Speaker 1 00:00:01 Join us as we gather around the hedge, where we dig into technology, business, and culture with the finest minds in computer networking. Speaker 2 00:00:20 Well, hello, Tom, still in front of the big thing of books, the bookcase that you haven't read. I see Speaker 3 00:00:27 , I still resent that comment from one of our guests because I have read a number of these books, and I've read in all of them. But yes, I'm still there. Speaker 2 00:00:36 Uh, just, just fun to give you a hard time. That's all. Tom . And today we have Yvonne. I don't know Yvonne, are you in your She shed you are subtly blurry, so I can't tell where you are. Speaker 4 00:00:51 Yeah, no, I am still not in the She shed. We have, uh, you know, there is a shortage of, uh, of skilled labor in our world today, and we are still waiting on an electrician to come and get it connected. So it's been moved. It's sitting out. That's Speaker 2 00:01:05 Very sad. Speaker 4 00:01:06 We're waiting on power. We've had some travel, so, um, okay. Trying to get schedules worked out so soon. Awesome. Soon. I'm Speaker 2 00:01:13 Hopeful. There, there, there are no, she shed helpers to help Yvonne with her. She shed Speaker 4 00:01:18 I know. It's sad. . I was thinking, I was trying to think of a way to iterate it, but my brain isn't that quick today. Speaker 2 00:01:25 Yeah. . That's very sad. So Yvonne, I'll be there at, in Louisville. I just actually booked my hotel room for the lo what is it? Speaker 4 00:01:36 Y not it's the, it's the KY nug meeting. So I believe got, I think it's March 21st. I should have looked up that date. Uh, what is the date? Do you have that? Speaker 2 00:01:43 I think it's March 21st. Yeah, Speaker 4 00:01:44 That's what it is. Yeah. March 21st. So, uh, look, look for details. I'll be sharing it on the socials, but Russ is gonna come and talk at our, uh, Ky Nug event and talk about I think DNS and some other fun stuff. I, I Speaker 2 00:01:59 Don't know, I may talk about, I, I may talk about how, how to make networking cool again, I don't know yet. I haven't decided. Okay. Yeah, I don't know. I, I'm still looking at my topic list and Adrian's coming with me, so Yay. She'll she'll be hanging out there at the hotel. We'll get Speaker 4 00:02:13 To, we'll get to meet everybody. It'll be, it'll be fun. Speaker 2 00:02:16 Yeah. Cool. Okay, so today we are talking about, and you are not, well, I guess it's, I don't know when it was actually published. I never, oh, January of 2024. January. So it is, yeah, it is a new one. And, um, a CM communications to the a CM about, about learning and the title of the articles tends thing software developers should learn about learning, but gee, these things all apply to network engineers somehow or just people in general, . So I thought this was, this was kind of a cool thing. Um, so I guess we'll just go through these and, and see where the conversation goes. Okay. Number one is human memory is not made of bits. Okay. Now I would've thought that's obvious. Speaker 3 00:03:02 , Speaker 4 00:03:05 Uh, you know, I think one of the things that, that, uh, I've learned over time and, and this has helped me frankly, like also in my relationships is he, we, we think of human memory as being very fixed, but our memories actually change over time. And every time you recall a memory, that memory changes a bit. And that's, that's why folks, uh, you know, they, they did some studies after nine 11 where they interviewed people a couple days afterwards and even had them write out their story and then interviewed them again five or 10 years later. And they remembered the story very differently. And, and, and when, when asked about it, they would even say, I don't know why I wrote that, because that's not how it happened. Right? And so our, our memories over time changed. So it's not as if we have zeros and ones that are stored in our brain in a collection of bits. Um, our humanity means that our memory functions differently. And, uh, and I think especially for those of us in technical fields, we, we create analogies that aren't necessarily accurate to reflect how our minds work. Speaker 2 00:04:16 Yeah, Speaker 3 00:04:16 Yeah. And the, the other thing is, I, we, we, we store things in our memory that are far beyond the capability of a machine. Like a machine couldn't encode emotion, a machine couldn't, and really encode nearly as much context as we have in our memories. Um, it's just a much more rich set, like it's almost apples and oranges in, in, in my opinion. Speaker 2 00:04:35 Yep. Speaker 4 00:04:36 Yeah, it's very true. If you think about like our five senses, right? So, and, and you think about the connection of either music or smell to memory and how those senses are, are able to trigger memory often, like far better than language. Um, that's, that's some of that additional, uh, data I think Tom, that you're talking about. And it's a fascinating observation. Speaker 2 00:05:01 Yeah, I, I do think that, I think our memory is richer than digital in a sense, than that we store more stuff. Like when you remember something, you're actually bringing it back to life in your memory. Like you can, I