Jerod Santo: Here’s a story. In 2015 or so Shea’s employer had layoffs. One of them was the only contributor to part of the codebase that made money for the company. He remembered reading about truck number - which is the same thing; it’s just trucks instead of buses - and thought it’d be fun to write a GitHub enterprise plugin that calculates who you can’t afford to fire. Shea found a research paper called the Truck Factor Research Paper, which actually went through this in a somewhat vigorous way.

“I started writing the plugin and talked five minutes on it at our Thursday afternoon lightning talks.” His co-workers said it would immediately hit Goodhart’s Law - once a metric becomes a measure, it no longer is a good metric - and they say they saw it as a way for management to easily calculate who you can fire. So all good tools can be used for good AND evil.

But long story short, I went out, I used this Truck Factor paper, which actually describes a way you can go about calculating this, at least fuzzily, to find out the key contributors to projects… And ran it against the Linux Founda– no, the Linux Kernel. Don’t run it against the foundation, run it against the Linux kernel. Found a way using the Linguist plugin to filter out documentation and third party libraries just to make sure it’s actually the people who are like working on the Linux Kernel, and found that not only is it small, even on something as large as Linux kernel, it’s going down.

Currently, Shea found a Truck Factor of 12. 12 people on the Linux kernel, down from in the 40s when it was run originally. So from 2015 till now, from the 40s down to 12. And his collaborator, M. Clare, who did some visualizations and stuff, also installed the plugin on her system. She got a factor of 8.

I’m not sure why there’s a discrepancy between the two. This is a developer’s blog post that I’m mining for this information. But yeah, so we have now this tool. I assume you can go take it and run it against other repositories. I would think, depending on the repository, it may or may not be accurate. Because if you have – probably the smaller the project, the harder it is to know that number for sure. But maybe you can know that number just by looking at the list of contributors in that case. But for large projects with thousands of contributors, you can actually understand how many of these are key in order to keep that sucker running. And it turns out, over time, it appears the Linux Kernel in particular has been going down, down, down. What are your thoughts on this, Adam?

Justin-Casimir Braun: Yeah, so I think it started essentially a bit later than in the United States. You kind of have this trend in policing, in kind of risk analysis… I would say that it begins in the early 2000s, where you kind of have semi-governmental organizations doing credit risk scoring, the first kind of instances of predictive policing, and also more serious thinking around big data mining for some risk analytics in the welfare context.

[10:23] And then I would say there’s a bit of a bifurcation. So you kind of see some instances where big industry players, Accenture, the Palantiers of the world, these big, big companies hype up the case for big data analytics to be deployed across different sectors… And at the same time, you have a lot of failures when those tools are deployed; they often don’t work very well, or people who have to use them in the agencies don’t know how to use them. You see some agencies that drop those systems, and at the same time you see other agencies that kind of build up internal capacity, and build those tools themselves, sometimes in collaboration with universities or smaller startups… But you kind of have these true pathways that continue to coexist at the same time.

I would say in terms of the systems that we looked at, most of them were developed kind of from the early 2010s onwards. It’s definitely gotten a lot more in the last five or six years, and across the eight or maybe nine countries now - I’m not quite sure how many we’ve looked at - but I think we’ve only seen a single country where we did not see evidence of predictive analytics being used to assess risk in welfare.

Dan Guido: It’s not. A lot of it is support for new cloud providers. Sometimes we tighten the constraints on the AppArmor policies or the CPU accounting, update some dependencies now and then… But again, even if there’s some kind of exploitable bug in strongSwan, a remotely exploitable bug in the VPN service that’s on the Algo VPN server, you’re gonna have a lot of – you’ll have to get through the AppArmor sandbox, you’ll have to avoid tripping the CPU accounting, you’ll have to get out of all the Linux security controls that we tightened around it, and then you’re on this box where there’s literally like nothing else running. It’s kind of a difficult environment for somebody to successfully attack.

The need to patch the day something comes out – and nothing like that has ever come out, because finding remotely-exploitable bugs in services like strongSwan is kind of like mining for diamonds nowadays; it’s really, really difficult.

