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[00:00:00] Zubin: Hey everybody, and welcome back to The Easiest Said Than Done Podcast. And I'm so excited because not once but twice I've managed to convince Aline to get back on the show with us and talk to us about all the fantastic things she's doing now. A couple of weeks ago I had Taha on the podcast and I'm, I'm trying to bring people to the podcast that you can learn a ton of because there's so much noise in the market at the moment.
[00:00:21] But I, I can promise you this, if you follow a Aline's. Posts on LinkedIn, you will never be laboring under false solutions under misapprehensions, under all sorts of false hopes. But instead, you can just use a Lean's posts as a way to actually build your plan using data with a no BS approach, which, you know, it's what I'm all about.
[00:00:40] So I'm so excited. Aline, thank you for taking the time out and joining us again. This is gonna be epic. I'm so excited. Thank you.
[00:00:47] Aline: Thanks. What a great intro. I love it.
[00:00:49] Zubin: Well, it's, it's your posts elene. It's, it's honestly your post. There are not many people I've said, I've said that to Taha and I'm saying that to you.
[00:00:55] That's the only two people I've said that to. Really fantastic LinkedIn posts. Of course, I'll, I'll [00:01:00] link to your LinkedIn in the in the description so people can follow and, and, and Elene, why don't you. Perhaps introduce yourself. 'cause I think I will, I will gush. So I'm gonna wait till you finish and then I will gush.
[00:01:11] But tell us about a little bit about yourself, about interviewing.io and the incredible data that you've collected over a very long time that inform so many of your posts.
[00:01:21] Aline: Sure. Well, I'm an engineer. Well, at least I was, these days, I, I don't really know what I am. But I started as an engineer.
[00:01:28] I ended up falling into recruiting because I had to help hire for my team, and I noticed that it was very, very hard and much harder than I thought, frankly. So then I thought, well, this is probably hard for everybody. I'm technical. I think I can do a better job than some of the recruiters we're talking to.
[00:01:45] Let me, let me try. And I did. I was very fortunate where like my first real recruiting job was a head of talent at 150 person company. That was scary. That company was called Trial Pay. And after that I decided this is, I'm, I [00:02:00] wanna stay in this space. This, this is fascinating to me. So I ended up running recruiting at Udacity for a little while.
[00:02:05] And then I. Started my own recruiting agency and then I missed building stuff. And by that point I had a pretty clear idea of what was broken about hiring. And I thought, well, the best way to fix it is not gonna be by being a recruiter, it's by building something that actually affects change. And that's where the idea for interviewing IO came from is how can we get companies.
[00:02:31] To hire the best people despite themselves. How can we get companies to hire what they actually want, which is great people who are actually available to work instead of everybody chasing the same 10 Google engineers.
[00:02:43] Zubin: Hundred
[00:02:44] Aline: and that's what we've been working on for a decade. Interviewing io briefly is at once an anonymous mock interview platform.
INTRO
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[00:02:52] Zubin: Welcome to Easier Said Than Done with me, Zubin Pratap, where I share with you my journey from 37 year old lawyer to professional software [00:03:00] engineer
[00:03:00] Aline: and that's what we've been working on for a decade. Interviewing io briefly is at once an anonymous mock interview platform.
[00:03:08] So people use us for practice before they real interviews. And we are also a recruiting marketplace, so. Lately we haven't been doing that as much 'cause the market wasn't very good, but now things look like they're coming back, so we're kind of blowing the dust off that part of our business. Very excited about it.
[00:03:25] If you do well in mock interviews, which are all anonymous, no matter who you are, how you look on paper, we can help you find jobs at top tier companies based entirely on how you perform in interviews rather than what you look like on paper. Yeah. And, and so for those of you who do follow this podcast, listen to the first episode of Elene as well, where we go into her, what she hasn't talked about this time that she was a chef.
[00:03:47] Zubin: So she's also a career changer a few times over. And she's used all of that to sort of drive her perspective on, on the market, and I think that brings a really phenomenally fresh perspective. Now, Elene, I, I. Everybody talks about data, right? [00:04:00] But hiring ends up being very, very emotional. So, for the, for the rest of this conversation, I, I, I'd really love to focus on some of the very unique data that you've been putting out on LinkedIn because it's yours, it's proprietary, you've got it, you've got access to it, you share a ton of it in your blog.
[00:04:13] So again, people should absolutely be following the interviewing.io blog because there is Elene styles is great. Guys, for those of you listening, right, she's. She, she states her, her hypothesis, she backs it up with facts, and it's just almost like a shrug at the end, like in a mic drop, right? It's just, it's just so good.