dunno if you've ever done this or not, but if you think about like a hot dog you ate as a kid or some really fascinating thing that you ate or something was your favorite dish, you can almost really taste it. Again, it's not as dry as or or as cut, cut and dry, um, or as abstract as computer memory, but it's like Yvonne says, it's very changeable. We remember things differently over time. Um, yeah. So that's, that's an interesting thing. So the other one, that's the second one, Yvonne, we've talked about this before because of thinking fast and thinking slow, and actually this comes up in the book nudge as well, which I kind of have a love hate relationship with these things. Speaker 2 00:05:54 But anyway, human memory is composed of one limited and one unlimited system. So, you know, it says there is working memory and there is long-term memory, working memory is fixed. And it says in this article that its capacity is roughly fixed at birth. You can't do much to change about your working memory, the size or scope of your working memory and things transition between your working and long-term memory as things go on, as time goes on. So, I don't know, I know Yvonne has talked a lot about the, the, the concept of two type of two kinds of memory in the past. Uh, I don't know if you have any more to add. Speaker 4 00:06:35 Yeah, well I think, um, you know, it, it, it's, I've talked a lot and, and a term that comes up in this section quite a bit is cognitive load, right? And, and cognitive load really is the amount of effort it takes us to accomplish a task or to think about a thing. And, and one of the highlighted quotes here, it says, expert developers can reason at a higher level by having memorized common patterns in program code, which frees up their cognition. So the, the more, more patterns that you recognize without having to cognitively consider them and understand what they mean, that that's gonna expand your capability. And, and we know this too from like people who who play chess at a master level, a lot of chess is pattern matching. And so once you know and have, have deeply understood those patterns, you can, you can tie them together in a way that is, um, it is almost difficult to understand for folks who don't have that level of, of pattern matching, but it's, it's because you're able to make correlations with your remaining working memory, that memory, but because you, you memorizes patterns and they're, they're more intrinsic to, uh, to how you think. Speaker 3 00:07:56 Yeah, I think the, I think an example of this in networking is, um, subnetting. So when you're, when you're troubleshooting, if you know, um, I if you're, if you're good, if you're proficient at, um, at calculating subnet masks, um, then when, when you're troubleshooting you, you can just look at a thing and you can know, you can know which address, which subnet this address is in, what it's broadcast, what's not, you know, all that stuff. But once you've done it enough, it's, it's, uh, it's, it's memorized in a sense, and you can look at it and pattern match it, and you can use that in a, in the greater context, um, of what's going on with this network. Where, where is this address versus that one. I think, um, that's, that's one example of once, once you have that locked in, you don't have to sit and think about it and write it on the paper anymore, and it, it gives you, it lets you do other more interesting things. Speaker 2 00:08:45 And, and yet once you're done with the troubleshooting session, two months from now, somebody might say to you, what was the IP address that you were dealing with there? And you're like, I don't remember. Right? That's totally blown past my mind at this point. And Speaker 4 00:09:00 That's okay. Well, and that's, and that's what we talk a lot about, like flow, getting in a flow state. Yeah. And it's, it's getting that, um, your, your short term memory in a state with all the right information in it where you can solve the problem or accomplish the task in front of you. Right. And it actually takes effort to get to that state, and then it is very easy to lose, right. Because, because of the finite nature of, of that short term memory. Yeah. Um, and Speaker 2 00:09:31 I think the big thing for you, Speaker 4 00:09:31 It's why managing our calendars is important, right? Speaker 2 00:09:34 Yeah, exactly. Yes. And why, by the way, it's important to get notifications for meetings. I've missed a couple of meetings this week because I didn't get notifications , but I don't remember, you know, I don't remember that I had a meeting on Thursday at two o'clock or whatever it is. I count on my computer to tell me these things. Um, so I think the, I think the thing that really kind of struck me about this one is just that the working memory is limited and its capacity is roughly fixed at birth that I didn't really expect. Like, so according to this, you can increase your long-term ability memory or it can fade off over time, but you can't ever change your working memory ability, which is interesting to me. Um, because it talk speaks a lot about, you know, why it's important to do things to, to, when you think about