Adam Stacoviak: Well, I’ll give you the TL;DR. Somebody who was just able to con people with large amounts of money, various bank accounts, he would con one person, then con another person… Credit cards, bank accounts that didn’t match this name, so when it comes to their customer and attaching a bank account to a cash app account, or to a Venmo account that doesn’t match your name - that’s anti-money laundering; that’s what you’re speaking of.

I mention that because this person never really used a bank account or a credit card that was in his name. It was always somebody else’s. So when it comes to these intermediaries - I don’t know how he was able to by-pass this stuff, but that’s what they do. They say “Okay, your account is in this name, Adam Stacoviak. Does your bank account that you’re attaching, money coming in or money going out, match that same name? If not, we’re gonna flag it for anti-money laundering and you have to prove you own it by way of W-2, or some sort of tax form, or–” W-2 wouldn’t fit there, but some sort of tax form that you filled out, that says this account is yours, or whatever. Something. Right? So that’s where that comes into play, this intermediary benefit.

Roey Mechrez: Definitely. And I think stability is well coupled with the ability of the model to generalize. Hypothetically, you have a dataset that is the only distribution of whatever, and you can generalize it well. But that’s clearly not practical. You don’t have access to the entire distribution, and you cannot generalize to the entire distribution, obviously, and you definitely don’t have [unintelligible 00:21:46.06] But when you shift into practice AI, I don’t care about generalizations. I want to be good on what I need to be good at, and make sure that nothing that’s out of that good will come into my model. And that’s why I call it garbage in/garbage out. I want to make sure that I have a trained set, I have its distribution, and that’s the data that I’m going to get, how we’re going to achieve that.

I think that’s one of the most important problems that cause this failure rate, the 90% failure rate that I talked about in the beginning. Because AI researchers, data scientists, they know how to build models, and they can achieve this 95% accuracy in the lab, for sure, with a stable, static dataset. The production is not stable, it’s not static, and it’s definitely dynamic and it’s going to shift. Data drifts, data shifts. Something is going to change. And how you react to those changes. And then why it’s important, by the way.

I think while we’re progressing with value around AI, we’re also shifting from, let’s say, statistical applications, which I define as something where… The difference between 80% or 81% or 82% accuracies - okay, nice; better to the business. But there are mission-critical problems. Claim assessment in insurance, anti-money laundering, fraud detection, defect detection in PCB boards, whatever… These are mission-critical. The difference between 80% and 81% is critical. It could be massive to the business. And I’m not even talking about applications where their business requirement is 99%. I need 99%. The model can achieve 90%. What do we do now? And that can be breached. There is a way.

Gabriel Geiger: Sure, I can start with that. I mean, the reason that we chose the Suspicion Machine as a title for our series is it’s kind of a driving metaphor for what these specific machine learning models are doing within the welfare context. So a while ago we wanted to investigate the deployment of machine learning in one specific area, but weren’t sure which one yet. So in the US, there’s been a lot of reporting about the use of machine learning or predictive risk assessments within the criminal justice system, also in facial recognition; and us over in Europe looked at that reporting and noticed that there’s a big lack of it over here in Europe. And so we were exploring different realms and settled on looking at welfare systems, which is a sort of quintessentially European issue, if you want to say, and in the last decade welfare systems have become this sort of polarizing political battleground within Europe - how much welfare should we be giving out, are people defaulting the state, how much money… And so we wanted to focus and hone in on this one area to make this sort of manageable, and we decided to investigate the deployment of predictive risk assessments across European welfare systems. And basically, what these systems do - I mean, they vary in sort of size and color, but the basic sort of mechanics remain the same, is that they assign a risk score between zero and one to individual welfare recipients, and rank them by their alleged risk of committing welfare fraud.

And the people with the highest scores are then flagged for investigations, which can be quite punitive, and where their benefits can be stopped. So we landed on this metaphor of the Suspicion Machine, because we felt that these systems were oftentimes essentially laundering or generating suspicion of different groups who were trying to receive welfare benefits that they needed to pay rent every month.

Mat Ryer: Could be seen as money laundering as well.