[00:04:30] It's just fact, fact, fact, fact fact, data, data, data graph, graph, graph. And you can see that you know, on, on, on Elaine's LinkedIn sheet, she refers to herself, I think as a data skeptic. And, and it shows, it's, it, there's a lot of data, there's a lot of skeptical questions and, and really great hypothesis and confirmation you know, where appropriate.
[00:04:45] And what I also love about Alene. On LinkedIn, I've noticed several times she will lay out the data and then she'll say, keep in mind that these are the assumptions. So there are situations where might reply you knows, very honest, you view. So Alina, I love that. And, and it takes a lot of intellectual decision to do that, [00:05:00] so, so congratulations and actually being able to, yeah, I've
[00:05:02] Aline: always wanted to be one of those people that just shares opinions without data.
[00:05:06] Right. And then I realized that to do that you have to be much more famous than me. So until I'm like, you know, until I'm famous enough, I'm gonna have the data and hopefully one day I can just dispense with it entirely and just say what I think and then, and
[00:05:21] Zubin: yeah, that's right.
[00:05:22] Aline: That day is not here yet.
[00:05:24] Zubin: Well, you know, as, as long as you've got the data, I think that data is gonna bring that day much closer for you, Elene. But and, and so many of your posts have really strike a chord and I sort of try to actively engage with them for reach and stuff because it's things that I've anecdotally observed
[00:05:38] in my coaching practice as well. I mean, one of the ones that really struck a chord with me about is about the difference in performance between men and women. And it's not what people think. And I know you got a lot of flack for that. We will talk about it later. But it was something that I, I'd anecdotally observed, especially when I was at Google,
[00:05:52] i'd anecdotally observed that on the hiring side as well. And, you know, in other parts of my legal sort of, in other parts of my engineering career as well. [00:06:00] And also to be honest, in my legal career, there is a pattern there that I think you fleshed out that is uncomfortable. So, we'll, we'll definitely talk about that.
[00:06:07] But speaking of hiring I mean, so I use the interviewing IO platform too, back when I was getting ready for Google and stuff and, you know, it was phenomenally useful. So let's talk about, what do you think. You know what's on everyone's mind now is AI and hiring. What do you think the next generation of ai, or sorry, the next generation of engineering hiring for swes is starting to look like.
CTA
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[00:07:27] Zubin: What do you think the next generation of engineering hiring for swes is starting to look like.
[00:07:33] Aline: That's let's zero in a little more on what that question is asking because Sure. There's so many different aspects. We could talk about how AI is being used to find candidates, how it's being used to evaluate how AI is, reducing potentially the number of inch jobs.
[00:07:48] Which of those is most interesting to you? Is it about interview formats or, I,
[00:07:53] Zubin: I think it'll be the category three, which is what is on most people's mind is, I get this question a lot, is should I [00:08:00] look to change my career away from engineering? Should I, I I want to be an engineer? Should I bother? Is that era over?
[00:08:05] Let's start there. About the impact on actual availability of jobs.
[00:08:08] Aline: Mm-hmm. So. The only thing I feel confident about really and it's going back to data, is it looks like eng hiring is growing now in a way that it has not grown for the past over three years. So, wow. If you look back, right, things were, you know, booming in 2019, then COVID happened, everything fell off a cliff.
[00:08:34] Then there was that COVID boom recovery, which a lot of people blame for what happened next, but 2021 was crazy. Then 2022 happened, and around May of 2022, everything kind of fell off a cliff again.
[00:08:50] Zubin: Yeah,
[00:08:50] Aline: that's when. I don't remember who froze first, whether it was Google or Meta, but they froze around the same time.
[00:08:57] Then I remember Amazon opportunistically [00:09:00] swept in and they're like, all right, those two froze. We're just gonna hire all the engineers. Yeah, and then they froze too. Uh uh, and then everybody kind of followed suit, and frankly, the next three-ish years really sucked. Yeah, I know that because you can see it in our revenue graph interviewing io, right?
[00:09:19] These companies freeze and boom. Right. All of a sudden people are not practicing. Yeah. And lately, in the last, I wanna say couple of months,
[00:09:30] Zubin: yeah,
[00:09:30] Aline: it feels like there's been a comeback and. I think it's very convenient to talk about how the downturn happened because of ai, or it's very convenient to talk about how AI is bringing thing jobs back.