improving your memory, you you really need to think about things that are gonna improve your long-term memory, not your short-term memory, because your working memory is just what it is, which is interesting. Speaker 2 00:10:37 So the third one here was experts recognize and beginner's reason. I think this goes back to what Yvonne just said about when you're troubleshooting stuff, that's absolutely true. Um, when I walk into a network situation, oftentimes I can tell you how the network is gonna converge. And it's not because I've sat down and reasoned through everything that's going to happen. It's because I've done that so much that I, I immediately recognize this is a ring, it converges that way that's gonna interact with that having spoke network this way. And it's just, that's the way it's gonna converge. And I think this is true, even when you're code reviewing, even when you're coding, you don't really think a lot about reasoning through. And by the way, this is also the source of a lot of our mistakes is because once we become experts, we stop reasoning, we start recognizing, and sometimes things are just slightly different enough that what we recognize is not what we think we recognize. Speaker 3 00:11:42 So that there, um, expertise is one of those things that to me is just a really interesting area of study. Um, there's a, um, I can't remember what the, I didn't read the whole book, but speaking of books I haven't read , you know, the, the, the 10,000 hours concept, one of the, one of the concepts in there is that, um, often experts couldn't tell you how they knew something. They just knew it. So they, um, you know, you'd ask the ask someone who is expert at recognizing artifacts and being able to distinguish them from a real artifact, from a fabrication, and they look and they, with a high degree of accuracy, they can tell you if something is fake or not. But if they, if you ask them, okay, how did you come to that conclusion? They basically couldn't tell you. They're just like, I, I, I don't know. And it, it probably because, uh, you know, it's, it's the pattern matching and the expertise, you know, derived over years of doing it. They're correct. Um, but they, you know, in, in many cases it's explaining it is a little different. And this is part of why, you know, teaching a thing is, is different than being good at a thing. Um, because as you achieve expertise, you don't necessarily achieve the ability to teach it, but you do understand your pattern matching works really well, Speaker 2 00:12:56 Which is also why teaching a thing helps you remember it better or learn it better because it forces you to go back to the basics and re reason. Right? Whereas if you never teach it, you just rely on the pattern matching and you're just going and going and going. But if you reteach it, then you have to really think through, why did I say that in a way that can be explained? Speaker 4 00:13:20 Well, and it's also like I've noticed that often people who are actively learning a thing are better teachers than folks who are long-term experts at that thing. Because if you're actively learning, then you have recently gone through the reasoning steps to understand, and you may be able to teach somebody who's just a little, uh, little behind you, um, better than somebody that's that's miles ahead because you've recently gone through the reasoning. Whereas it's, um, there's, there's like the, the four stages of competence and like once you reach that unconscious competence stage, it becomes much more difficult to teach than if you're in that conscious competence stage where it's like, I am competent, but I still have to actively think about it. And that is a sweet spot for teaching others, honestly. Speaker 2 00:14:22 Yeah. Yeah, it is. Yeah, it's really cool. So the next one is, um, understanding a concept goes from abstract to concrete and back. So this is always something I've said when I teach network engineering is one of the fundamental skills of a network engineer is to be able to move between the abstract and the concrete at will to be able to look at a system from the outside and say, I kind of understand what it looks like, but the whole opaque box looks like. And then to be able to dive into the opaque box and look at it, it will, to be able to move in and out of layers of abstraction. Um, so I don't know. This is, you know, like again, for example, when explaining a ver a ver uh, veac function in Python to someone new to the concept experts might say that it is a function that takes a variable number of arguments. A beginner may focus on details such as the exact syntax for declaring and calling the function, right? So the difference between syntax and overall concept and being able to extract it out and understand the idea of a function that takes a variable number of arguments is something that is hard for people to get. Um, I don't know any thoughts there on that one? 