Jerod Santo: Alright. We have six definitions for jaggies. Only one of them is actually the definition. Here they are. Number one, containers used to hold raw mining material for being processed. Number two, stair-stepped edges in pixelated graphics due to low resolution or aliasing. Number three, comfortable pants for remote working. A play on jeggings. [laughter] Number four, an irregular pathway taken by an object in response to magnetic stimuli. Number five, in digital photography, when an image is not clear, the image will have jaggies. And number six, a Gen Z slang term initially used in colloquial typing and texting to refer to the lightning bolt emoji. And this usage has now transitioned into the realm of software engineering. In this technical context, jaggies indicates sections within code where high computational load or intensive processing is handled, drawing a visual parallel to the sharp, energetic nature of a lightning bolt and its link to electricity. Okay…

Johnny Boursiquot: You go underground, you go underground. It’s like real mining. It’s like coal mining, except for data.

Feross Aboukhadijeh: I’ll just second what Tobie said… I think we’ve been super-lucky with the types of attacks that we’ve seen so far. It could be much worse than what we saw in this particular attack. I think we’re starting to see these kinds of supply chain attacks happen on a much more regular basis. It’s sort of picked up in 2021, and it hasn’t really let go since. I see a headline about something like this every two weeks, it seems like; sometimes even more frequently. And if you look at the kind of packages that are being affected, they’re all these pretty headline-grabbing numbers in terms of downloads.

So the two packages in this incident were faker.js, which has ten million downloads a month, and color.js, which has one hundred million downloads a month. But we’ve also seen attacks against UAParser.js and COA, as well as RC… And all of those packages had at least 30 million downloads a month each. When malware makes it into one of these packages, through whatever mechanism - you know, in this case it happened to be the maintainer themselves who added the malware. In the case of the three I just mentioned, UAParser.js, COA and RC - those ones were actually a third-party who got access to the modules and added malware to them.

In all these cases I feel like the attacks have been pretty naive and caught pretty quickly. We’re not really looking at super-sophisticated actors here. In the case of faker and color it was very obvious that something had happened, because the packages were outputting gibberish code, infinite loops, sort of outputting the Unicode text – it was very obvious that something had happened to the package.

And then in the case of the three I mentioned earlier - all of those were pretty obvious as well. There were crypto miners added to the modules, so when you updated, your CPU usage would just go straight to 100% and it would be mining crypto for the attacker. So this isn’t like a super-sophisticated thing; it gets caught within a couple of days. Someone notices, the malware gets removed from npm, and then we write a bunch of new stories about it and people talk about it.

There have been hints at much more scary attacks. If you go back to 2018 with event-stream. The event-stream incident was much more sophisticated. A bit of obfuscated code added to one of Dominic Tarr’s packages when he gave up the package to another maintainer to take over after he was done working on it… And in that case, that was an attack that targeted one very specific company. And it was a no-op for everybody else. And so that went undetected for much longer. I just think it’s interesting that we haven’t seen anything that sophisticated since 2018, which makes me think either people just have forgotten that those things exist, or maybe we’re just not finding them, which is the much scarier thought.

Jerod Santo: Gift is literal, but let’s analyze it a little bit, because it’s a fascinating gift, right? Because it is a snapshot in time… The gift you give - like you said, each commit, each thing released… That release is the gift, and that’s a snapshot in time, and then it’s received. You give it to the world, but it’s received individually or corporately, you come to it yourself and receive that gift… And it goes and lives on in a world that changes. You gift it a snapshot, but that has to live on in a sea of change. And the person who receives the gift, because of the value of the gift - I’m just empathizing a little bit with the angry users, to a certain degree… Because we have to see where they’re coming from as well. I think it’s valuable as maintainers to understand the angry customer or user, because it helps us not take everything personally and it helps us deflect. Why are they acting this way? I’m not giving any excuses to be a jerk or anything like that, I’m just trying to understand. If I receive this amazing, valuable gift, I start to build my house on top of it, right? I may not even understand it. Large corporations, experienced developers - we love open source because it’s open and modifiable, and I can go in there and fix that bug. Lots of people who receive the gift actually don’t have the skills to do that. So we find ourselves dependent and unable to do anything about it, and then we start to build our life around that gift.

[35:50] And then things go wrong, because maybe the gift didn’t change, but the world changed around it. Bit rot is a very real thing. It wasn’t a bug, but now it’s a bug because I updated this other thing, or somebody decided they’re not gonna use time zones anymore because of some sort of vote that happened in this part of the world… Crazy stuff happens. And now I’m just like “I’ve got nothing to do. What do I do?” Well, I just turn back to the gift giver and I’m like “Help!!” And that doesn’t always come out very well, because a) we don’t communicate well, b) like Adam said, we don’t understand the humanity, because all we’re seeing is the text area, or the GitHub issue, and then c) because we’ve got all other problems of our lives going on, like my boss is breathing down my neck, or my website is down, I’m losing money, or worse - I’m mining Bitcoin for some random stranger on my website. It gets real bad. So I think it’s not excusable, but it’s easy to see why the grumpiness happens, because you really do depend on this thing… Which has no strings attached, but you sure wish they did. You sure wish they did. It’s crazy.

