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Track 1: I had never coded in my life before, never written a line of code. Then I, I went to a boot camp soon after graduating, finished the boot camp by the end of 2016, and landed my first developer role at Google in early 2017. And yes, it is insane how fast Things have changed and evolved. Yeah. Yeah. And so, so you did Google and then I think after that you went to Facebook.
Track 1: Yes. I went to Facebook two years
Track 1: Hello everybody. And welcome to the latest episode of easier said than done and how to do it.
Track 1: And today I'm super excited because I've got Clement Mihalyski. Now, I may have butchered your last name there, but maybe not. I don't know, Clement, you can tell me, but I've got Clement on the show. Now Clement and I have an interesting story. So back when I was I just become a software engineer and I was, What 38 years old transitioning from law to software engineering.
Track 1: In my first dev job, I decided, okay, I'm going to try and go for the FANG world and head to the U S and work there. And of course the dreaded DSA stuff happened. And so I joined Clement excellent algo expert. io [00:01:00] and that I believe helped me get into Google. And so I reached out to Clement and said, Hey man, you helped me get into Google.
Welcome to Easier Said Than Done with me, Zubin Pratap, where I share with you the tens of thousands of dollars worth of self development that I did on my journey from 37 year old lawyer to professional software engineer. The goal of this podcast is to show you how to actually do those things that are easier said than done.
Track 1: And since then Clement and I have been in touch about all the various things happening in development. And so Clement. Thank you and welcome and thanks for taking the time out to be with me today. Hey Zubin, thank you for the kind introduction. I remember vividly like four or five years ago when you contacted me about AlgoExpert.
Track 1: By the way, if you want to use AlgoExpert, check it out. AlgoExpert. io, use the code "clem" for discount on the platform. Shameless plug as always. Stayed in touch since then. I remember you were just like a happy customer and since then we've kind of just like bounced ideas off each other and stayed in touch halfway across the world.[00:02:00]
Track 1: That's right. And then while at Chainlink also, I mean, cause I'm still a Chainlink we had also worked together on the Blockchain Expert. Program that you ran and stuff for that. So, you know, we, we've done stuff both professionally and personally, and it's always, it's been a real pleasure to see your evolution.
Track 1: So, thank you for being on the show. And I think the reason I really thought it'd be valuable for my audience, for us to have a chat today is there's been so much change, man, like even five years ago, when I reached out to you, right, that was before the entire LLM boom, that was before COVID the world has changed a lot .
Track 1: And I, I guess a good place for us to start Clement is a little bit of a history. Now, when did you become a programmer? I, I seem to remember that you did math in college and then you did a bootcamp, right? Just give me a little bit of the timeline there. Yeah, so I did math in college graduating in 2016.
Track 1: I had never coded in my life before, never written a line of code. Then I, I went to a boot camp soon after graduating, finished the boot camp by the end of 2016, and landed my first [00:03:00] developer role at Google in early 2017. And yes, it is insane how fast Things have changed and evolved. Yeah. Yeah. And so, so you did Google and then I think after that you went to Facebook.
Track 1: Yes. I went to Facebook two years later for just a short two months. And then I left Facebook to pursue algo expert full time, which I had been working on while at Google and Facebook. So I'll algo expert. It's kind of been a thing for like, Fantastic. And what an incredibly successful product and service because I found it tremendously valuable.
Track 1: What I really loved about algo expert is a, you were personally explaining things, be the videos were very, very well broken down and it didn't presume prior knowledge. You didn't make leaps and assumptions about what people needed to know.
Track 1: You broke down the reasoning and it was both a sort of bottom up and top down approach to a given algorithm. Which was super helpful. And then of course you improved the platform so much over the years by having the [00:04:00] different levels and the different the ways you could time things and run it in multiple types of code.
Track 1: That was another thing I think was tremendously thoughtful of you to have the same algorithms and data structure problems in multiple languages. All tested so that people could even read some of the, you know, the, the tests and understand, you know, what sort of edge cases they needed to watch out for.
Track 1: So tremendous learning tool, truly ahead of its time. I mean, at that time, Leetcode was pretty much the only thing. And they hadn't got the videos. I think when you started out, they hadn't done any of the things you'd done. So you kind of changed the industry. So honestly, great job, man. And you're like, in your early twenties, I'm seriously envious of your, of your chutzpah really, really well done.
Track 1: Really well done. Appreciate it. Appreciate it. So look, speaking of so much has changed, let's give people a bit of an overview, man. So 2016, you did the bootcamp that was in the height of the bootcamp phase, right? Like that was pretty much at the peak of, of when bootcamps were efficacious.
Track 1: So let's talk about some of the macro changes in the last decade, because it's pretty much almost 10 years ago now, when you think about it, it's almost a decade ago. At a macro level what are the big [00:05:00] changes that stick out to you last decade versus this decade?
Track 1: Specifically in the context. Sorry, I didn't complete the question specifically in the context of what it is like to get into the industry. So not so much as a practitioner inside the industry, but the pathway in. Yeah, I think the main thing that has changed over the last decade, which is by the way, crazy to say that it's almost a decade ago, but is that back in 2016, I wouldn't necessarily call that the peak of the bootcamp era.
Track 1: I would probably say the peak of the bootcamp era was like a couple of years later, three years after. Back then bootcamps were kind of this niche thing that not necessarily everybody knew about. And it was almost this thing of like. You know, there's this thing called coding bootcamps where you can learn how to code.