'cause that's, that's an interesting one to me too. Speaker 3 00:15:46 Yeah, I think the, um, concrete is, uh, like, it, like it says in the article, uh, concrete is, is easier for the beginner to parse. It's easier for the beginner to grasp onto 'cause it's a literal thing. Um, and then concept is, is harder. And the, the idea that you start at abstract and go to concrete and come back, I think is really interesting because when you're first describing something to someone, you can't really dive into the details. You have to start abstract. Um, and then as they begin to learn and know how the thing works, it becomes very important to go to concrete so that you can apply what you're learning. Um, I think a common place where in networking where we have a hard time is we start with abstract, we go to concrete, and then we get stuck in concrete and we never come, like in terms of a person's personal development, their skill development, they get into the concrete, and concrete has business value, and you know, you configuring stuff has business value, so you end up staying there for a long time. Um, but the engineers that I have always looked up to, they're the ones who, um, made the journey back to, uh, abstract. And I think that's, that's really important. Speaker 2 00:16:49 Yeah. Speaker 4 00:16:51 Well, and I, I think about, you know, I I, I have a 9-year-old and he is in the very concrete stage of learning, right? I, I want to know what the right answer is. I want to know like, is this right or is this wrong? Or, you know, we had this conversation at school and I want to, I want to understand how to think about that in, in very, you know, binary concrete terms. And I, I do think like the, the being able to move back to abstract is incredibly important. Um, that abstraction though can be very frustrating to somebody who's in the early stages of learning. And I think understanding when we're in a conversation or when we're teaching somebody, you kind of have to get a sense for where they are and whether like what they need in the moment is a concrete answer or a more abstract way to think about it. Speaker 4 00:17:45 Because I find myself e even with my 9-year-old, uh, it happened this morning trying to be like, okay, but there's another way to think about this, right? And then, but, but if you go there too quickly, it just becomes confusing for the learner because they don't have all the context yet to be able to parse all of that. Um, and so I think there's this interesting calculus that we have to do to figure out like, when does the situation need to be super concrete and when can we dive into the abstract? Um, that's, that's, uh, and, and that's a, a different but very related topic. Speaker 2 00:18:22 Yeah, it is, definitely. Yeah. And, and I think that this, this idea of, like you said, Tom, of being able to go back and forth, uh, for instance, I was just interviewed recently for an article and they chose hardware as something to talk about. And I was like, but that's very, that's very, very concrete. But I understand why people who first come into the networking field are very interested in hardware and in configuration, because those are concrete things. I can put my hands on them, I can do something with them. So that's, that's a thing that, that is very important, um, to start with. But it is important to be able to move out into the abstract levels, uh, at some point. So the next one is spacing and repetition matter. Now this is your, what is it, 30 hours, Tom? Is that what you said? 20 hours, whatever it is. Speaker 3 00:19:16 Oh, no, the rule is 10,000 hours. 10,000 Speaker 2 00:19:18 Hours. Yeah. Speaker 3 00:19:19 Well, yeah. So yeah, I, I think probably what's being dis discussed, discussed here is, um, when you, when you learn things a human, you can't just in a computer, you stick a, a value in memory in, in a, in a variable, reference it with a variable and you're done. Humans don't, it's not that easy for us. Like we can't just, you know, one operation stick stuff in our brain. We have to be exposed to it rest, be exposed to it, rest, and then that's how things become permanent for us. Um, and that's, that actually requires quite a bit of discipline, I think. Um, it's a lot easier not to do that. And I think we also have to be really selective about what we spend that effort, what, what things do we store permanently like that? Um, because there's lots of stuff that's worthless that, um, in, in terms of having, you know, stored in your memory. Is it really worth having that in there? Um, yeah. Speaker 4 00:20:14 Well, and um, Speaker 2 00:20:15 Go ahead, Yvonne. Speaker 4 00:20:16 Well, I was just gonna, you know, we've all had that experience where you're troubleshooting a problem or you're debugging code and you're banging your head against the wall and you've spent a ton of time on it, and you, you know, you're close, you know, it's right there in front of you. You just can't figure it out. And I think like this, the concept of spacing is that sometimes like, yes, you, you need repetition to learn, but also you need breaks in between there, um, to allow your brain to, to, to absorb and to conceptualize. And, you know, I know I had one very memorable situation where I sat at my desk, it was seven or eight o'clock at night. I knew I was close. I was almost there finally, like, I'm just exhausted, you know, it's time to put the kids to