Alexey Palazhchenko: Yeah. So actually, after that happened, I did some research what other people think. Yeah, a lot of people think that GitHub Copilot is basically a code laundering machine, because they publicly say that they teach the code on all the public code, not caring about the license. So I can assume that all public code includes some code you can’t – for example, GPL code. If you use a permissive license, you don’t want to touch GPL code, but you don’t know. The same thing - it would be hard to say “Okay, this code is copied verbatim”, because basically almost nothing is copied verbatim… And then that would be for a judge to decide whether it was copied or not, we don’t even know from the philosophical standpoint which should be considered copyable but not from a legal perspective.

So I would say that by the time we have some precedents and we actually know whether it was legal or not, our judges will be AI tools. So that would be an AI judging an AI.

Chris Benson: So I’m curious, as we start winding up, you really got me thinking about use cases that I had not thought about before, and all the things I might be able to do here… So I’m a little bit like a kid in a candy store. I’ve got to ask you, and I’d like each of you to take a swing at it… It’s pretty cool what you’ve talked about today in terms of what is possible for us. How are you thinking about the future? What are the new, cool things that you have in mind?

[00:44:12.24] I often say like when you’re kind of not necessarily working hard on a problem, but you’re kind of chilling out at the end of the day, and your mind is just wandering in free form, and you’re thinking “Boy, what if we could do this? I could imagine that, and I can kind of see a path forward to get there.” How are each of you thinking about MotherDuck and DuckDB in terms of what the future might offer? If you want to kind of get out there and wax poetic a little bit, and - it doesn’t have to be grounded in current work, but more in imagination and aspiration.

Jerod Santo: Yup. Literally a cease and desist, saying Automattic must stop doing what? Slandering them, the WP Engine trademark, or something?

Justin Garrison: I got at least part of the title good, so this is great. This is a good start now. Bailey, we first met a couple years ago at KubeCon. I was wandering the KubeCon floor, and I was trying to figure out this WASM thing. Like what is Web Assembly… And I went to a couple booths, and it wasn’t really explained really well to me. I remember stopping at your booth and talking to you, and you were so excited about WASM and what it could do, especially for developers and applications… And then mostly around that abstracting away all the little details behind how an application plugs into the rest of the stuff. And it’s been a couple years now… Can you describe a little bit of what you’ve been working on, and what WASI specifically is for how applications fit into this ecosystem of products?

Jerod Santo: Kinda pandering…

Don Beyer: Chris, I should have been more afraid than I was. [laughter] I had originally graduated in 1972, a degree in economics. And I worked for a year or so, and then I decided that – I didn’t know what I wanted to do, I was wandering around… So I thought “Well, I’ll go to med school”, but I didn’t have any of the prereqs. So I went back and did the whole pre-med thing in about 12 months. And at the time, I had figured out that it was just sailing; I was competing against 18, 19, 20-year olds, and I was 23, and I’d worked for a year, I was married… And I crushed. I just felt like I dominated. So I thought maybe I would again; that all these kids were – they’re too busy trying to figure out who their next romantic liaison might be, and how much they could drink tonight… And boy, was I wrong. The kids that I was in school with are so serious, and they have these great technical backgrounds from high school, and they were the ones crushing me. So it was really a great exercise in humility for me.

And I honestly say, I did well in college, and taken courses here and there over the years… I’ve never worked harder in a course than I did on this last Java course. I typed 193 pages of notes.

Jon Johnson: I think the only time I can solve problems is really when I’m not trying to, and you’re forced to sit with your own thoughts. And I’ll share one more example of this. I went with some friends to – I don’t know what… It was like Handel’s Messiah, I think, a Christmas play… I was just so bored, because I didn’t understand what they were saying. It wasn’t English, or I just couldn’t hear anything… And so we were just all sitting there, like “This is not that pleasant.” And so then my mind just started wandering to this stuff we’re talking about, actually, and I solved a problem I was having with my oci.dag.dev website, and I realized I could store things in a particular format to make it amenable to like seeking – so I wouldn’t have to buffer anything at all in memory. I was like “Oh, I’ve done it. I’ve figured it out!”