Raul Jordan: Indeed. So as it stands today, Ethereum actually runs on what we call proof of work. People have probably hear about cryptocurrency mining - bad for the environment, killing trees, all sorts of things. Basically, the way that Ethereum and Bitcoin work today is they require people around the world to basically run these machines that are specialized computation circuits that use an extreme amount of electricity to basically solve this brute force math problem, and participate in the consensus of the blockchain.

The paradigm is that you’re putting in a lot of real-world electrical power into this system such that it becomes almost impossible that anyone can collectively try to revert that. You cannot revert the Bitcoin or the Ethereum blockchain without having more electrical power than all these people combined that put their effort into it.

So Ethereum is actually migrating to what we call proof of stake; so instead of expanding a lot of electricity, what you’re doing is that you’re instead locking up a lot of Ether, you’re locking up a lot of this cryptocurrency called ETH, and to participate in consensus, you need to put that up for stake. If you do anything malicious, you can start losing that stake. It changes the paradigm. You don’t need this crazy computational thing; it’s just basically anyone can run it on a home server. You can run it on a consumer laptop.

[58:49] Since Ethereum was already proof of work, the transition into proof of stake is very solid, because we can leverage all the security that Ethereum has already over the years into this new paradigm. So we are one of the teams that’s building proof of stake for Ethereum. Ethereum proof of work - the mining, basically - is planned to go away in less than a year, so basically no more shortage of GPUs on the market. You’ll be seeing a lot of really cool stuff happening, estimated I believe to be like 99.98% more computationally effective, and better for the environment. That’s something that we’re excited about, and just keep an eye out on that.

Lucy Lu Wang: Yeah, so the entire project is a coordinated effort by the White House Office of Science and Technology Policy. I think some time in early March a group at Georgetown, the Center for Security in Emerging Technology (CSET) reached out to us at Allen AI to help coordinate the release of this dataset, along with a couple of different organizations. You mentioned MSR (Microsoft Research), Chan Zuckerberg, Kaggle was also involved, and the National Library of Medicine, which is part of the NIH. So all these groups - we’re going to come together to essentially create this dataset to help create text mining and information retrieval tools that could assist medical experts in understanding more of what was going on with the epidemic.

For Allen AI, the way that we got involved is we had recently created a new pipeline to revamp our open research corpus. We had a pipeline for essentially taking these paper documents, which are traditionally in a PDF format, not very easy for text mining, not very accessible, and converting them into a structured full-text format, where you could run these natural language processing models on them more easily.

So that’s our major contribution to the dataset, is the pipeline for both harmonizing the paper metadata that we’ve collected over the years, and also producing these structured full-text parses, so that we can run our models over that text.

Todd Gamblin: We’re not allowed to mine Bitcoin on these machines; it’s not legal to use government machines to mine for Bitcoins, but even if you did, it wouldn’t be worth it. If you look at what people are using for Bitcoin mining, a lot of that is custom chips; they’re very low power and only do hashing… So you’ll do way better investing in that than you will in one of our machines. I think at some other compute centers people have been fired for trying to mine Bitcoin on the machines.

Daniel Whitenack: So much of what we talk about on this podcast has to do with the data side of things, which is whether it’s just the practicalities of often that’s where trying to practically build these systems is difficult is in the data side of things. Or it’s the element of like ownership, like you’re talking about, and these other elements of – I’m thinking of like language communities in particular; that is the area that I work in. This idea that in certain cases, big tech, or whoever it is, is really kind of mining these communities for the language data that they have to offer. And the systems that are built out of that really don’t provide a benefit back down to those communities where their data has been leveraged. So yeah, I really appreciate that perspective.

Before we get into a couple of the individual stories, Bridget, as you were kind of curating these stories for the podcast, how did you decide what themes to kind of focus on? I’m sure there were so many stories related to AI that were interesting in one respect, and not another… What went into the kind of curation process and deciding kind of what to focus on and what stories to feature on the podcast?