bed, I've gotta get home. Speaker 4 00:21:00 And then the next morning I came in and 15 minutes into looking at the problem, I knew what was going on with it. Right? And so I think understanding these rhythms and patterns and how our brains work can help us know, you know, you know what, like the best thing I can do right now is to step away, to go take a 15 minute walk, to go, uh, grab some lunch, to go run an errand, to give my mind some space to process. So there's, there's this flow state that we can get into. And when you're clicking, right, keep going. But like, once you hit a wall, you need to just step away 'cause it's actually gonna be more productive and, and get you to a solution more quickly if you take a few minutes and, and back off. And, and to me, that's, that's what I think about when I see this, like spacing and repetition matter. You need repetition, but you also need time between those repetitive motions. Speaker 2 00:21:57 Yeah, and I would also say that this is something that, that people who write applications and stuff, um, take advantage of this actually. This is where you get into addictive properties where people can use variable reward to push specific learned behaviors into your long-term memory. And it's very hard to break a habit once it's in long-term memory that you do this and that happens, and you can do this and you get a reward, whether it's endorphins or whatever it is. And so this also relates a lot to habit making processes intentionally building habits, that it doesn't really help to do the same thing at the same time every day all the time. Sometimes it's better to give yourself a very, a variability in that, because that actually builds into your long-term memory better. So that's another area where I think these kinds of things can come into play in the way that we think about intentionally learning. Um, and if you're thinking about, I'm gonna go learn networking, I'm gonna learn this new protocol, it may not always be good to set aside 10 o'clock in the morning every morning to go do it. It may be better to study twice one day and once the next day and skip the next day. Because the variability can actually help pull stuff from your working memory into your long-term memory better. Speaker 4 00:23:21 And I love that, that the article gives us some very concrete guidance here. It says, the structure a day of learning, learning should limit learning bouts to 90 minutes or less. Um, and that has to do with the, the neurochemical composure of our brains. But then it also says, after each of those 90 minute or less sessions, we should take 20 minutes to rest. Um, which is, uh, like we're not, um, you know, working on other tasks or browsing the internet. It's like we, we are literally resting by going for a walk or doing something that's, that's, uh, appears very idle. Um, and it also, uh, adds that sleep also helps with this process, which we calls the consolidation process, where we take what we've learned, we pack that back into our long-term memory, but that is, is actually a thing that takes time. Um, and we have to allow our brains that time or ultimately we, we just won't absorb whatever it is that we're attempting to learn. Speaker 2 00:24:23 Yeah, yeah. So the next one is the internet has not made learning obsolete. And this is actually hard for people sometimes. Why should I, why should I memorize anything or learn anything when I can just get it off the internet? Right? Why should I learn to code this when I can just go to stack overflow and just grab the copy, copy somebody else's code? Why should I learn how to access a file when I can just go to GitHub and, you know, grab that bit of code from somebody else's project? And I think part of the reason is, is that, as it says in the article, we learn by storing pieces of knowledge in our long term memory and forming connections between them. If we don't have anything to work from, then there's nothing to make connections between. And therefore we don't, we can't move from the reasoning to pattern recognition for real, because we can't recognize the patterns. There's no, there's no connections in our brains to do it. So I, and article, and I think back to like article again, go ahead. Speaker 4 00:25:28 I was just gonna say that the article also, it, it refers to cognitive load again, right? The, the, the act of going to find the information as opposed to having it in your memory and being able to pattern match and recognize and, and compare that contextually to everything else going on. Like, that's not gonna happen if that's not in your brain, right? So if you have to research it and find a fact, and then like, there's a load associated with that, um, that takes resources away from cognition regarding multiple facts or multiple pieces of information. So it's still important to have it in your brain. Speaker 2 00:26:09 Yeah, Speaker 3 00:26:10 I, I, I find that, um, a lot of things that I need to do, I have to synthesize, um, from multiple, uh, sources of, of whatever I find on the internet. And if you have, um, if you have the connective tissue in your mind of concepts that are already in there, it makes that, uh, so