Track 1: It was almost like, you know, this, this secret weapon that you can, that you can go into. And some people were a little bit scared, like, is this risky? And then over the next few years into 20 18, 20 19, 20 20, it [00:06:00] became more and more adopted and that was the heyday I think, of like YouTubers.
Track 1: And here, you know, I know that I was. Part of that, you know, like my video how I learned to code in six months and got it to google that got like Two million views, you know, so tons of people saw that and now in 2024, I feel like coding boot camps They're no longer it's no longer a question of whether they are an appropriate Tool to learn coding like it is a given it is one of the many paths you can take to learn to code.
Track 1: Now, does it, is it easy to land a job right now? No, and there's a lot of complications we can talk about, but it's not necessarily because coding bootcamps have gotten worse. It's more the industry has gotten tougher and, you know, the sentiment has changed. But I think coding bootcamps are now very well established because like, yeah, they are one of, you know, three canonical paths you can take to learn to code alongside college and self teaching.
Track 1: Absolutely. And so, you know, you've mentioned something really [00:07:00] interesting that you think it was a couple of years later, that was the peak. And it's interesting you say that, because in hindsight, I can see why that would also have been the case because in 20, nine, 18, 20, 19, I think is when, no, sorry.
Track 1: 2018 is when I, Did join a bootcamp. I left my family here. I borrowed $54, 000 and went to San Francisco and I quit at the end of my first week. And it was a top rated bootcamp. Like it's one that everyone's heard about. And I quit because I realized that personally in that particular scenario none of them were really teaching me how to change career.
Track 1: They were just teaching me the literacy of the coding piece of it. Right. And I was in my late 30s already. And I'm like this, I'm not sure this is actually going to make me hireable yet. And I didn't have the time to spend in the U S till I got hired. Right. So for me that I had to come back and I had to go through another, go through another route without being formally trained.
Track 1: So. The interesting sort of observation I'd like to make on that and a question asked for you is has the industry [00:08:00] matured to a point where hiring managers are looking for different things?
Track 1: Because I have seen that happen in other industries. Around the 10 year mark of an industry's boom. It's maturity gets so high that the bar just jumps up a lot. And what people look for is much more sophisticated than 10 years ago. The expectations are high. What is your view on that? So I think it's, it's very difficult to make broad sweeping statements about the industry, because at the end of the day, every company is different.
Track 1: Every hiring manager is different. Every recruiter who looks at your resume is different. And sometimes. You can be at the mercy either in a negative way or in a positive way of like a single recruiter, you know. I've always given this, this example for my own situation that I was struggling to land interviews back in 2016, 2017.
Track 1: Like it was really difficult despite having, you know, an Ivy League Math degree and all that. Like it was very, very difficult.
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Track 1: Like it was really difficult despite having, you know, an Ivy League Math degree and all that. Like it was very, very difficult.
Track 1: People think I had it easy. I did not have it easy. And I always asked my Google recruiter, why did you give me a shot? You know, because so many other companies just like ignored me and he just said, you know, I saw that you had a math degree, [00:10:00] I saw that you had a coding bootcamp, and I thought this person, you know, has a good shot of passing the interviews.
Track 1: So all that to say it's hard to make broad sweeping generalizations. That being said, I think that in this day and age, Because the market has tightened there's way less nonchalance in the hiring. Like, it's not like, oh yeah, we're gonna hire 50 engineers and pay them 300k a year to do nothing.
Track 1: It's no longer like that. I think that companies have a higher bar, and unfortunately they rely on certain, sort of like, safe metrics. Like, oh, does this person have experience? It's much tougher if you don't have experience. And then you'll see, you'll see some companies who will put things like their requirements online, like, Oh, you need to know JavaScript, C plus plus Solidity and like a billion other languages, and those, I don't know that I'm necessarily taking as seriously as
Track 1: as some people do, because I think it's just, they're putting keywords, you know, they're trying to come across as like fancy and all that, but at the end of the day, [00:11:00] you know, it's like, you're going to need to know software engineer, you're going to need to know how to code, and I think you're going to need to have like, A lot of tenacity and sort of, you know, maverickness in the hiring process, if you want to be successful.
Track 1: And I, and I think that's the second bit that people tend to not remember is that it's not enough to just learn to code. You need to attack the industry. As you said, you knew how to code and you still had a tough time getting interviews and the first one's always the hardest, but that's true of everything.
Track 1: The first time you learn to drive the first how you buy, it's always the hardest. The first business, you start always the hardest, right? The first job you get in a new industry is always the hardest. Would you say, that the bar is not higher than it was 10 years ago from the recruiter side or from the hiring manager side, or would you say that the bar has increased along with the industry's evolution?
Track 1: It's tough. Like, I don't know that it's necessarily the bar that has increased. I think it's more like there is, there are fewer positions available [00:12:00] than at certain other times in history, namely the 2016 to 2020 era. And there is more competition because there is no, there's an influx of developers, whether brand new, you know, who drank the Kool Aid of like, I'm going to learn computer science and land a high paying job or people who got laid off, unfortunately.
Track 1: And so that increased competition and the fewer positions makes it tougher to get a job. And if you want to label that as a higher hiring bar, Perhaps, you know, but I don't know that that's necessarily the most appropriate thing to say. And then of course you sprinkle into the mix AI, which makes things more difficult.