Jerod Santo: Right. Well, this isn’t an investment show, it’s definitely a show about software, so I’ll stop down that route. But let me just say that we’ve been watching this, and watched loosely some of the alternative coins or the things that are changes to Bitcoin or inspired by Bitcoin, and like I said before the break, there’s lots of things that come and go… I was even mining some CoreCoin for a few months just for fun; that’s at 0.00 at this point, and perhaps is completely just a footnote in history.

One thing that we look for as we invest our time and our skills and perhaps even our money into communities and software projects, because this is a very large software and open source software projects - there’s over 80 projects that the Ethereum Organization has on GitHub… We look for sustainability and long-lasting things, and one of the constant themes in our shows and in our community of developers is the JavaScript fatigue and the constant churn of new frameworks and new ways of doing things - some people see it as a Renaissance, other people see it as ridiculous… I personally don’t buy into new JavaScript frameworks very often because I’m just waiting for the next one, and it seems like with the cryptocurrencies there’s very much that possibility. Aside from Bitcoin, many other ones have come and gone, and of course, Bitcoin itself hasn’t been around for all that long in the grand history… So that’s one of the questions I have about Ethereum, especially as we look at it as app platform - and we’ll get back to that. Looking forward, you’ve survived what would be considered a PR disaster and really a crisis in your community over the summer… Looking forward, what is it about Ethereum that you believe makes it have lasting power, and that we can have trust, not just in the currency, but in the platform as something that we can invest in?

Tess Rinearson: Yeah, that’s definitely a thing you could do. You could have every attendee at GopherCon have their own asset that’s issued by their… One thing I didn’t really talk about, but if you’re familiar with Bitcoin or you’re probably familiar with mining - which is used for consensus, but it’s also the process by which new Bitcoin is created… And because you have a system for generic assets that often tie to real assets that are held somewhere else, assets on a blockchain are issued and are tied to a private key. Then, basically, to redeem your asset, you have to take it back to the asset issuer and say, “Hey, I see that you signed this with your key.”

[15:58] So what you could do at GopherCon is every attendee could issue their own asset, tied to their own key pair, and then maybe for each person you meet, you can issue them one token, and then whoever has the most tokens wins, or something.

Jerod Santo: [01:07:55.19] So there’s a fella named Patrick DeVivo, who has a website askgit.com. He has done a lot of work around – basically, I think he is retro-fitting a relational database around a Git repository’s history. He allows you to basically query Git as if it was SQL. And I haven’t looked at how he’s doing it; I think he might be doing exactly what you’re describing, but… I think the power that you’re describing in having a relational database on your source control history would allow for a lot of interesting mining and visualizations and connecting of the dots that you’re describing. And he’s doing some of that with Git, but he’s having to add tools in order to provide that kind of a thing.

Sameer Ajmani: So I’ll ground this in just what are the opportunities for the existing house, versus the other opportunities. Let’s look at cloud, for example. Go is a fantastic language for building cloud applications, multi-threaded servers, doing a lot of communication. All the estimates I hear of point at the majority of workloads that could be on the cloud still aren’t. So there’s a huge still future for building scalable software and infrastructure that needs to be reliable and production ready and efficient, and all the things that Go is. So I think our existing sort of base for Go is still really important, and it still requires a lot of investment to do really well.

At the same time, as Cameron pointed out, software engineering and software development is transforming. And we need to pay attention to how that is changing. So I don’t think we know – I remember seeing in your notes, you were asking like “Well, what about ML? What about frontends?” We even looked at Go mobile years ago… We try not to chase next new opportunities just based on what’s hot. We try to be selective. For example, when it was all the crypto and mining stuff, some of the crypto people adopted Go, and people at Google asked “Oh, we see [unintelligible 00:54:56.19] and we basically said “No, we’re good. We don’t need to chase that ball.” But with supply chain security we saw the opportunity to do something that was truly differentiated and was well-aligned with what our existing users would want.

And so with AI - again, we’re not trying to chase the data scientists and the Python users who are iterating on model development. What we’re looking at are people who build in production at scale - they’re going to have to deal with AI, and deal with incorporating AI into their existing systems, and they’re going to deal with models and security and observability. So how do we serve them? So there’s a way to take our existing user base, with their existing concerns, and also think “Well, how are their needs expanding? How is the Go user’s job going to change?” And so it’s both building systems that use models, and also using AI themselves to generate Go code, or understand their Go programs. So I guess that’s the way I think about how our jobs change, is if you are an average Go user, how is your world changing, and how can we, the Go team, facilitate that change?

Johan Vos: No, but there might be signs… For example, with the same quantum computer you could also trick the whole Bitcoin system and do the mining… So you would see that in the Bitcoin price, for example. Now, not to worry people, but even if we think that breaking encryption is ten years away, that is still something to worry about today.