Matthew Boyle: Actually, I spent some time looking at this today… So one of our teams is responsible for running our internal CI system, and there was an incident where our CI system stopped receiving job requests for a little period, due to like a network blip. So effectively, engineers were trying to [unintelligible 00:45:33.02] to build, and it wasn’t triggering the build. So what we did was we restarted the system, and it meant that folks were basically effectively trying to get their builds scheduled… And this is kind of known as the “Thundering Herd Problem”, is like something fails, and then everyone just clicks Retry a million times.

[00:45:53.20] So we had our sort of steady job loads coming in, we had lots of people retrying jobs, we had other systems that were trying to interact with us in the sort of 15-20 minutes (we had some downtime), and this system operated sort of between 60% to 80% CPU at normal periods, but because of all this sort of extra traffic that was coming at it, it all of a sudden went to nearly 100, which meant that all the jobs that were already in the queue were starting to slow down, but then new jobs were still coming in, because we had so many builds going on at once that we kind of ended up in this really difficult situation where we are struggling to process the amount we have, it’s slowing down the system, but then more jobs are still trying to get in at the normal rate… So you end up in this really difficult situation where you kind of have to either scale vertically, horizontally, or make a decision to potentially rate-limit or pause the amount of jobs coming in, so you can process what you’ve got, and then you can proceed. So that’s just one example.

I do have another example from maybe a more customer-facing thing as well… But I worked on a project, it was one of the last projects I worked on before I became an engineering manager, actually, and it was called crawl hints. So you can type in Cloudflare crawl hints; I’ll share a blog post. And it was a project I worked on that I was really proud of. But effectively, what we did is we’d take signals from internet traffic, and then we used that to push information to various search indexes, to let them know that something might have changed in a website.

So if you think about it, before we built crawl hints, what happens is you have all these bots that go around the internet, scraping the internet, looking for changes to content to decide what to rate it on a search engine. And we worked with some of our search partners and we were like “Well, that’s a really inefficient use of resources.” You’ve got all these bots, all over the world, scraping the internet. What if instead we pushed information to them as websites change? So that’s what we did. We built that. Cloudflare is in a unique position to be able to give information about when sites change, so we started to build that.

So what we did is we took a bunch of information from our edge, and we forwarded it to a Kubernetes cluster, and we began to process this information to figure out what was fresh, and then push it to the search engines. So the service I wrote was in Go, but what was really interesting is we kind of did this on a polling loop, for various reasons that are even more confusing; these search engines had – they have rate limits, right? We can’t push too much information to them within a period; we’ve got to kind of throttle ourselves a little bit. So what we were doing is we were pushing this information into Redis, we were storing it there for a bit, and then we were pushing it to the search engine.

So we had these situations where the system I was running was like really spiky. You’d basically have like no CPU usage, and then all the CPU usage. And we traditionally have things like Horizontal Pod Autoscaler set up on our Kubernetes pods, which is a very fancy way of saying if various things start to go up, then you scale the pod horizontally. So we basically have this thing where the pod was like bored, it was bored, it was bored, and then all of a sudden it burst to life, and it has all this traffic, and it has all this work to do… And it was kind of making all these confusing signals and metrics and graphs and things. So trying to figure out a way to – instead of kind of letting the Horizontal Pod Autoscaler scale, or to have a whole bunch of CPU available, it was about trying to tune the workload so that we had the right amount of CPU we needed to do the job we needed to do, without having to kind of like scale it and unscale it, if you will, continuously. And it was kind of like that trade-off between “What’s the easy thing to do, and what’s the right thing to do here to use a limited amount of CPU?” Because as we said, we don’t have infinite.

Shannon Selbert: …in a commuter town outside of Chicago. So you know, a wraparound porch… I’m not saying this was the dream house, but we had agreed on it, and we were looking through the house, and the inspection is taking place… And I think we’re just kind of wandering around to kill time, and he turns to me and has this – I mean, a very emotional response about “I do not want this house. I do not want what will come with this house. I think it’s going to cause fights. I don’t like the–” And I just said “Okay, well, we’re out.”