Track 1: Like a lot of companies probably now expect you to know how to leverage AI tools. They may or may not test you on that in interviews, but that's certainly going to be a factor. That's why I recommend that definitely people [00:13:00] like people should definitely, you know, become comfortable with AI tools and leverage them.
Track 1: But yeah, it's definitely a harder market. It is a harder market. And again, it's something I noticed previously. In, in my previous careers, including in the law you, I think you're absolutely right in that the numbers work a certain way, which is just economics. There's a demand and supply asymmetry that happens.
Track 1: Right. But that also changes behaviors in that in a buyer's or rather, yeah, in the buyer's market, because in this case, you know, hiring managers are buyers of services in a buyer's market where there's lots of people, putting their hands up saying, I'd like to go for this role. They need better filters.
Track 1: And that drives different behavior changes in terms of how you filter out. Cause if you're now getting 10, 000 applications instead of 5, 000, which is a wild exaggeration, it's usually more like 300. That's actually closer to the real numbers. If you're now getting three times the number of applications you did 10 years ago, you're going to have to have a better filter.
Track 1: And also from what, from speaking with recruiters And I'm talking [00:14:00] about external recruiters. I understand that the number of applicants that's noise, that is applicants, not even close to being qualified for the role has almost tripled in the last 10 years because of things like LinkedIn, easy apply.
Track 1: And, and all of these now, I think all these systemic factors changes hiring side behavior. Right. Because you end up having to have better filters. You end up having to have more, you know, ways to to rule people out, which means people get ghosted. Then there's this, you know, irritation about that. And then you try to bend over or you have more difficult problems.
Track 1: And I've seen this certainly being on the hiring side in tech is that once the applicant quality increases a lot, the kind of questions you ask, especially in the behavioral side or the kind of signals you look for get harder and harder because it gets benchmarked to higher quality of applicant, just sheer numbers.
Track 1: Right. And there's this skew certainly that was the case in the law. And I, I believe I'm seeing it in my limited area of tech in the world that I occupy, but definite. Increase in , in the sort of [00:15:00] expectation. At the moment, I actually think I'm looking at true up at the moment.
Track 1: And we've returned to pre pandemic numbers. So about, you know, 250, 000, just under that roles available at the moment in tech. Not, not the most precise measure but during the pandemic, it, it went up dramatically. And, and I think people thought that was the baseline, but I don't, that's like saying the temperature you have when you're running a fever is the baseline.
Track 1: It's not, that was the spike, you know, and that was the spike. Yeah. And I think people are still comparing to that and saying, Oh, it's a precipitous drop, but it's a precipitous drop from a spike back to pre pandemic levels. But I think what's changed is That's the number of roles available. What's again, what's changed seems to be the number of actual hires being made.
Track 1: Everyone seems to be keeping their powder dry. Be careful in budget, seeing where AI goes and all the rest of that. So, you know, it's, it's a, it's actually a complex analysis and the way I'm sort of trying to encourage people to think about this, who are listening to us is it's not typically what, as simple as what you see on Reddit or in some [00:16:00] blog, there's lots of dynamic factors at play here and it may have nothing to do with you as a candidate and more to do with the market environment.
Track 1: Right. Would you agree with that? Yeah, definitely. And I think like, you know, to go back on the thing of the hiring bar, it's like if a company is now hiring half as many positions as before, and they are getting three times as many applicants, and they've noticed that of those three times many applicants, a bunch of them are just flatly unqualified.
Track 1: Yeah, they may start to incorporate more stringent filters that may or may not be good, but are good for their problem, which is their problem. They have way too much stuff, right? So as an extreme example, they might start to say, okay, we will literally disqualify anybody who does not have a CS degree from Harvard.
Track 1: Just giving an example, right? And that's rough. That's brutal. And, you know, but, but it's like, it could be a thing. Now, of course, not every [00:17:00] company will be like this company, perhaps 20 percent of them or 50, 80 percent of them, but there will be some companies that are not like that. And so unfortunately, or fortunately, depending on how you view it, it comes back to, it is still a numbers game.
Track 1: Just like in 2016, it was a numbers game. It is still a numbers game. And so as an applicant, you must be able to be like resilient, like emotionally and all that, understand that it's a numbers game with a little bit of like actual personal things that you can do. Like, that's what I mean when I say you have to be a bit of a maverick in your application.
Track 1: For example, you know, maybe you want a position at a company that you really believe you would be good. And you have to contact somebody at our company and try to get that sort of like, you know, sort of sneaky entrance. Exactly. Yeah. I spent a great deal of time with my students talking about that because, you know, with my, when I'm personally coaching people, because I think it's.
Track 1: People tend to take the easy way out and hit that LinkedIn easy apply button, just like [00:18:00] everybody else does. And I'm like, look at it from the hiring manager's point of view, you've got the sea of seemingly eligible candidates and you're not making it any easier for them to pick you out of the crowd.
Track 1: Right. And think about how you'd want to be, the only one in a sea of gray wearing the yellow jumper, like how, how do you do that equivalent from the hiring managers point of view? And we're not talking about gimmicks. We're talking about Communicating and signaling value, but exactly as you say, you need to be a bit of a maverick to do that, or you need to really put yourself outside of your comfort zone, but then, Hey, that's kind of what growth looks like and the kind of jump you're making, especially if you don't have a CS degree from a fancy, university, you don't have the yellow jacket.
Track 1: That a lot that some people do, you know, that you need to stand out with. So, and that's a hard thing for people to understand because unfortunately I see more and more people trying to solve the, I'm not getting interviews problem with grinding more DSA, you know, and that's a bit of a [00:19:00] mismatch. Grinding DSA is important when you have an interview.