[00:09:45] Zubin: Mm-hmm.
[00:09:46] Aline: But I think it's something much more basic than that. I think that the fangs are the ones that kind of drive these trends. Yeah. And everybody just follows what they do and for whatever reason they're kind of hiring [00:10:00] against and now everybody else is hiring against. And of course they're all working on AI things, but I have not seen any indication that AI is taking engineering jobs except for juniors.
[00:10:13] Zubin: Yeah. I think that's, that's
[00:10:15] Aline: right. Right? You're, I'm sure you're seeing that with your students and it's, it's swift and it's brutal. But if, and, you know, we can talk about what that means when there are way fewer juniors in a few years.
[00:10:27] Zubin: Yeah.
[00:10:27] Aline: But short of that, it appears to me that whatever's happening is much more fundamental and people are trying to sort of stuff AI on top of that graph and be like this AI thing.
[00:10:37] And I don't know. I think that they're kind of stuff is happening, but I don't know that they're really all that related.
[00:10:44] Zubin: Yeah. And, and I, I will comment there that you know, the, the big tech, pretty much any company, and you'd remember this 'cause I think you know, you, you'd started your career on the, you know, the, the global financial crisis.
[00:10:55] I think, you know, whatever dominates the NASDAQ or the other stock market. [00:11:00] Whatever happens there has a chilling effect or a surge effect on the rest of the, the economy. Right? And now that it's all dominated by tech, anytime the FANG companies decide to go slow, change a policy, everybody else immediately, blindly followed a hundred percent.
[00:11:12] And, and that's what I think
[00:11:13] Aline: companies themselves are even like citing AI as the reason, but I don't even believe that either. Right. I think it's you know, when, when the 2022 downturn happened, the reason companies were were citing is, oh, we overhired and we did this. And it's like, no, you probably have been wanting to do layoffs for a while.
[00:11:30] Yes. And now that somebody else cited over hiring or the fang, now you finally have permission to do it. And if you say you overhired or you say something about the fangs, then nobody's gonna blame you. You're just doing the right. And I think it's very convenient to cite AI because nobody's gonna question you.
[00:11:46] But
[00:11:47] Zubin: so true.
[00:11:47] Aline: I don't think AI has anything to do
[00:11:49] Zubin: with it. No, and you know, you, you've absolutely hit the nail on the head. So I've been on both sides of being laid off, both laid off, and the guy sort of making or being involved in the decision and delivering the bad news. I've, I've done that a few times before my [00:12:00] tech career and I can tell you, even outside of tech, it's the same thing.
[00:12:04] You, you are spot. The roles that often unless it's a, it's a part of the business that's leaking money, it's a cost center and absolutely, you know, hemorrhaging money. If it's not that, and it's a part of the business that was stable but then has now got layoffs, it's usually because that role has been trending towards being redundant for a few years.
[00:12:23] They need an event to make is an excuse because nobody wants to get rid of people. But when there's a really good commercial reason to do so, they tighten the belt.
[00:12:32] Aline: Yeah. Let's stuff. Every layoff we've wanted to do for the last five years into this box, package it up, call it ai. Right. And exactly.
[00:12:40] Zubin: Exactly.
[00:12:40] So you know, it was depend a hundred percent. And so I think you're absolutely right. I think that becomes the, the label that they put in it, the convenient hand wavy excuse that seems plausible in the moment, but it typically happens to either seriously underperforming units or highly expensive units, or.
[00:12:55] Units that two to three years ago were kind of trending down anyway, that, you know, the writing [00:13:00] was on the wall. It's just that nobody wanted to, you know, as long as revenue is good or margins are good, people will keep them around because it's uncomfortable and legally expensive to get rid of them. You know, that's, that's much more mundane, sort of prosaic reasons for for this at, at the end of the day.
[00:13:14] And I will say that, you know, absolutely it appears to be across all industries. I think PBS and a couple of other, and. I wanna say Bloomberg and others had reported that juniors in all industries, basically grads in all industries, are absolutely suffering at the moment. And I think that's true across the board.
[00:13:31] I will, it, it seems to be anecdotally that the top 20% will still continue to get opportunities. I'm seeing that I think five out of my students and I only work with like 15 a year or something. Currently, you know, interviewing two of them just got roles. It, it will happen, but there is definitely.
[00:13:47] A down market. Now there is no question about it. And I think hires are, you know, on the hiring side, they're quite jittery. They're not actually entirely, they don't have the confidence. Even if they have the need, they may not have the confidence to hire right now 'cause they may be keeping their powder [00:14:00] dry for it for a downturn.
