kind of technology was developed in newsrooms.
- And these are stories, I think, that we know in our little world, but
- might not be more broadly known.
- >> Yeah, you sort of think about news as a legacy business, but
- Django is far from the only example.
- D3, the JavaScript framework that helps create graphics was incubated,
- I would say in a newsroom was sort of invented before Mike Bostock got to
- The New York Times, it was really incubated there.
- Backbone underscore sort of a lot of JavaScript behavior frameworks.
- >> Svelte.
- >> Svelte, speaking of JavaScript frameworks,
- very much came out of Rich Harris's work at The New York Times and elsewhere.
- And I found out in writing this piece that the varnish proxy
- write through cache was written on behalf of a newsroom in Europe.
- So there was a time, and in many ways, it can still be here,
- where newsrooms were not just open sourcing code for other newsrooms to use,
- but open sourcing code that people far outside news were using.
- >> And Django, named after the Jazz guitarist Django Reinhardt.
- >> Yes. >> Cuz Adrian Hollabarty,
- massive Jazz fan.
- I always felt that when you put data sets online or put anything online,
- the numbers of people it would attract would be very small,
- often like 10 people, one person even.
- But to me, that didn't matter because it was a sign of transparency and
- the sign of kind of openness, which is important,
- especially in this age of trust deficit that we see.
- How important is it, do you think, like emotionally almost,
- for news to be part of this open kind of culture?
- >> Well, I think it's important to do just for its own sake, as you said, for
- transparency in the scientific spirit of Phil Meyer that really launched
- this whole movement.
- I think it's part of being credible and convincing to your audience.
- But I wouldn't write off its appeal as a product either.
- I think we can point to a number of data products that are incredibly
- successful in the commercial marketplace and with readers.
- If you look, for instance, this week we had the first midterm elections of the year
- in the United States, in Texas, and elsewhere.
- And the live election result pages on all the different news sites that night were,
- I'm sure, drawing absolutely stupendous readership.
- And those are data products, just like anything else.
- And they are made by data journalists, including ones at this conference.
- We can look at the coronavirus story.
- We can look at, this one might be a little controversial to some.
- We can look at the WikiLeaks story and many others, and
- see cases where publishing data on the web has had really dramatic readership.
- >> Yeah, Simon, you bring up a really good point,
- which is the idea of sort of reproducible research.
- And when I was at ProPublica, one of the things that we really paid attention to
- was the rise of the reproducible research movement within academia.
- And the idea that it was incumbent upon us,
- if we were doing a sophisticated data analysis, to publish, initially,
- white papers where we explained, we're sure, a very sort of academic light,
- although never peer reviewed, sort of research papers to help people understand
- our analysis.
- And then later, when Jupiter Notebooks, then called IPython Notebooks,
- came out to actually publish or reproduce what you could run.
- Now you could do it right within GitHub, but you could rerun our analysis and
- see where we might have screwed up.
- We did that very much in the spirit of journalism,
- where we said, here's our methodology down to a T.
- You could reproduce it and do the exact same thing if you have the same data set
- that we do, which often we published as well.
- And you could do it, and if we did our math wrong, we want to know.
- Just like every journalist wants to know if they get it wrong, and
- then want an opportunity to get it right.
- And so that was very much part of our impetus for
- open sourcing all of our data stuff.
- And on some level, that is still happening.
- We're going to be talking at this conference with the Myre Award winners.
- And in both of the cases of the folks we're talking to, ProPublica and
- the Baltimore Banner, they published very detailed methodologies,
- explaining all of the decisions that they had to make, because the data's imperfect.
- And the code out there that's available to do this work is imperfect.
- And all of the decisions that they had to make that brought them to the analysis.
- So that too, it's not just creating Svelte and D3 and open source products.
- But also simply as part of, it's how we are transparent as data journalists.
- We have an opportunity to be very transparent about
- our methodology down to the line of code.
- And why would we not take advantage of that?
- >> But how do we fix this?
- Because I know you guys actually spent a lot of time talking to people about
- how to make things better.
- So what are your recommendations?
- >> Well, we asked everyone, we interviewed that same question.
- We also convened a panel at the News Product Alliance conference last fall in
- Chicago and asked the whole room full of journalists the same question.
- And we've had, I have to tell you, dozens and
- dozens of conversations with different data journalists since the story's come out.
- And we've asked all of them, how do we fix it?
- Same question.
- I don't think anybody knows the perfect answer.
- And I don't think there's one answer.
- There's probably a lot of different things that need to happen.
- But ideas that came up, I would be happy to share.
- One is the idea of trying to make open source more of a norm within the project
- cycle.
- One big reason a lot of projects were open in the past, we heard from people,
- is that was how you started a project.
- If your project started open, it was easier to keep it open rather than
- waiting till the day before you published and
- having to mop everything up and shove it out.
- So kind of better defaults was one idea that they'd come up.
- Another is trying to create in these sort of growing and
- strong institutions that are succeeding right now.
- Maybe a position is focused on this.
- And open source editors, is that what they called it, Scott?
- >> Mm-hm, mm-hm.
- >> What other ideas did we hear?
- Hm.
- >> Well, one of them, I mean, one of them was just like, give it the office.
- Like, if you want open source to come back, open source your stuff, right?