Track 1: And, and like there, yes, I will always tell you, if you have an interview, you definitely want to cherish it in this market and you want to do your very best to be the best. So, you know, get AlgoExpert, spend a lot of time trying to prepare and everything. But until then that you could still, you know, do like daily practice.
Track 1: I'm not going to tell you like, don't do practice. If you want to be ahead of the curve, do it. But like that is not going to help you land interviews like whatsoever. So you got to do these other things. Exactly. I keep telling my students, I'm like, if no one's coming to the shop, Don't buy more stock in inventory, you know, market the shop, get people in the door, then buy the stock.
Track 1: You know, like you, you can't just keep adding more stock to the shelves and hope more people come in. That's not how it works. So, okay. Now you made a really interesting video, Clement, about three months ago. A dramatic one about, I think end of bootcamps or death of bootcamps or something like that to that effect.[00:20:00]
Track 1: That's not actually what you're saying. Of course, you had a very positive uptake at the end or rather a very balanced perspective. It wasn't particularly positive, but definitely wasn't negative. It was more a. You need to do the research kind of, you know, analysis and understand what's going on. So let's talk about that in the context of your own experience, having done a bootcamp in 2016.
Track 1: Why did you not choose computer science degrees or some other format? So in 2016, or rather like four years before, when I got into college, I really didn't know what I wanted to do in my life. I knew I wanted to create a company, be an entrepreneur, like vaguely, but that was it. And I was turned off by coding, the little bit of coding that I had, like, seen from other people.
Track 1: It was scary, it was daunting, and that was, you know, the classic like misinformation. You know, if I had just given it a chance, I would have realized like it's fun. But like, you know, right now if I tell you learn a brand new language with like [00:21:00] an alphabet that you do not know, you might be very turned off.
Track 1: But then if you start doing it, you might enjoy it. So that was kind of why I didn't do computer science in college. But when I discovered coding boot camps, that was, It was at a point where, like, I started seeing friends who were getting good, high paying jobs from computer science, and I was a little bit, like, envious, like, Hey, we went to similar schools, we're similarly skilled, you know, but why am I not being able to get this?
Track 1: Then I realized that coding would be super useful for creating a company, and so that's why I went to a coding bootcamp. And I think what's nice about coding bootcamps is that they offer you this very, like, dense, Like that's the word boot camp, right? You have a boot camp where you're going to be kind of like military style, trying to learn a skill very quickly.
Track 1: And that was appealing for my personality and my, my sort of like, life situation at the time, which was like, I need to get a job probably pretty soon. Cause like, you know, I can't just like chill forever after college. Yeah. And were you in New York then? [00:22:00] Yes. Yeah. You definitely needed a job as soon as you could.
Track 1: I mean, New York's not exactly. Yeah. All right. And so now a lot of people seem to think that coding bootcamps are the online courses that you could get on Udemy or go see around and stuff. That's not the book. Like you, you gave the analogy of the military bootcamp, which is an all in highly intensive multi month process of complete immersion and commitment.
Track 1: Exactly, like they're, they're very different kinds and levels of coding bootcamps and courses to me, for example, not to diss on Udemy, probably a great platform, but like, let's take a platform like Udemy, if they have like a four hour JavaScript course. That is like probably the bottom of the barrel in terms of like, you will learn a very, very unique subset of computer science fields that you probably need to already have prior knowledge of to understand.
Track 1: Correct. Then there are sort of more legitimate, within the context of [00:23:00] learning how to code from zero to a hundred, more legitimate online courses. And here I'll actually seamlessly plug the programming expert, our learn to code product. That product, so it's entirely self paced, right? Like you don't have instructors who are going to be like picking up the phone to answer your questions.
Track 1: But it was developed with a curriculum that takes you from zero to hero, right? We teach you everything that you would learn in a coding bootcamp, just on a platform that is self paced online. And it's more like, you know, 100, 200, 300 hours worth of content. But then there's a coding bootcamp where, you know, whether it's remote or in person, for me, it was in person.
Track 1: These days, there's a lot of remote. It is much more like taking a trimester in college where you are taking a class with a professor with multiple professors who will be there to answer your question, and you have classmates, so you have that whole like kind of experience, [00:24:00] sort of social aspect in a way, but that contributes to the academic aspect.
Track 1: Yeah. Okay. And, and, and I think that's a really important perspective for people to understand is that you need to know your starting point and you need to pick a resource that's appropriate for your starting point and directionally correct for your goals. And that's a really hard decision to make when you're an outsider, you know, very, very hard.
Track 1: And I made that mistake when I, cause I'm self taught after the bootcamp, after the one week I came back and I had a non technical coach. Really work on my mindset and my, my planning ability, but I've totally self taught. And I actually relied on some Udemy, some Coursera. And then when I was looking at the Google stuff, it is Algo expert.
Track 1: But that was different, like preparing for Fang interviews is completely a different plan from learning to program and crack into the programming industry. You know, it was a completely different, right? So again, people need to understand the two very different things. But on the self taught route, I fell into so many mistakes and this is what I help my students do is I help them avoid the mistakes and the pitfalls [00:25:00] because that's, that's kind of what the correct path is, is avoiding all the wrong paths.
Track 1: I've, I spent so much, like hundreds of hours doing stuff that was actually designed for people with prior programming experience. I just didn't know that, you know, so learn Java in 24 hours or whatever those books are like, it's really for people who already understand programming basically. You know, it's about learning a second language.