[00:14:01] All of this is just exactly how we'd budget at home. It's no different, you know, when, when prices go up, we'll stock up on a few things and we'll tighten the belt on other things and we will be nervous about spending an expensive holidays and it's. Same thing in a company. It's a budget. Is a budget is a budget, right.
[00:14:14] So, I, I think you're spot on there. One of the posts that you mentioned, and I'm gonna sort of read here the relevant bits of it. You said, you know, DSA questions are here to stay, but interview questions are changing. Instead of asking questions that elements. Can easily solve. Companies are moving to custom questions that require thought and understanding.
[00:14:34] Tell us more about that because that's it's, it's a very important take. It's very nuanced. And not many people fully understand what that means unless they've been on the hiring side and this. See the challenge, you have to be in the, in the shoes of the hira to understand why that distinction between something that an LLM can solve versus something that shows human thought in January and application of mind.
[00:14:54] Talk us, talk us through what you're seeing on the data there.
[00:14:57] Aline: Yeah. Well I think there's been a lot [00:15:00] of speculation about how AI is going to change interview processes, kind of the other side of this question, right? And. There are all these headlines all the time being like the, the most egregious one that I saw was I think in Fortune, and they said, anthropic is now letting people use AI in interviews.
[00:15:18] And then if you read the piece, all Anthropic said was, oh, you can use AI to like ask questions about anthropic. Like what it's like to work here or you can use AI to adjust your resume. Do not use AI during interviews. And they have
[00:15:33] Zubin: a blog on this. Yeah, I've seen it. Yeah. Yeah.
[00:15:35] Aline: And, and this most egregious example, but there's so much stuff like metas letting people use AI and I got sick of it.
[00:15:42] So, you know, fortunately on interviewing IO we have a lot of primary sources. We have thousands of interviewers who contract for us who work at these companies. So I just sent out a survey and I'm like, Hey guys, what are you actually seeing? Yeah, and we wrote up the results.
[00:15:58] And one [00:16:00] of the biggest takeaways for me was that indeed, at least at the fangs and the fang adjacent companies where we had responses, I think we had people from DoorDash, Stripe, Uber glassy, and a few, a few other companies, just, you know, of, of that caliber.
[00:16:16] Yeah. None of them are getting rid of algorithmic questions.
[00:16:20] Zubin: Yeah.
[00:16:20] Aline: And I'll talk about how the interviews are changing despite that, but the only company that it was adding anything AI assisted as of when we published this a few weeks ago was Meta. Mm-hmm. And they're not replacing their algorithmic screen.
[00:16:34] It looks like they are adding an AI assisted round to their onsite.
[00:16:39]
[00:16:39] Aline: This is also, you know, talking about how is AI changing engineering work and headcount. If AI proficiency were so important to these companies and if AI truly were writing so much of the code and and working with it, you'd think this would be a bigger priority.
[00:16:55] Mm-hmm. Right? Yeah. And it's not like nobody is changing their process except meta. And, you [00:17:00] know, meta maybe is the leader and others will follow suit once they do what they're gonna do. And I think they're adding this round either this quarter or next quarter. I think they're experimenting with it internally right now.
[00:17:10] Yeah. But Okay. Algorithmic questions not going anywhere, but the nature of the questions is changing. Yeah. I think last year we did a study in interviewing io where we tried to see how easy it was to cheat if you used an LLM. Yeah. And one of the most surprising things that we saw is that if companies asked verbatim lead code questions or lead code questions with a small twist.
[00:17:36] Yeah, then it was ridiculously easy to cheat. Sure. Interviewers couldn't tell. I mean, on our platform everything's anonymous so there's no camera. So I guess like you can't track eye movements. Right. But still people couldn't tell. And only when companies asked fairly custom questions, namely ones that are not found on lead code.
[00:17:56] Yeah, then it was hard to cheat because either the [00:18:00] LLM would give the wrong answer or it just wasn't that useful. Yeah. And I, we kind of predicted that out of necessity, this is where interview questions would go. Yeah. That doesn't mean that they're not algorithmic, but it means that they can't be ripped straight from lead code.
[00:18:15] Yeah. Meta is probably the most egregious example where I think you can find pretty much every meta question on a lead code list. And you can just decide that you're gonna work through them and get fast at them, and then you will pass your coding rounds. So that doesn't really work anymore. So I think companies have to come up with their own questions.