Kyle Wiens: We’ve been doing this a long time… To go back, the first law that I got passed… Let’s talk about cell phone unlocking, because it’s part of kind of the war on general-purpose computing. So my buddy [unintelligible 00:32:33.11] made an unlock tool for unlocking the flip phones back in the day. And AT&T got very upset at him, and others, and so they sued him. He went to the copyright office and asked for help, and in the process, we put together a White House petition. This is in the Obama era. And this is really our first opportunity, or the first time we activated the internet to achieve political change.

So we got the second-highest petition count on the Obama “We the People Petition” site that they’ve ever had for a cell phone unlocking bill. The first most popular petition was to deport Justin Bieber back to Canada, which I think we can also get on board with… [laughs] Obama did not respond to that petition, but he did respond to ours and said that he agreed, and so I got experience flying back and forth to DC, and [unintelligible 00:33:21.15] and we got it done. We went from where the US was the only country in the world where it was illegal to unlock a cell phone, to it had been relegalized. That was the first time that we touched that Section 1201. So that was back in the 2012 era.

And then I’ve spent the deck last decade since then fighting for Rights to Repair laws. And it was a long time kind of wandering in the wilderness working on this. We have introduced hundreds of bills, and we have lost hundreds of times before we’ve won four in the last year.

Jerod Santo: What up, nerds? I’m Jerod and this is Changelog News for the week of Monday, October 30th 2023.

Move over, T. Swift! Changelog Beats logged over 5k streams in our first week as an ‘artist’. Now if we could just get one of our tracks into a viral TikTok dance challenge…

Oh, and we’ll be at KubeCon NA 2023 next week! No booth this time, so you’ll have to reach out to meet up or hope you run into us wandering around in the hallways like everyone else.

Ok, let’s get into the news… which is admittedly web dev heavy this week.

Kris Brandow: So prototyping is like you have a known, defined quantity of what you’re trying to do, and exploration is just kind of wandering around, trying to acquire new knowledge and information, perhaps discover something new. I guess they’re both forms of discovery, but the success criteria for them is quite different… And I think that means that the types of tooling and kind of environment you want for each of them is also quite different.

Daniel Vassallo: Honestly, nothing very specific. I got to approach it like I’ve approach 2022, 2021 and 2020, with just an open mind, taking care of my downside. My main concern, my only goal, I would say, is to sustain my current lifestyle and to not go back to a nine to five job. I know it’s not the end of the world if it were to happen, but for me it would be a significant lifestyle downgrade.

[58:10] So the attitude of doing whatever it takes to maintain this, I think it’s a helpful attitude for me. As I mentioned before, I almost feel incapable of being highly creative, highly productive, unless I feel the need to. So right now things are going well, I have sort of this routine, I’m doing these regular cohort courses, I have the Small Bets community, there’s a steady stream of people joining, things are going well; I don’t feel I need to do any major changes… I have some plans to improve things, working on some small improvements here and there, but nothing radical.

But again, I recognize that this thing might not last. No idea whether by the end of 2023 it’s still going to be working as it is today. It could be much worse, it could be even better, or anything in between. But I’m fine with that. I’m still with my eyes open, and if I run into some idea, some opportunity, I have lots of slack in the system right now, mostly in a state of wandering about, waiting to pounce on something… And to me, basically, after the year that started, I would have been incapable of predicting how it’s going to be ending. So if I were to get this question, at the end of 2021 I would have certainly not predicted how 2022 would have finished. And same thing for the previous year.

So I think I would be foolish to try to predict where 2023 will end, and again, for the type of personality that I have, I think I like it this way. Some people might say, “Oh my God, it would be a nightmare for me to not know what I would be doing, how much I would be making”, but I realized actually I think I need this… Because if I don’t have some variance, some unpredictability, some ups and downs, I lose almost the drive to actually do things. If things are too steady, I become incapable of working. So I recognize it, and when things are good, I take it easy; when things start to show some signs that they need some correction, I don’t need to convince myself that I need to work harder, or try other things. Suddenly, almost another part of my brain activates, and I become super-motivated. Again, I start seeing better, new opportunities, new ideas, maybe new, creative ways of marketing my existing products and services, and so on and so forth.