Track 1: They're not going to say that on the title, but it's really those kind of resources about learning a new language when you've already learned programming generally, exactly. And just to go back to like the way I framed it, that's what I meant by like Udemy courses for the most part, for me, feel like they're appropriate for someone who already knows programming.
Track 1: They're just picking up a new skill within programming. Then for example, something like Programming Expert, the product that we created, to me, like, if you take Programming Expert as your only thing to learn coding, you will be self taught. That is what I consider self taught, because it's self paced.
Track 1: Absolutely. And I would recommend Programming Expert [00:26:00] to people who fall under two categories. One, they really cannot spend the money that a bootcamp can. Costs, which is like, you know, 10 to 20, 000 or more programming expert is more like 50 to a hundred dollars, right? So very different cost perspective, or I would recommend it to people who feel very confident in their ability, or perhaps who have like a technical mentor, like imagine I were somebody who's older brother, yes, I could tell them just do programming expert.
Track 1: Like, trust me, it's a good product. And I can kind of like. You know, I can kind of clarify the sort of scary thoughts that you have. Yeah. But if you're alone, which is a lot of people, right? And you have this daunting thing of like, can I actually learn to code? Like, can I actually learn to code and get a job?
Track 1: Like, is this a real thing? That's where a bootcamp is very helpful because it gives you that structure and that sort of assurance of like, This is a school, like, we're [00:27:00] teaching you this, 100%, man. And, you know, I think people could also step back and take a macro view, like, we already know this indirectly, right, but you and I, okay, you did math in college, I did not, but you and I speak the same English language.
Track 1: You and I know the same geometric formula for the area of a circle and so on and so forth. And we understand whether it's Euclidean geometry or something else, or even the basis of economics, right? We had different textbooks, different professors, different countries, different schools, but we know the same principles, right?
Track 1: It's because the content actually is the same. The delivery mechanism may be very different and each person needs to find their own delivery mechanism. But where people tend to fall down is accountability, structure, consistency, and direction, right? Yeah. Content is pretty much like you write the same line of Python.
Track 1: I write the same line of Python. It's the same thing. You know, it's exactly the same thing. It's going to end up doing the same thing. Whereas people, two different people walking into the same [00:28:00] interview are going to get different results because life is not that deterministic .
Track 1: Right. And I think that's the missing piece is whether it's a bootcamp or something else is figuring out how do you go from having the skills to having the job. And what most people don't realize is, I mean, I've set it out in a chart of seven steps, but there are four steps after you learn to code, including how do you get interviews?
Track 1: How do you do well in those different types of interviews? How do you choose the right offer? That's directionally correct for your goals. Cause not all coding jobs are the same. I've had people get stuck in, In QA roles, wanting to then get stuck out of it or get stuck in DS data science roles.
Track 1: And actually what they wanted to do is more programming, you know, and things like that. And so all these other choices that need to be made are really hard. If you don't have the map or you don't have a guide, like you said, someone's older brother, you know, sort of guiding you. Those decisions are very difficult ones because they lead you in a different direction.
Track 1: Every decision is a fork and you could go down a completely different branch of the tree than you want to do. And that's scary for people, as, as it should be. And I think that [00:29:00] that's where people really find it hard. Like I wouldn't try to reverse engineer how to bake bread, even though I know what the ingredients are.
Track 1: You know, I would look at a recipe. I will get help. And, and that's how I encourage people to think about it is it's not just about having the ingredients in your cupboard, which is, I know some Python and JavaScript, or, react or I know whatever it is. It's about how do you put it together to bake the bread, which is getting.
Track 1: Yeah. And that's where it's really helpful to have. either some sort of mentor or to at least like basically look at videos like this one, you know, this podcast, which hopefully will, will reach people and, and like provide them with this knowledge that they might not already have, because that helps give them that sort of map.
Track 1: A hundred percent. And that's why, you know, I think I love all the AlgoExpert products and programming expert. I've not used it directly, cause I was already a programmer by the time you launched that. But it sounds like the kind of thing for people who have really great personal discipline and focus who can absolutely self execute on it.
Track 1: And, shameless plug on my side, what I do with the Inner Circle Program is pretty much the same. Ultimately content, [00:30:00] you know, we, we. Produce or pick the right content for the person on their goal. We spend a lot of time we spend weeks, not joking weeks, just baselining the person, not just the technical skill.
Track 1: How much time do you have in a day? Cause most of my students are married with kids or working, in their thirties. There's a serious opportunity cost at that time. So how much time do you have? How consistently can you do this? What are the things that break your discipline? What are the things that are likely to trip you up?
Track 1: Because at the end of the day, there's no point in giving someone a map. If they think the road is smooth and it's not. It's the potholes that are going to break your axle and you have to know these potholes, these risk factors, and then work around them consistently and have someone unblock you.
Track 1: And then once you've learned to code, how do you get the attention of recruiters? Very hard to do. And it depends on the individual. Some people are not very good at reaching out. Some people are very good at it. Some people don't know how to communicate with engineers and communicating with engineers is a little bit from communicating with other people.
Track 1: So, it's, it's a hard problem to solve.
Track 1: Would you say based on your video, cause I I've watched it and I'll link to it. Cause I think [00:31:00] it's a very important video for people to get a really balanced perspective, which you're good at Clement, but.