[00:18:36] They can't just be a little twist to an existing question that's out there. It has to be a little more original. But from what we're seeing, even if these questions are original, they still have an algorithmic kernel inside.
[00:18:47] Zubin: Yes, a hundred percent. Yeah.
[00:18:49] Aline: Yeah, and
[00:18:50] Zubin: I, I actually, sorry, just to jump in there, Ling, I, I actually noticed that as well.
[00:18:53] So when I was at Google there was an entire a reported, you know, sort of, disallow list of questions mm-hmm. Of [00:19:00] things that had been linked. That had linked right, correct. And engineers would come up with new questions ahead of interviews and debate. On this platform internally to say, hang on, is this too close to something that's already been leaked?
[00:19:11] And they would come up with real life scenarios map. And as you said, there's always a pattern at the heart of it. At the, at the kernel there, it's a category of DSA problem. Right? Yeah. And at the kernel, and they wrap a real life sit situation around that. And a lot of the chap, the, a lot of the debate and the discussion would be on the conversation pathways that would emanate from the approach.
[00:19:31] Mm-hmm. Right? And that's where a lot of the debate happened is, well, what are we trying to test for? Is. Not just that this person can recognize the pattern and they know the, you know, the principles of a tree or a graph or whatever, but they're asking the right questions, going down the right pathways, ideally touching on all of them and eliminating the ones because that conversational analysis is what a lot of the signal came from.
[00:19:51] And, and so I don't think, I don't see how AI would change any of that because it's not about brute forcing a solution and getting something in, in three minutes. In [00:20:00] fact, candidates that typically finish the interview problem in like 15 minutes. Left you kind of wanting, you're like, that was un before.
[00:20:07] Yeah. Yeah. It was, it, it is completely unsatisfying and, and an antis signal, you know? So, yeah. So, sorry. I, I did interrupt. Well,
[00:20:14] Aline: that's actually a, that's a great segue into the other thing we learned, which is that not only are questions changing, but interviewer behavior during interviews is changing as well.
[00:20:24] Interviewers across the board were saying that now they have to be. I mean, ideally they would've been really engaged anyway, but now they have to be a lot more engaged. They have to ask why. Right. Change up constraints and, you know, turn it into a discussion between hopefully two smart people about approaches and that.
[00:20:45] I mean, the only way companies change their behavior is because they have to, there has to be some kind of external forcing function. Either you're running out of money or something else, or you know, you're losing signal and, and I think LLMs [00:21:00] are that forcing function. It's like, alright, finally we have to invest a little bit.
[00:21:05] And I don't, I haven't seen this happen yet, but I'm predicting that companies will be investing a lot more in interviewer training. Setting up good interviewer incentives. You know, those most companies don't reward people for doing interviews. You just have to do them. They're not tracking which interviewers have predictive power.
[00:21:22] They're not tracking which interviewers create good candidate experience. Some companies do but most do not.
[00:21:29] Zubin: No, don't do.
[00:21:30] Aline: I think because now the interviewer has to be able to ask follow up questions, vary up constraints, do all of these things that hopefully they would've been doing anyway. I think interviews are gonna move more toward, yeah.
[00:21:40] We're still gonna have to like write code because that's the job, but it's not just gonna be, Hey, did you memorize a bunch of questions? Correct. It's gonna be, can these two people solve a hard problem together and collaborate well while they're doing it?
[00:21:52] Zubin: Yeah, and I mean, we use ai. I use AI at, at work. You know, I've got a team and we all use AI and we use it as an accelerant.
[00:21:59] But we have [00:22:00] to craft, aI usage very carefully, you know, very, very carefully, very intentionally. And we have to immediately spot when it's going down the wrong path. We have to know when to challenge it. You know, and often we just have to hand type things or at least, you know, steer it.
[00:22:13] There's a lot of steering that's required for, for effective and safe AI use. And all it does is it saves you the keystrokes. It does. Save you the thinking. It does not require you to not know things. Literally all it's saving is the keystrokes and some research, but you have to know what to research.
[00:22:27] It's no different from looking at things in stack overflow and copying and pasting, but blind copying and pasting is dangerous. It's the same thing with ai, you know, blindly doing. It's very, very dangerous. And now on, on on I know you guys at at interviewing, I know you've shipped the AI interviewer and you're shipping.
[00:22:42] I, I believe you're shipping, design, interviews suit. So tell us more about that.