Johnny Boursiquot: And basically, I’m part of the team that basically has been working on this sort of integration for months now. We had different systems talking to each other, and everything else. And development, and even in staging, everything works perfectly. [laughs] Systems can talk to each other, we’re sending lots of traffic, keeping an eye on things, and we are observing to the best of our ability with the tools that we have on production day, basically, which is when students actually sit down to do the thing…

What we didn’t test against is basically having roughly 4,000 students trying to log into the system at the same time. [laughs] And because you have these systems that are talking to each other for authentication, and pulling things, whatever it is, basically, we just had a thundering herd kind of situation happening, and we didn’t account for that, because all of our tests, even our integration tests and everything else - they didn’t factor in that kind of scale. And I take responsibility for that, because I was one of the team leads, and basically, one of my questions was supposed to be, “What is the expected number of users and clients on this system?” And we touched on these things, but there were bottlenecks in the system that we should have better accounted for. And thankfully, we had enough of an understanding of what was happening, we had enough observability to be like, “Oh, crap, we know where the bottleneck is. We need to go do this”, whatever.

So within a matter of about an hour and a half or so, while students are waiting there - because they can’t really dismiss everybody and send everybody home; we’re talking like countywide 4,000+ students, all this coordination across months… To me, this remains the best and worst moment of my career, because I’m like, “Here I am, I’m supposed to be serving these kids.” Often, we are so far removed from the consequences of our code, right? Good or bad. We’re so far removed from it. But here I am, I knew exactly what the impact that my mistake was having on these kids, right? And they already have been given a short straw in life, and here I am just making that worse, right?

So after that incident, I was never again, like, “What do I need to do? What do I need to learn? Who do I need to talk to?” Like, it was – I had to level up. And any point in my career - I can’t remember a single incident that has driven me to level up as much as this one, because the impact was so real. It was so in my face. Just undeniable. I thought that was scary…

Lars Wikman: Yes, because there was not a widely spread practice of carrying computers to other people’s houses and downloading a sort of wandering MP3 collection, that for some reason had disproportionate amounts of Enya in it.

Alex Suraci: [01:08:00.01] Yeah. It’s tricky, because this is one of the things we struggled with with Concourse, was do you bias to place workloads where a cache is present, or where it’s not present? Because if you do one, then you end up with like everything thundering onto one machine. If you do the other, then you’re not caching as much… Ideally, you’re caching once per worker, so if you run things enough, it’ll warm across the board. But yeah, there’s trickiness within there as well, I guess is alll I would say. Sometimes it comes down to the use case, like the user has to know if it’s going to be cheaper to transfer this over the wire, or just fetch it from scratch. Sometimes it’s faster to just avoid the cache.

It’s like a giant repo.

Matt Moore: Because I was working from home when I was at VMware, and then when I took my break, it was just like, there was – I was never really home alone when I was working from home, because she was always there and she’d follow me around the house. One of her health problems - she was deaf, so she wouldn’t always follow me around, because she didn’t know I’d be wandering around if she was asleep. But so we actually got really lucky with Charlie, who’s a great dog, because we started to talk to folks with this breed of puppy… We love this kind of dog. They’re Cavalier King Charles Spaniels. They’re fantastic dogs. We started to talk to them not because we necessarily wanted one right away, because we were still getting over Lily… But during the pandemic, everyone seemed to get a dog, so there was this really long waiting period.

[28:00] So we weren’t telling our daughter that we were talking about this, or anything. And then one of the folks that my wife had reached out to called her one time when she was in the car, and it was on speaker phone, and she was like, “Well, I had someone fall through who was going to take a puppy home like this weekend, and would you be interested in meeting him?” And so this was on like a Thursday. And so we went out and met Charlie on, I think it was going to be Saturday, but she had another litter of puppies all over her house, so we ended up meeting him on Sunday. He was quite a bit smaller… And it was fantastic, both because of him, but also because of sort of - you walk in, you sit on the floor and you get mobbed by puppies. It’s a very therapeutic –

Jerod Santo: It’s like that old adage, “How do you eat an elephant? One bite at a time.” I think that’s the way it goes. But that’s really what the hard problems are, right? Like, “How am I going to eat this elephant, for instance?” A big thing, that’s tough and all these – and it’s like, well, momentum, right? You’ve got to get started. And I really like the top-down approach for this that you described, because the details are where the difficulty is, but your end goal and your starting place are actually relatively straightforward to define. In fact, if you can’t define your end goal, then you’ve got some other thinking to do, right?