Ashley Jeffs: Made it open source. It didn’t really do much. For about two years I was working on it and it didn’t really – it read from Kafka or ZeroMQ, and vice versa. I had a few brokering patterns for mixing things up… And then you could have like a disk buffer in there as well if you wanted it… Which I didn’t like. In my opinion –

Jerod Santo: Right. And it’s out there in Release Candidate. It’s probably out there as we speak, at least in the official 3.13 experimental, as you guys pointed out. Are there early indicators? Are there people who have been just pining for this and they’ve already tried it in the RC, and are saying “Wow, we’re seeing this”? Or no?

Jerod Santo: Cool. Taylor’s pining for bonus points, but he’s not going to get a bonus point for that. We move now to round six. This is non-STEM, and it’s also a bit nonsense… This word is a tough one. Round six. Your word is Quomo Docunquize.

Amal Hussein: Yeah. And a number of other big tech companies were also interested in purchasing at the time… So it should have just been in hands where the registry and the project, this important part of the ecosystem would actually get the TLC and the love that it deserves. That was like the only silver lining for me when that acquisition happened, was like “Okay, well, at least there’s a big company here to take care of this now.” And problems have only been really getting worse. So it’s not going to come out of npm, and so the question is I think this has to be community-driven, as always…

Adam Argyle: Yeah, a lot of people didn’t get the memo. They gave that memo because people were really realizing that darken and lighten would sometimes give them weird results. And so color adjust was used to do the same work, with a very similar syntax, but in a better color space, so that the results weren’t so weird.

So now we have CSS, which can – it can lighten and darken colors. And so there’s two different ways to do it. One has great support, and one is on its way. The great support method is the color mix function. You call the color mix function, you pass two colors, you can say which color space to mix them in, and you’ll get an output result of them. You can mix a color with transparency, so to make a color more or less transparent, you can mix a color with white, to lighten it, if you want to lighten it in a sort of like mixing fashion… And you can darken things by mixing it with black, or another darker version of the hue. And that’s color mix. It’s kind of – I don’t know, a little basic in it’s way, because it only…

But there’s also one called relative color syntax, which allows you to – so let’s say you have a brand color that’s like #f10 or whatever, right? This is - again, a designer gives you a HEX color, and you need to do manipulations on it. You can now say “color” – we’re gonna use the color function, like we were talking about earlier with Display P3… color, open parenthesis from your HEX color. And when you say “color from” – well, you can even say “OKLCH from”, you’re gonna get back a destructured version of that color in the color space that you asked for it. So you could say “HSL from HEX”, and you’d get H, S and L back. You could say “OKLCH from HEX”, and you would get L, C, H back. And when you get those values, you can use the calc function to increase or decrease them, or divide them, or multiply them. So you could double the amount of saturation, you could divide the saturation, you could divide the lightness. You can also just squash it. So you could be like “Hey, I got LCH back. I don’t care about L. I want a really dark version of this color.” And so you just set L to 5%, or something like that; now you have a really dark version of that HEX color.

And so you’re getting these new functions that allow you to very, very dynamically build out variants, and derivative colors, and like derive colors from other ones, and build entire systems and entire palettes that are very robust, and consistent. Again, if you’re working inside of that LCH, or OKLCH space, you’re gonna get that consistent lightness. So if a user chooses they want the theme to be green, they get a nice, visually-consistent green theme. If they change it to red, they’re gonna get something that’s perceptually the same lightness across the board, just a different hue. And it turns out HSL does not have that power. It has a lot of oddities in terms of lightness.

So yeah, relative color syntax - often called RCS - and the color mix function are here to save the day for mixing colors, making variants, and just doing overall color manipulation inside of CSS.

Dave Cheney: [44:02] Yeah. The goal is never to be the best, to get 20 out of 20, or something like that. It’s about what you can learn. I think the quiz format, it worked, I sound like someone pining for days gone past, when we used to be able to travel and go to meetups and things like that - it’s a format that works better than Twitter clicking, clicking buttons. It works really well in the collegiate setting, in a meetup group… Because you can present the four answers. We would always do that on our meetup, like “Give a show of hands. Who thinks a, who thinks b, who thinks c?” And then you can ask, if there’s a stand-out, or if there’s a lot of people who were choosing a particular option, just pick someone and say “Why do you think that? Explain it.” And then they give their answer and you can pick someone who had an opposing view, and you have a dialogue before you even “A-ha! The answer is actually c!” and “Let me just explain to you the answer.” It’s a really good format for having a discussion and a dialogue, which - that’s the best kind of learning. Rather than just rote learning, memorizing these answers, it is “Show your working. Show me your thought process”, which is definitely, to tie it back to your other question, the style of interviewing I think is more successful.