[00:22:45] Aline: It's a beta version of that live right now. So we've had algorithmic AI interviews out in the wild for about a year. Yeah. And I think it was last week we shipped system design. Yeah, so play with it. If, if you have time with, I'd love to see what you think.
[00:22:59] [00:23:00] It's, it's supposed to feel just like a real interview where there's ex sc draw and you're talking, the AI is not talking back. You're talking to it and then it's typing back. Yeah. The audio is a little, little rough right now where we, we need to make some of that better, but it can tell what you're drawing and it's gonna ask you the kinds of system design questions.
[00:23:19] You'd see a thing.
[00:23:20] Zubin: Phenomenal. So that, and that's just, you know, just showing how AI is just a new interface for things, but the underlying skills are, you know, exactly the same. And it's once it's interactive and if it's intelligently interactive, whether it's a human being, whether it's a, a motivated interviewer or, or a well-trained ai the experience is, it's.
[00:23:37] Got to get down to the bottom of, can you do this job reliably? You know, and do you know enough to make the difficult judgment calls involved in engineering? That's great. That's really amazing. So, you know, good on you for constantly sort of innovating and updating things. That's really great. I'll, I'll put a link in the show notes as well and I'll, I'll definitely, and it's free,
[00:23:53] Aline: like I, everybody should try it and then yell at us about what we should do better.
[00:23:57] Zubin: Perfect. All right, well that link's definitely going in [00:24:00] now. One of the other, now I want to get to the controversial one and then I, I will get to the question on burnout as well. 'cause I think a lot of people. You know, feel, feel something akin to burnout and I think your views on that would be very valuable to them.
[00:24:11] But before we get to that one of the things you did mention was that only about 25, I think this was a post maybe a week, week and a half ago. Again, I'll, I'll put a
[00:24:19] screenshot and I'll link to it and all of that. But one of the posts was that women tend to fail technical technical interviews far more often than men.
[00:24:27] And I'm, I'm quoting a bit now here. You said we have the data, but the reason is not what you think. It's not bias against women and it's not the women I was coders most importantly. Right. So, tell us more about that. I don't want to give away too much because I found that that was just, it was like a drink of cool water on a warm day.
[00:24:43] It was just beautiful that post. So tell us about it.
[00:24:46] Aline: It's so nice of you to say so many years ago we built. Actually have a patent on this. It's our only patent on real time voice modulation. Yeah, so this was 2016. People were just [00:25:00] starting to talk about bias and you know, diversity was just becoming this like hot button topic.
[00:25:06] So we were very curious about what would happen if we could just take gender off the table in technical interviews and make women sound like men and men sound like women. And. Just see. So, before we did this, we did notice, as you said, that at least on interviewing io women were performing significantly worse than men, and we wanted to know why.
[00:25:30] And after we built voice modulation and turned it on and ran an experiment, we saw that when women sounded like men, they were not doing any better. When men sounded like women, I think we. I don't remember, did we make everybody sound like men? But maybe in some early experiments we made women sound like men.
[00:25:48] And then I think in the big experiment we just made everybody sound like men. But there was no difference. Like, no matter how people sounded, they tended to get pretty similar scores. Yeah. So that did not account for why [00:26:00] women were performing worse. So then we dug into the data a little bit more and we saw a really, really surprising thing.
[00:26:07] So people come to interviewing io to practice. Mm-hmm. But not everybody does a lot of practice. Some people do one interview and then they just leave. Yeah. And what was startling is that women, after a failed interview, were leaving seven times more often than men. And then seven. Seven, is that 600, 700?
[00:26:28] Is it?
[00:26:29] Zubin: Oh, because more so. Yeah. 600% more.
[00:26:32] Aline: Yeah. Yeah.
[00:26:33] Zubin: Wow.
[00:26:33] Aline: Yeah. It was wild. Right? And then if they stuck around and then they failed another interview, I think they, women after that second interview were still leaving like two or three x more often than men. So after two interviews, you know, like a ton of the women had just left.
[00:26:51] So then we said, okay, well, let's correct for people of both genders leaving and just look at people who didn't leave after. Mm-hmm. Poor [00:27:00] performances. And then the performance disparity went away in terms. Right. And that's, I mean, there's a meta principle for life there, which is not great. You know, the more, the more shots you take on gold, the more likely you're gonna get on them.
[00:27:11] Zubin: Like, we, we know all these things, but I think, I mean, I, you know, I, I can't speak for women, but is this a cultural thing? Is this a, a biological thing, a mix of all these? Like, if you were to speculate, and I know it's speculation, you know, I mean,
[00:27:24] Aline: one of our engineers at the time was like, well, I mean, surely.