[40:20] I’ve done a lot of client work where that’s a lot of the conversation I have with them, is they’re describing this problem or this solution, or whatever they’re describing to me, and I have to either derive from them or have them explicitly state, “Well, let’s forget all of this stuff you’re saying. What exactly do you want it to do?” That’s your outputs, right? Your inputs and your outputs. And if you can define well your outputs, what are you trying to accomplish? What’s the goal of this hard problem? You know what your inputs are because you’re already there. That’s where you stopped, right? You stopped at your inputs, and you know your end goal, and now you have something that’s tangible. Like you said, that’s momentum. And you can go from there. Now the solution in between - it’s going to be a long and winding road. You may find out, actually, there was some other way of attacking it that made it into an easy problem. But you’re going to find that along in the journey. But if you don’t know where you are and where you’re going, then the rest of it is not procrastinating, it’s actually just like mindless wandering. There’s no good end to that.

Jessica Kerr: It’s okay for one of the four people to have their attention wandering at any given time.

Tammer Saleh: And the sad thing about it is they just don’t know any better. They’ve got very simple applications, they’re a small team, and they end up spending a lot of time building these Helm Charts to distribute them, and stuff. You don’t need that. Customize, for example, is a great tool for managing your YAML when it’s being deployed to multiple environments, because you can make very small changes. Customize is much easier to understand, much easier to maintain. If you’re really small, you don’t even need a tool like that. You could just apply the YAML and just call it a day.

[36:14] I think when a team chooses Kubernetes, where it should focus on is automation; building out their own internal automation system, not just for managing the cluster, using Terraform, which is by far the best tool for that kind of stuff, but also for managing the resources inside the cluster. A CI/CD pipeline, maybe using GitOps at the end, or whatever… That’s the fundamentals that your team should focus on, because once you have that, all the other changes become simpler. And frankly, that automation is half of the value prop of Kubernetes, because the Kubernetes API is so good. It’s so easy to automate stuff through Kubernetes. And if you’re not investing in that automation, you’re wasting that value. And then, obviously – I mean, I run a company, so I should say that… If you’re just choosing Kubernetes, you should be looking for training. And I love our workshops, obviously, but there’s others, right? But you do need to invest in your engineers’ knowledge, because they are going to have to debug it when it goes sideways, and you don’t want them floundering and using Stack Overflow in the middle of an outage.

We offer engineering services, usually not for people who are just now adopting Kubernetes, unless you’ve got a very interesting application you’re moving over, but you should be finding experts, either hiring Kubernetes experts, or finding a partner that you can integrate with your team, that will give you those subject matter experts for Kubernetes, because you’re going to save a lot more time and money in the long run if you do that early on.

Stephen Whitworth: Yeah, absolutely. So when stuff goes wrong in technology organizations, and it goes wrong fairly frequently - you get paged by PagerDuty or Opsgenie, and then you sort of get dropped into this white space where you need to define a process. And what often happens there is that, I guess, you’re floundering. There’s a lot of stuff to do. You might need to go and tell the executive that’s responsible for the area, but you also might need to SSH into the machine and reboot something, or simultaneously trying to investigate the logs and see how bad it is. And in reality, these are probably a few different roles. But the lack of having a sort of structured, automated way to pull apart your incident usually means that chaos ensues and you kind of take all of these roles on yourself. And what we’re trying to do is say, “No, you get to encode your process in the way that you’d like to respond to incidents into the tool”, and as a result, we can give those different responsibilities to different people… And including taking a lot of the process management onto our tool, so no other human has to do it, so you can really focus on the problem and not the process of actually working through the workflow. You get to focus on logs, or communication, or whatever it is a human is best at doing, as opposed to trying to follow a workflow under high stress, which - we just find that never really works that well.