Track 1: If you had to summarize, what are the challenges that the boot camp model is facing going forward that you felt is resulting in the shutdowns and being absorbed by universities and other things? These changes compared to 10 years ago. What are the challenges now that they're facing?
Track 1: Yes, first of all, to clarify, one of the main things that I said in that video is that all the sort of negativity around bootcamps was not about the bootcamp product, like the product that they sell. The product that they sell is still very good. Of course, there's some bad bootcamps, just as like, There are some bad apples, you know, but it's more about the business model itself for coding bootcamps.
Track 1: It's a very difficult business model to scale to be profitable and especially in like high interest rate periods of times they're just tricky economic times It's very hard to to have [00:32:00] enough students like basically And the incentives are kind of mismatched because at the day a coding bootcamp You Its main goal is to get students getting students a good job or the good skills Is a secondary goal But it is not the primary goal The primary goal is to get as many students as possible, right?
Track 1: And right now we're in a market where it's like You don't want to get as many students as possible because they physically cannot all get the five jobs available. Yeah. Then, you know, there's a mismatch and that's why coding bootcamps have struggled a lot. I think a lot of them are shutting down or merging together .
Track 1: Like at the end of the day, I think bootcamps are here to say it's just that we might have just a few big players that are too big to fail, kind of like banks. Rather than a bunch of players. Yeah. And I think that's, so that's an important distinction for people listening to this is that it's the business model of the bootcamp, which is kind of the reason why I didn't create my own bootcamp and ended up going the personal coaching [00:33:00] route is the business.
Track 1: Model of the bootcamp appears to be sort of similar. It's, it's like a micro university model. It has to be profitable. It has to be profitable and like colleges, like I went to a couple of really good colleges for my law and my, my MBA.
Track 1: There's never an outcome guarantee about the job that I've seen. Right. And. That's what makes me nervous about the bootcamp thing is that they have these strange job guarantees. And I'm like, I don't see how that works in a truly free market. Like how could that, and some of them did that, or were these sort of weird stipulations of like income share agreement, like you only pay if you get a job and all that.
Track 1: But then it's like, then you realize that, okay, so their business model. Starts to morph into like a lending model. They're finance. Exactly right. They finance. And so, and now we understand why the business model doesn't, is not all sometimes in rainbows because. They're not just an educational company.
Track 1: They're also like a bank, you know, or like a [00:34:00] financial company, which means it affects people's credit score in the U S right. Cause I think credit scores are very different around the world, but I think in the U S is it's a, it's a tough credit score system that you have in the U S. These semi lending models affect a student's credit score, right?
Track 1: Wouldn't they? I have no idea. I think like, I'm not sure, like an income shared or an entity. is like appears on your credit report, but certainly other things, like if you take actual debt to get into a coding bootcamp, then yes. Yeah, which is fair. I mean, ultimately an income sharing or deferred payment is debt.
Track 1: It's just, it may not be called it, but it's debt. It's a, legal obligation to pay back. So it's a debt, you know? So that, that, and that's interesting. So so I thought very carefully about launching my own bootcamp model when I was wanting to sort of teach people how to switch careers to engineering.
Track 1: And the reason I didn't do that is, is because I felt my incentive, my incentives would then misalign with the students. And so I went the opposite way of saying, I don't really want to grow huge. I just want to grow enough to, to serve the few people, to get really good results for a few people. And that's enough for me.
Track 1: [00:35:00] Personally, like I don't need to make this an industry. I just want to make the self sustaining. And so I've, I've chosen a very different model, which doesn't require me to grow beyond a point. It just needs to have a consistent set of students. And I only work with, you know, 10, 12, 15, depending on the mix of students for a full year.
Track 1: That's not a lot of people for a full year so that I can give them the attention they need. And to hack on this or hammer on this point of like the bad business model of bootcamps You see you just nailed it Like you have the ability to grow as much as you want As little or as much as you want which means and you're not like in debt to investors or things like that So overall, you can actually serve your students.
Track 1: Whereas a coding bootcamp that might have raised millions of dollars from venture capitalists. What is the VC model? The VC model is you've got to grow, grow, grow to get a return. Otherwise you're just losing money. And how do you grow, grow, grow by getting more and more students. But when you're now in a market where there's so much competition, And so few jobs is it's a [00:36:00] tricky model, even though the product itself is still very good.
Track 1: Absolutely. No, I, and the products have always, you know, the ultimately learning to code as a product is not the hardest part of it. Like that's an easy enough thing to, to put together. But you know, the, the lack of incentive alignment does worry me because parents of students of children. Will send their kids to a school where the teacher to student ratio is favorable.
Track 1: Fewer students per teacher, but the bootcamp model, it's around scale, then it's about maximizing that student to teacher ratio so that they can make the returns on their investment for their investors. And that's kind of the opposite model from what a parent would want for their child.
Track 1: So, there's a natural tension in those incentives which I think , will be interesting, especially the way AI, is coming through and what actual keystrokes mean the job. Now it's different from what it was 10 years ago. Writing code is, I'd argue, not the most important part of the job anymore and it never was.
Track 1: So last question for you before I let you go, man, because this has been a really interesting [00:37:00] conversation is, and this is a tricky one. So, Assuming Clement had all the knowledge and insight and experience of today, 2024, but was back in 2016. And wanted to get into the industry. What would you do?