much easier. You, you can, for example, I, I find that I can read most man pages really fast because I see a word. I know what that means. I don't have to sit and read the sentence and figure out, uh, what's the, what's trying to have, what's going on here? What did the author, author intend? Um, but if you, if you just said, oh, I'll just, I'll just have, I'll store the connections in the, in the computer, in the, in the cloud or in the internet, um, then you have to look up every single one of those things. You, your brain can't do it automatically. And so I think that's part of the, the cognitive load, uh, comment there too. Um, and, but, so, but when you have the connections inside, then you can, in, in your mind, you can look at multiple sources, synthesize really quickly. I think the efficiency goes way up, um, when you have made the map in your mind rather than waiting to retrieve the map, um, from an external source. Speaker 2 00:27:17 Yeah. Yeah, definitely. I think that's all definitely true. Um, I, my mind goes back to when writing was first invented and people were saying, well, if you can read and write, then you won't need to memorize anything. It's the end of learning. And this goes all the way back to Greece. Um, that's not really not true, right? It doesn't really replace your human ability. And if you're allowing it to replace your human ability, you're doing the wrong thing. Um, so the next one in here is problem solving is not a generic skill. Yeah. It's learned, I don't know how, how many times somebody has to say that for people to accept that you learn problem solving. This is not a, um, so yeah, you learn how to solve specific problems. It's not a generic skill. So when somebody says to me, you have good problem solving skills, or I don't, or you don't have good, whatever it is, it's all very situational. This, this is why I may not know how to take my cell phone apart, but I know how to read a network, right? 'cause problem solving skills are specific. They're not, they're not generic, like people seem to think they are . Yeah. Yeah. Speaker 4 00:29:46 And that goes back to having context specific knowledge, right? Like, so just, just your ability to problem solve in general doesn't necessarily mean you'll be able to problem solve in another domain. And this is where we end up with the, the problem of folks who have deep competence and capability in one area enter the public sphere and think that they know how to solve all problems because they have deep, deep competence, um, in, in a particular area that those, those skills don't necessarily translate without the expertise and contextual knowledge. Speaker 2 00:30:23 Yeah. And by the way, this is, this is also movie stars who played a, a, a farmer in a movie and now think they understand farming because they had to go research it and sitting in front of a congressional panel or something and saying, I I know farming, I played a farm. Wrong tv. No, no, no, no. That is not deep expertise. I'm sorry, . It's just really not. So, and we do that an awful lot, particularly in the public sphere. We think that because someone's a technical expert, they should also know a lot about privacy and the importance of privacy. That's not really true. I mean, you know, there's a, there's your expertise is, is is your expertise in a given area. And speaking of expertise, you know, Tommy, you were talking earlier about how being interested in expertise, the next one here is expertise can be problematic in some situations. I think we've talked about this a little bit already, and how yeah, sometimes because you are an expert, you miss things. Speaker 3 00:31:27 Yeah. One of the other points in here I think is really important is, uh, the relationship between experts and beginners. Experts often help to train beginners, but beginners with that experience and training, others do not often, often do not realize that beginners think differently. Thus they fail to tailor their explanations for someone with a different mental model. Uh, that's a, that's a, that's a big problem. Like we, we look at experts and think of them as, oh, they're these wonderful people. Um, but there's some real downsides to expertise. And one of them is not being able to relate to other people who are not experts. Uh, that that's a huge downside and it's almost universal among experts. Yeah. It's, um, and you know, often they're, it doesn't mean that they're all mean or that they're, they're bad people. Um, it, I know many experts that are extremely kind individuals, but still there's like scratching their head looking at this person who doesn't know, doesn't have the level of expertise they have, and they see, they sincerely don't know what to do with that person. Um, and I think we often assume that because someone has expertise, they also have the ability to explain it to someone who doesn't. And it's just not the case. Most of the time, experts don't have the ability to explain it to someone who's not also an expert. Speaker 2 00:32:37 Yeah. Speaker 4 00:32:37 When an an example of this, that, that, that put this into stark relief for me, recently, I was at a, at a sales conference