- Start with you, was one of the- >> Pull up your pants.
- >> Yeah, just do it.
- So that was definitely one of them.
- Assigned somebody to coordinate, we heard, make an open source award.
- So sort of best open source project of the year.
- We also heard from people who were talking about, really among the people who
- said, you know, what changed in my newsroom is that we matured not just as
- journalists, but as product managers.
- So as product development became more sophisticated and
- more formalized in newsrooms, you needed an OKR, you needed a goal.
- You know, it had to link to some corporate strategy to open source things.
- And so you just could never prioritize it.
- So one of the things we heard is, you know,
- the bosses should make it one of the goals that we should try to open source for
- projects this year.
- Or we should try to document, you know, five projects this year so
- that we can't open source it.
- So we also heard from people that if one of the reasons that this might have slowed
- down is that formal product management kind of methodologies came in and
- dissuaded people from spending time on this.
- How do we then use that to help persuade people to do this?
- I think we need to welcome in the vibe coders.
- What do you guys think about this?
- 100 percent.
- No question about it.
- There's just this growing group of people who are able to participate in coding in
- this dawning era of AI assistance.
- And these are people that have kind of been locked out up until now.
- And I see in them the ones I meet so much hunger and curiosity and desire.
- And also as a numbers person, I see a growing denominator, just like more
- potential participants, more people to collaborate with.
- And I just kind of wonder how can we capitalize on this moment?
- And I'm I feel like I don't know what it will look like, but I sense that there
- must be the GitHub moment coming for vibe coders where they all are able to not
- just make something in private on their computer with Claude, but share and
- collaborate with each other.
- There's a moment of connection that's happening that must happen, I think, or
- logically follows.
- And I think that there's a way in which GitHub is part of why we had our peak and
- this smaller niche.
- And I think that's why I'm thinking of it as the GitHub moment for vibe coders.
- And I can't wait for it to get here.
- No, I couldn't agree more.
- I mean, how many projects have I looked at where I said, oh, gee, I wish it did
- this thing a little bit differently, or I wish it supported the CMS that I use, or
- I wish it could do this, but I don't have the time or the inclination or I don't
- know this programming language, or I don't feel like I've got the chops to do a
- pull request for this from GitHub, so I'll just live without it, or I'll do
- something else, or worse, I'll make my own.
- And so what vibe coding can do, I mean, vibe coding is, it gets better every
- couple of days.
- I mean, it is already a spectacular at doing software development.
- And one of the things it's particularly good at is reading an existing kind of
- code repository and making changes, including making security changes, you
- know, making speed changes, just sort of looking at it and looking to look at the
- Git history and understand why things were the way they were so it can be
- careful about what it does.
- And so the idea of, you know, fixing a repo that's out there so that it works
- better for your newsroom, or let's say somebody takes a look at your, at Ben at
- your code for, you know, that wraps data wrapper and says, well, how do I do this
- with flourish? Well, you can vibe code, you can have cloud code change so that it
- works with the flourish API instead of the data wrapper API, or let's say that
- data wrapper changes this API, because if it's undocumented, that means I'll be
- going to change it without telling you.
- And then when that happens, it's something that really probably you can vibe
- code. Now, interestingly, there is some concern about this in the open source
- community. So there's a developer who created a system called vouching, where
- they are so concerned about vibe coded pull requests on GitHub, that there's now
- a system where you can vouch for other developers. So you can say, well, I know
- that Simon is a real developer, he didn't vibe code this, so you should let him do a
- pull request to your code base. Whereas no one knows Scott, so don't take his
- code because he probably does vibe coded that. So it's kind of interesting that
- there's sort of, you know, antibodies being deployed to stop this. But I agree
- with you, Ben, I think that it's going to open it up to all sorts of really smart
- technical journalists who may not be able to code at the level that you would need
- to do a real pull request, who can add really critical features that you just
- don't need or you don't see, but but actually make your stuff better.
- It's like back in the day, back when we all started, there was a kind of opening
- up almost democratization of day journalism because anybody can make a
- chart. And now you've kind of got the same thing happening again, right?
- There's a new technology, it's opening it up. Anybody can do it. It does change how
- we do what we do, but it's an incredible moment, I think.
- Without a doubt, they'll be good and bad, but you know, who cares, you know?
- Yeah, I guess I'm still the hopeful one. You know what I mean? To me, I find
- myself at this AI moment, feeling, using the language of my new employer, feeling
- kind of bullish. You know, I feel kind of up about this. And my hope is, is that
- this next AI chapter is going to turn that curve back around that we made in
- our story. And we're going to see more people sharing in the years to come.
- Ben, thank you so much for joining us. Great to have you.
- Thank you for having me.
- Thanks for listening to the Data Journalism podcast. We're hosted by Alberto Cairo,
- Scott Klein and me, Simon Rodgers. Special thanks to Lauren, Chris and the whole
- NikeR team for all of their support with this episode.
- Our producer is Aussie Linus Goodman.
- The music you can hear is the sound of data made with two-tone, an app that turns
- numbers into tunes. This week, we used Google searches for the term "data
- journalism" since 2004. Do you have a new data driven story or project coming up?
- Tell us about it at dayjournalismpodcast@gmail.com and we might even feature it
- on the show. Subscribe to make sure you never miss an episode.
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