Track 1: So if I were, I'm back in 2016. Yeah. But you just happened to have the knowledge. Yeah. Somebody just matrix style downloaded the knowledge of an experience into your head nine years in advance. Yeah. Honestly, I would do what I did. I would do exactly what I did. And of course, you know, maybe like, maybe I would take a right foot forward.
Track 1: Like the one day that I fell into a puddle, you get what I mean? Like that, I don't know the nitty gritty, but at a macro point of view, I would do what I did for two reasons. Oh, sorry. Go on. Yeah. Go on with the reasons. Yeah. Two reasons. And then remember what you were going to ask me. So number one is like, I'm very happy with my path overall.
Track 1: I was fortunate that my path [00:38:00] was actually quite good. And in fact, on paper, it's almost picture perfect, even though nobody knows the pain that I went through and the tough times and all that, but on paper, it's picture perfect, right? Coding bootcamp job at Google. A couple of months later. So that's Facebook, then my own massive business.
Track 1: But so given that it was overall at a macro point of view, good. It's like, why, why change the pudding or whatever the expression is, right? Like the white change is what I did. But the second thing is like. I do think that 2016 was a great time to go to a coding boot camp. And so I still would go to a coding boot camp because, like, it taught me the right skills.
Track 1: And I know that I have the sort of right mindset to then find a job and all that. And just to hijack your question for a second, Imagine the exact same question, but I'm now in 2024, not in 2060. Yeah. That's a little bit trickier. And basically 2025 at this point. That's a little bit trickier. I still think I [00:39:00] would do the same thing.
Track 1: I still, meaning I still think I would attend a coding boot camp because At the end of the day, like, like we just talked about, I still believe in learning to code, and I still believe that learning to code is important. Now, it has different flavors today, right? Now, today's like, you gotta also learn AI tools.
Track 1: You gotta, adapt to the market and, you know, It might be way harder to get a job, but like, I look at all the, all the industries out there, and it's like, coding is still a very good industry, right? Like, if you want to build software, if you want to build an online company, or any company for that matter, you're gonna need some coding chops, you know?
Track 1: So it's still very useful and I think it's, it's kind of asinine to claim that it isn't. Like, that's why, even though I'm not trying to be, you know, Gus, all sunshine and rainbows, like there are some scary parts of the industry right now, but I think it's axonime to say that, like, to be a doomer and say like, Oh yeah, coding is dead.
Track 1: It's like, [00:40:00] it's not dead. It's not dead. It might be very different. And like AI is a huge part of it now, but AI has not replaced us yet.
Track 1: Probably won't for a very long time, if ever, but who knows? I'm not going to make sweeping predictions there, but I don't think it's going to replace software engineers anytime soon in the next five years, even though it's getting insane but still, it's not going to like a company is not going to say, okay, no more software engineers. Like we just have AI, you know? But yeah, so I think like if, if coding is appealing to you as a skill, and if you want a good quality of life and a job, you know, where like all things considered, you have it pretty well, you can sit at a computer, you know, you're not like breaking your back all day long and everything.
Track 1: And that's still going to be high paying. I think coding is great. 100%. And I think also people who worry about the AI taking over software engineering, I feel it's based on a fundamental misunderstanding of what software [00:41:00] engineering is, because it's really not about writing the lines and lines of code and solving the DSA.
Track 1: It's about designing products and the decisions and the trade offs and making them commercially valuable and understanding what to fix and prioritize and focus on. And That I'm not sure the AI can will do because those are human centered design decisions that then get translated into lines of code, so I think that brings me on like two two final points about AI fully agree with you, even though it is incredibly impressive that an AI is so good at let's say algorithm style competitive questions.
Track 1: And I wouldn't be surprised if an AI can can do all the problems on how the expert, you know, like the best human can. But those are much more standardizable things, right? They're not, like you said, human centered design and all that. But then also, as a quick secondary point here, there are some people who will then criticize, for example, job interviews and say, like, why am I doing a data structures and algorithms problem when an AI can just do it in one [00:42:00] second?
Track 1: The company is not hiring the AI, they are hiring you. So they are, they are still gonna test you on a metric that is relevant to human beings. You know, it's like, yes, AI can destroy the best players in chess in the world, but we still watch humans compete in chess because it's still super impressive for a human.
Track 1: You know, absolutely. And also, when companies are using DSAs, I mean, my experience at Google was I almost never use the DSA stuff once I was on the inside it was the filtering process, right? Not the job. It's a different thing. It's a filtering process. But not necessarily part of the job.
Track 1: It could be. And yeah, the AI stuff is fantastic. It's a wonderful tool. It's a jet pack. It makes me so much faster. It reduces my research. It takes away a ton of friction, But ultimately I have to decide what the AI needs to write. And most of the work was always that anyway, or the important part of the work was always deciding what to write.
Track 1: The actual writing was not the hard part. It was not the important part. It [00:43:00] was the frustrating part. It was not the important part, and when you're building algorithms, but it wasn't the lines of code that mattered at the end of the day. It was. What features do I want shown on the screen?
Track 1: What behaviors do I want out of the system? That's the stuff that you, and you need to be a human being to understand what other human beings truly want in that sense and respond to that feedback. So again, I'm with you. I'm not too worried.
Track 1: I truly believe learning to code is a commodity aspect of the equation. Like because it's the same thing for everybody. What would you do differently in terms of the bigger problem here, which you've correctly identified is there is a structural shift in the job market. Presently. We don't know, and chances are the boom will come back cause it usually does, but it may look different, it's shape may be different.