[00:27:30] Like you've seen what happens in dating, right? Where, you know, men are, are rewarded for trying and trying and trying again. And they're generally, you know, at least in straight couples, they're the ones that, that are the ones that initiate.
[00:27:45] Zubin: Yeah. And they're the
[00:27:45] Aline: ones that get rejected and then they just pick themselves up.
[00:27:48] I, I don't know. Right. If I had to speculate, I'd say maybe there's some biological or social thing there. Right. If you get socialized that rejection is just part [00:28:00] of life. Yeah. And then you just pick yourself up and you keep going. Maybe if it's in, in a different domain, when you get to this domain, maybe it's a little bit easier.
[00:28:08] I don't know if it's entirely about that. And I, I can't even begin to speculate about whether there's a biological component to this or this is just how. Women versus men are socialized in our society. But yeah, for whatever reason, and maybe it's both, right, but for whatever reason, it's there. I, one thing that I really want to do that we have not had the opportunity to do is to do a fairly simple experiment where we just do an intervention and you know, if we say, Hey, you know what, we actually.
[00:28:37] We did, we added something. We didn't do this in a controlled way, but after we ran this experiment, we did add an email that goes out to people and if they fail their first interview, we just say, Hey, look like only 25% of people perform consistently. Like this is normal. Just keep going. And I don't know what that's really done to attrition, but I hope it's done something good.
[00:28:58] Zubin: Yeah.
[00:28:59] Aline: But I think there's a [00:29:00] lot more work that can be done there, and maybe there are interventions that can happen even before you fail an interview.
[00:29:06] Zubin: Yeah, yeah. You know, this, it's, so this is something, like I said, I've observed anecdotally in previous careers, I've observed this as a manager. And I've observed this in my coaching program now with students who wanna become coders.
[00:29:19] And I know that as a man, you know, there's certain things that I'm told I cannot say assume, and that's probably fair. But as a coach, regardless of my gender, I feel like, Hey, I'm just gonna say it. You know, and it's up to them to decide what they wanna do with it. And one of the things I said, I've noticed.
[00:29:34] Consistently, especially when I'm managing people or when I'm coaching students, is women tend to worry about the consequences much or they seem, seem to experience anxiety over the consequences of. Negative perception. Mm-hmm. Much more acutely than men do. They, they really like, even for salary negotiations, right?
[00:29:52] Mm-hmm. They quite literally, often me, mechanistically, it appears to be they ask for less, they ask Morely or they don't ask at [00:30:00] all. You know, and then they get whatever they've asked for. If they've asked for zero, they get zero. If they've asked for 10, they get 10. They, they ask for 50, they'll probably get closer to 50.
[00:30:08] But there's. An energetic kinda difference in the way they approach it. And men seem to have, perhaps it's a sense of entitle, who knows what it is, but they will be like, okay, I'll give it a shot, I'll give it a crack. You know, and if it doesn't work out, sure I'm gonna feel bad, but, you know, okay, I'm gonna, I, I want to go for this.
[00:30:22] And I don't know whether there's. In an evolutionary reason, like, you know, for perceptions matter, evolutionary, you know, and, and men have to, you know, do slightly more risky things because of, of the physicality of the male versus the, who knows. There could be any other explanations you know, that, that drive these behaviors.
[00:30:38] But the one thing that I found kind of works, and again, this is why I love your data. 'cause I work with so few people, the data is not meaningful, but it's meaningful for me emotionally and psychologically, which is what I tell my students is. If you find information in your experience of things that don't work, the best way for success is to do the opposite.
[00:30:57] Right, like literally that's inversion thinking what Charlie Munger talks about. [00:31:00] And so for all the listeners out there, especially if you, regardless if you are, you know, of whether you're male or female or anything else, if you identify with what Elene is talking here about, you're likely to quit after the first rejection.
[00:31:11] Literally, the secret to this appears to be do the opposite, which is. Don't quit after the first rejection. Don't quit after the second rejection. Do the opposite of the impulse. And that itself will unlock opportunities because I think lean's data is pretty clear that literally the more flips of the coin you do.
[00:31:27] You're gonna get it. And, and you've mentioned something really interesting, which is also one of the posts I wanted to talk about,
[00:31:31] which is only 25% of people do consistently anyway, which, I'll put it another way. 75% of candidates perform inconsistently and years of experience had no relevance. You know, and there's, there's a, there's a diagram and a post, and I'll link to all of that.