Kevin Ball: I go back and forth… I had been remote for a few years, and I started at where I’m working now at Humu in late October, before the pandemic; late October 2019. I was in person, and I was in person for all of five months, and then we went remote… So kind of wandering back and forth. But I think what was key is prior to that, at that particular company everybody had been in-person, so there was a lot of rediscovery of – or at least everyone on the dev team had been in-person, so there was a lot of rediscovery of remote practices.

Abe Heifets: If you were wandering on a factory floor and you saw a machine that was going haywire, you might throw in a monkey wrench, so that the machine, instead of doing what’s it’s normally doing, is just busy chomping on that monkey wrench and you’ve essentially turned off that runaway machine just by physically blocking it out.

Elisha Goldstein, PhD: Yeah, you need to have the awareness… You know, he has the awareness - because he has to - with the manual model of his car. We have to have the awareness to do that, or else, as Adam is saying, we just go to that. And the interesting about the default mode, speaking of brain science, is that there’s a part of our brain that’s named “the default network.” That part is involved with just kind of – you’re either ruminating, or the mind wandering, or if we’re feeling an uncomfortable feeling, it might say like “Hey, what’s this? How do I get away from it? What are all the reasons this is here?” type of thing. And what we know about that part of the brain - which is also called the cortical midline area - is that it has an inverse relationship with being present. So in other words, I’ll give an example that everyone can relate to here… When you’re sitting there at lunch and you’re typing away your email, you’re talking on the phone or you’re doing whatever and you’re eating your lunch, how often do you really taste that food?

Another example is if you’re really, it’s the most delicious meal on the planet and you’re really tasting that food, it’s like your favorite thing in the world, how often are you worrying at the same time? Not very much. So when one is up, the other is down. And there’s a whole lot of science around that which shows that. But we know that from our practical experience.

Basically, all science does is it usually backs up the things we already know, but it’s a window for us because we’re a culture that believes in science, so it’s like “Oh, yeah. Okay, good. That must be true, because that shows me things I already know. Yeah, I love that.”

That’s important, because our default mode can get us in trouble… Because the default mode, just to kind of point this out - the default mode to stress is to start projecting, our mind starts projecting into the future to see about what are all the worst-case scenarios that might happen… Or it reaches back into the past to say “What do I know about this stressful experience?” and it brings up a bunch of past stressful experiences.

So we either get caught feeling this negative thing from the past, or this anticipatory anxiety about the future… And what we really need to do noting the neuroscience is take a moment and very practically - now that we know this, we wanna lower the volume on the default network in that moment to help ourselves with the stress… And the way to do that - it’s very simple. It’s just like, you can maybe eat a sandwich with more presence, and you’ll do it. Or you can just kind of come to your body, take a couple of deep breaths, soften any stressed-out muscles that might be there, maybe do a one-second gentle stretch to open those muscles up… Because you wanna open things in your body, because that’s the opposite of what’s happening in your body during a stress response.

So we wanna open those parts of our body, take a couple deep breaths, and then ask ourselves – because now we’re in that space between stimulus and response that we’ve been talking about; we’re at that choice point… “What’s most important for me to pay attention to right now? Is it this social media that I’m on right now? Maybe it could be… Because maybe I’m playing, and that’s cool… Or is it this work that needs to be done? Or maybe I need to get outside and get some sunshine splashing on my face. Maybe I need to reach out to a friend. God, it’s been a while since I’ve had any real social connection.” Or “What is it that I need to be paying attention to right now? Maybe it’s that book I’ve been avoiding.” I don’t know, it could be anything.

Jon Calhoun: [56:07] I should say that I don’t hate debuggers – I think I’m similar to Jaana, where I don’t hate debuggers or anything like that; I just think that learning to debug in these other ways helps you train your brain to think about the right things…

Mat, when you said it’s important to sit there and think about the problem, I think that’s the reason – in college especially, I think people should be printing out some of their code, because that’s when you’re learning to think and learning to actually be a programmer… And it’s a useful tool for that. I haven’t printed out code in the last ten years, so it’s not like something I’m doing daily or something like that… It’s just doing it a few times sort of forced me to actually avoid those bad habits that otherwise might just leave you floundering and tweaking one line at a time.