and, um, you know, we were talking about inventions and, and inventors and, um, bent, surf's name was mentioned. And, um, and, you know, as, as the inventor of, of, of protocols and, uh, and somebody kinda within my orbit was like, well, what is IP? And how, like that is actually for someone who would ask that question is quite difficult to answer, right? To think like, how do I answer that? Well, it's, it's what makes the internet work. It's like the protocol that, that everything we do is founded on, but beyond a basic, like this is how it applies to you, you think about answering that question, um, after a certain degree of expertise in networking. Um, and, and it's actually quite difficult to have a, to give a meaningful answer, right? And that's just a small example. And, and Tom, I had highlighted that exact same paragraph. Um, and, and, and it just says more eloquently what I was trying to say earlier. It's somehow, sometimes however knowledge becomes so automated, it's difficult for experts to verbalize. And this was what Russ was getting at. Like that I just know, like, I I could just tell like I've seen this before, um, which which can be a double-edged sword, but certainly, um, is one of the challenges of expertise. Speaker 2 00:34:13 Yeah, it is. And, and you know, the mental model thing, a lot of times what's hard for an expert to understand is that the person that you're talking to just doesn't even have a mental model. What you're talking about with what is ip. It's not just that they don't under, it's not that they're mental model is deficient or small, it's that it doesn't exist. And here is where it's important to be able to go back and say, okay, there is a concrete real world example. What can I do to illustrate this concept in a real world example, right? What, what does that mean? Um, like for ip, the one I always use is a shipping container. Like you encapsulate something in it that you're gonna ship it, it has an address, people read the address and figure out where to send it. That's about the closest I can come to real world IP there. I mean, I don't know what else, like, it's not perfect, but I'm sure there are better ones. But like, that's what you gotta do in your head. You've gotta think, okay, this person has no mental model. Now what do I do? Um, so the next one is the predictors of programming ability are unclear. This is so important because we think that we're gonna be such good interviewers or whatever, that we're going to figure out how to tell who's gonna be a good network engineer and who's not. Speaker 2 00:35:40 And to be honest, it is not something that you, it's very hard to do and it's never going to be perfect. Um, it's very difficult to predict how any given person's gonna fit into a culture, how that person is gonna, what their abilities are in their environment. Again, part of this is because problem solving skills are specific, they're narrow, they're not broad problem solving skills. So sometimes you bring somebody in who has lots of problem solving skills in one area, and that doesn't necessarily translate to solving problems in other areas. Speaker 3 00:36:20 I, I, it, I feel like in my career, as I've interviewed people over the years, I've gone from, um, in the early, my early, uh, time with it from, okay, I want to find out what skills they have and maybe they don't have all of'em, and that's fine, but let's, let's find out the ones that they have and then know where that fits in. And it seemed reasonable at the time, but the more I interview people and talk to people, the more I'm like, hiring is a total crapshoot. Like there is, there is no way to even, like, I almost have abandoned the whole idea. It's, it, the interview should be a gut check and it should be, make sure I can stand this person. And basically you get what you get and they're gonna be good at some things and they're not gonna be good at some other things. And your team is, the personality of your team is composed of the personalities of all of its members. And it's gonna change every time you hire someone, your, your team changes. Every time you lose someone, your team changes. And , I, I don't know, that probably sounds a little fatalistic, but it's, it's kind of like there we can't predict, but, but we just have to, you know, hire people. We can stand and we'll come up, we'll come up with something. I don't know what you guys think. Speaker 4 00:37:26 The way I've been saying that lately is that people will surprise you, right? And they'll surprise you in both directions. Like, there'll be somebody who's like, you know, I wasn't sure they were gonna work out, and they end up just being an incredibly stellar contributor. And then there'll be other people that you were sure that they were gonna, uh, fit in and be great and really set the world on fire. And, you know, there may be mediocre or worse. So, uh, you know, I, as, as, as fatalistic as it is Tom, like I, I've observed that as well. Like, hiring is a crapshoot. There are things that you can do. Um, you, you do wanna be sure it's somebody that like you could stand working with day in and day out. Um, and there are some basic like bars for