Track 1: Right now
Track 1: let's say you had the coding knowledge as well, but you were trying to approach the market. It was your first time you're trying to break into this new industry. Would you do anything different from what you did from 2016? I probably would put more effort into doing things that are indeed out of my comfort zone, like reaching out to more people.
Track 1: You know, [00:44:00] the, the classic, like networking, even though that word kind of is annoying, like networking, it's another word. Yeah. Ironically, like going to events, meetups, trying to have a, so sort of like social circle contacting people on Twitter, on LinkedIn, you know, on online platforms. And I didn't do that much, if at all, back then because I didn't like it.
Track 1: And so I was much more like, okay, I'm going to just bang my head against the wall as, as much as possible until something works, but it may be trickier to do right now. Yeah. And like you said, it is a bit of a numbers game and you need to stand out a little bit.
Track 1: And you would have seen this at, at algo expert too
Track 1: is people come expecting the product to solve every problem and to deliver the outcome neatly in in a bow tied package. What do you say to people who just think, like, I'm sure that people will come to algo expert and say, if I just do the DSA stuff, I'll get the job.
Track 1: That's not how the real world works. So what would your advice be to those people? Well, I mean, at the end of the day, [00:45:00] like you just said, it's like, nothing is a magic pill, nothing is a magic pill, nothing is a magic book, no one is going to care about something for your life as much as you will, you know, so that's another thing of like, Look, even if they're your best friends or your best mentors and everything, at the end of the day, you gotta care as well, you know, you gotta want it to be able to put the amount of effort and do the amount of sacrifice and everything, so the point is that you really have to identify in yourself that, like, you want this, and you will do whatever it takes, and no one's gonna save you.
Track 1: No one's going to say no one's going to say it's that's those exactly the words I use with my students because 100 percent of my students will learn to code, you know, because I sort of, there's not too many of them that I work with and they'll all learn to code, but not all of them will end up actually doing the things necessary to get hired.
Track 1: No, exactly. And I keep telling them, I'm like, you know, you have the skill. You just don't [00:46:00] want it badly enough to do the things to be the cause for the effect you're trying to produce. You've got to be the cause, right? And that come to Jesus conversation that I invariably end up having with, about 30 percent of them or whatever.
Track 1: You can see, you can see the light come on and you're like, Okay. So I have what it takes. I just need to now go out and get it. And like, yeah, you know, there's, there's an unavoidable amount of effort involved in that. And it's not going to just land in your lap, especially not in this market. Right. And people are still finding jobs in this market.
Track 1: So it's possible. Now, how does it become possible for you? That's the question, and that's exactly right. So, sorry, this is the final thing. Don't make yourself out to be a victim, even though you might have a, like, you might be in objectively worse conditions than I was in, in 2016. But no one's going to save you.
Track 1: Like, it's like, you can't, unless you can find a time machine and go back to 2016 where you can be in those favorable conditions. So like I understand, but like lamenting is [00:47:00] only fruitful to the extent that it's like a cathartic experience, but otherwise like lamenting will not get you anywhere has zero productive value.
Track 1: In a, in a strange way, it is fair in its own way, in the sense that everyone's in the same boat now, right? It's not like some people have the advantages from 2016 in 2024, 2025. They don't, everybody has the same situation. And I can tell you, Clement it's not just coding. I know because the NASDAQ and the world's largest companies are tech dominated.
Track 1: We hear about it in tech. I can tell you it's worse outside of tech, like the logistics, it's horrible out there. So it's not a tech thing. This is a global condition at the moment and things will probably pick up, but it's also a great moment. I've always found that the downturns are a great time to prepare for the next boom run because there will be.
Track 1: Yes. So polish that surfboard, wax it, get in shape and then wait for that wave and be prepared for when it comes. [00:48:00] You know, don't sit here lamenting and playing the victim card as you say, right? Like that's the only way to do it. What do you think? That surf analogy is a great one to end on. Great man. I'm glad you like it.
Track 1: Look, dude, thank you so much for your time. As always, I really enjoy. Chatting with you you know, and the perspectives you bring, you bring such a rigor of thought, Clement, to things and such a sensitive analysis. Like it's a very thoughtful, finely tuned analysis of things. With a lot of personal story and I like the way you, you threw in there that everyone thinks it's a picture perfect outcome.
Track 1: That's what it looks like on paper, but no one knows the actual pain and struggle. And I can. Promise all your listeners out there. It's true of every one of us. Like those of us who you think, Oh, they've got a picture. You're seeing the tip of the iceberg. You're not seeing what happened on the dark nights, the 3 AMs, the, the crying on the couch moments.
Track 1: You're not seeing any of that. You know, you're just seeing the highlight reel and don't think that that's the whole story. So thank you Clement, for your honesty for the incredible amount of work you've done on the algo expert platform, [00:49:00] guys, please go and check it out. What's, what's the code again?
Track 1: Clement? C.L.E.M for a discount on the platform. There you go, so that's the code. Get the discount. Go and check it out, whether you're learning to program or something else. Go and check it out there's a ton of stuff in there for you. Follow Clement on, on YouTube and on LinkedIn. Clement posts infrequently, but it's Such high quality stuff.
Track 1: Like I save almost every post that Clement puts out on LinkedIn, because it's a reminder of the reality of things, not just the fantasy of things to follow this guy. He will change your life if you let him. And, and thanks again, Clement for taking the time to be on the show.
Track 1: Appreciate all the kind words Zubin really appreciate it. Awesome, man.
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