At Heptio we’d do the thing where we’d ask the candidate to go and do an exercise and bring it back, but it wasn’t just like “Give us your code”, look at it, if it’s above some kind of artificial bar which I won’t tell you about, then you progress. It was like - the next step was you got on a phone call with someone who worked at the company and you would just talk about the code. Just like “Show me your working. Show me how you approached this problem. Why you chose to do it this way or that way. Walk me through your design.”

Interviews are artificial, but I think a lot closer to the kind of discourse that you would have between coworkers on a team… Like, “Let’s talk about how you wanna do this, let’s talk about the trade-offs, talk about some limitations of that approach.” That’s the kind of discussion you would have building and maintaining a service on the team. Interviews are artificial, but perhaps closer to the real one, and also it’s a discussion between two people about the code, rather than simply “What you wrote was good enough. Move on to the next question” kind of thing.

Kevin Ball: That actually to me is sounding like support staff and well-defined roles. Someone who has a well-defined (probably) product management role, that is responsible for lining things up, figuring out dependencies, making sure that the right information gets to the right people… Separating out who’s responsible for figuring out the problem and who’s responsible for doing the work - I think that’s one place where… Those are two very different types of work, and when you blur those lines and have the same people doing both things, or at least for the same project…

I do both of those types of work, but when I’m in an implementation, I usually try to stay only in implementation, and be like “Okay, for this project, y’all are figuring out what you want me to do, and I’m gonna do it. And for this project, I’m gonna figure out what we’re gonna do, but I’m not gonna do it. You’re gonna do it.” Kind of having those separated, at least for me, leads to a lot less of that weird blurred lines, and that type of thing.

Jerod Santo: [22:04] Just thinking about that timeframe, you brought me back, mentioning EC2, or Slicehouse as well, I was like “I haven’t heard that name in a long time.” Ultimately, I think Rackspace ended up buying Slicehost, and trying to provide some VPS services of their own through that acquisition… But I’m just thinking about all of the things that were burgeoning at this time that Heroku, that you guys started to execute. You had Git and GitHub around the same timeframe, you really had the birth of AWS, specifically the EC2 offering; I believe it was at the end of the 2000s; what do you call them, oughts? I don’t know. Like 2008-ish was EC2. S3 was a couple of years earlier… So you have like the actual – your guys’ access to that ability - you can talk about how you built the platform out, but building Heroku on top of that.

Then you had Ruby on Rails, of course, which had been around for a while, but it was becoming so successful that your potential customer base was exploding, and then the pain of deployment for Rails being kind of the sore thumb of what is otherwise an excellent building experience. There were so many things lining up for you; I’m not trying to take anything away from your execution, but when we talk about reasons why businesses, startups succeed, a lot of it is timing… And you guys were in a very fertile ground to build something like this.

Carmen Andoh: Yeah… Really, it’s about that mental preparation, whatever habit that you need to get into. It could be that you wear your blue pajamas to bed, and the green pajamas to work… It’s just the point meaning that you’re preparing your mind for a work mindset. And it’s the same trick as I sit in this spot of the small dining table for work, and this spot to eat meals.

Again, it’s boundaries, separating the psychic space of “This is the home, where I try to relax and unwind from work, but also where I now have to work.”

Carmen Andoh: Yeah… Really, it’s about that mental preparation, whatever habit that you need to get into. It could be that you wear your blue pajamas to bed, and the green pajamas to work… It’s just the point meaning that you’re preparing your mind for a work mindset. And it’s the same trick as I sit in this spot of the small dining table for work, and this spot to eat meals.

Again, it’s boundaries, separating the psychic space of “This is the home, where I try to relax and unwind from work, but also where I now have to work.”

Ian Lopshire: So I always thought the fancy office chairs were silly. Like the Herman Millers, and the expensive office chairs… And I’ve been using a chair designed to be at a dining room table for like two and a half years.

Jerod Santo: [laughs] That’s the first two? A method for extracting gluten from wheat. A controversial chests opening, or the intestinal casing of a rabbit, which is used for French fine dining.

Kevin Ball: I mean, we’re looking for every silver lining we can find. Right?