competence, but at the end of the day, it's very, very difficult and people surprise you. So that's all I got. . Speaker 2 00:38:17 So the last one I think is really important is the, your mindset matters. Um, so here I think the money quote is a fixed mindset aligns with an aptitude view that people's abilities are innate and unchanging applied to learning. This mindset says that if someone struggles with a new task, they're not cut out for it. A growth mindset aligns with a practice view. People's abilities are malleable, says that if somebody struggles with a new task, they can master it with enough practice. And I'm not sure either of those is absolutely true, but I think it's important when dealing with people and even with yourself to say, yeah, I'm not getting this right now, but maybe I just need more practice. Or maybe I've been practicing a lot and I'm not getting it. It's time to quit Speaker 4 00:39:07 . It's, we, you know, I, I know mothers who get very offended if you challenge the idea that their kid, you know, they will say, well, my kid can be anything they wanna be, and you challenge that idea and all of a sudden they're very offended and, and so yeah, I think there's balance here, right? Can can, can they be absolutely anything they wanna be? Well, you know, there are some like capabilities and, and, and circumstances that just are beyond our control. Yeah. But, but at the same time, I find the people that are most compelling, most successful, um, and, and really do, do the most to grow, learn, um, help and, and contribute, have this idea, have have a growth mindset and, and, you know, continue to try things that that and, and, um, have confidence in the ability of other people to grow and change. And I, that's something that I've observed in, in some of the most phenomenal people that I've, I've worked with across my career, is not only do they look at the world that way, hey, I'm gonna try this thing, it'll work, or it won't and will get better. But they also see other people that way with, with an optimism in their ability to grow and change. And I do think that's incredibly important. Speaker 2 00:40:33 Yeah, Speaker 3 00:40:35 The uh, um, the thing that I think is in the article, um, the author seems to, there seems to be a, uh, an idea that growth and fixed mindset are sort of fixed. Um, and that's what you have. Um, but I, I disagree with that, at least in the case of children. I think children can switch between the two depending on their environment. Um, but I think I, I, I think I I I somewhat agree in, in adults and, um, I, I, my last comment on this is the most important thing about whether it's fixed or growth is how you treat yourself. Yeah. Um, how you evaluate other people kind of doesn't matter in that way, but how you evaluate yourself I think is really important. Speaker 2 00:41:13 Yeah, I don't know. I do think it matters how you see other people because it reflects on you as well. Like the way you treat others sometimes reflects on the way you treat yourself. So the more you see other people as being able to learn new things, the more you'll see it in yourself as well. So true. Uh, alright, so let's wrap up right there. Uh, I know Yvonne has to take off for another meeting, so Yvonne, how can people get in touch with you or follow you? Speaker 4 00:41:40 Yeah, you can find me on, yeah, yeah, yeah. You can find me on LinkedIn at Yvonne Sharp. Uh, I still, uh, am on the platform, formerly known as Twitter. You can find me at Sharp Network there. And I am doing some semi-regular writing over at Packet Pushers. So look for content from Yvonne Sharp there. Speaker 2 00:41:55 Cool. And Tom, Speaker 3 00:41:59 You can find me on LinkedIn. Speaker 2 00:42:00 That's it. Speaker 3 00:42:01 See, that's my homestead. Yep. Speaker 2 00:42:03 He's, he's just his that, that's it, you know, just like one thing. Yes. One thing. And Tom, I mean all those books in the background, that means you must think that you can learn things. Speaker 3 00:42:14 I do think I can learn things . In fact, I think, I think you can learn things. Russ and the people listening, they can learn things too for they Speaker 2 00:42:23 Can learn things too. That's right. Yeah. I think, I think the whole learning thing is really important and just think being metacognitive about your learning skills and how you're doing things and, and observant of yourself is really important. That's why I think this was, this was a really good paper to go over. So, um, anyway, alright, cool. So I'm Rus White, you can find me here at the hedge on rule 11. Do tech on LinkedIn and on the platform formally known as Twitter, we now call x. And I think that's about it. I do write it packet pushers from time to time, uh, about once a month I think. And I actually write over it. Mind matters for anybody who wants to look that up, but this's not generally as technical content over there. Um, anyway, so thanks for spending the time with us. We hope you found this show or this recording, this episode to be really useful in your thinking and how you think about learning. And again, thanks for listening to The Hedge and we will catch you next time.