[00:31:45] So, okay, so let's break that down. 75%, three outta four people will do inconsistently. Even if they're good, they will do inconsistently, right? What does that mean?
[00:31:55] Aline: A few things a few things kind of tie into it. You mentioned seniority a moment ago, [00:32:00] right?
[00:32:00] We actually have data that says that juniors perform better than seniors At the beginning I've seen it not that surprising, right?
[00:32:06] If you've recently had an algorithms class versus, you know, you've been working for five years and not really doing this kind of stuff, yeah, you will do better. Things start to even out. I think at around five interviews, there seems to be something magic about the number five. People's performance tends to get more consistent across different groups.
[00:32:23] But also after five interviews, your odds of passing real interviews double. And after that, you know, things start to level off a little bit. But most of the growth happens in about five mocks. But that's, you know, that's a lot of interviews, right? If, if you wanna quit after the first one you don't quit, then there's the second and the third, and the fourth and the fifth.
[00:32:43] Then maybe it starts to feel better at some point. There's, there's really something magical about the number five, at least in the data. Yeah. So maybe one takeaway is, okay, maybe if you really wanna quit, maybe you quit after five, but tell yourself you're gonna do at least five and then see how you feel.
[00:32:57] Zubin: Can I just jump in there? I didn't [00:33:00] know about the magic number five until we just spoke. But I wish I could show you the recordings in my, in my coaching program for this bit where I tell them, I'm like, if you have failed less than five interviews, I'm not gonna have enough information on what you need to do better.
[00:33:13] Right. So I tell them, I said, your goal is to aim for two to three interviews per month. Now, whatever needs to be done to achieve that. I know that's really ambitious, but we are not talking about FANG here, right? Yeah. Like most of my students aren't necessarily interested in fang. I'm like, it doesn't matter what kind of interview it is, as long as it's a coding role.
[00:33:28] Do it. You don't have to accept the offer, just do it to learn until you get to five. It's there in the recordings. I'm like, I can't actually analyze what's going on. Anything on one day could change the data, right? So at least get to, so it's fantastic that I know it's a different use of five that I still think it's a magic number in terms of getting enough data and for you to learn.
[00:33:50] The pattern yourself. There is a pattern to this. There is a, a, a rhythm that you can feel to interviewing and how the interview's going. And so on the you know, perform [00:34:00] inconsistently. When we talk about consistently or inconsistently in this context, what do we mean? Mm-hmm. Is it interview behavior?
[00:34:07] Aline: Well, we have a rubric. That we use, and we've used the same rubric from the beginning where people are evaluated on their technical ability. So in an algorithmic interview, that's your ability to write code? Yeah. Your problem solving ability and your communication ability. All of those are in a scale from one to four.
[00:34:27] In general. We also ask interviewers, you know, thumbs up or thumbs down. Mm-hmm. Put very simply in our data consistency is you generally get fours or you generally get one, so you generally get twos. Mm-hmm. And we define consistent as a standard deviation of less than one half. Okay. Across that set.
[00:34:45] I think intuitively should track so only 25% of people have a standard deviation in that rubric of less than one half.
[00:34:55] Zubin: Yeah. Yeah. Okay. That makes a lot of sense. And I think for folks who are listening to this and are struggling to [00:35:00] understand why this would be the case again, once you guys get to a hiring stage, you'll start to see how there's a dynamic in every room.
[00:35:08] And if there's just two people, there's a certain dynamic, that dynamic that there's a way they play off each other. You know, little things at the start can set off butterfly effects downstream in the interview. And it's such a dynamic environment that there's so many variables that you know. So many variables in an interview that it's hard for you to understand what it's like until you've actually been on the hiring side as well.
[00:35:30] On the candidate side, you'll have your perspective and then when you start to see from the hiring side, you see just how complex an environment that is, which explains, in my view a lot of that inconsistency. 'cause it has nothing to do with the individual. Or not always, but you know, quite often it has to do with circumstances and, and the dynamics that get set off in play.
[00:35:46] There's a domino effect of things that can happen that can cause psychological shifts on either side of the table, both sides of the table. Mood shifts on either side of the table. Both sides of the table and human beings are very flawed creatures. There's a, a brilliant study that I think Diana really did that [00:36:00] showed that, you know, a a, an interview with a warm drink and a hand is.
[00:36:03] More favorable to candidates than an interview with a cold drink in the hand. Like we are that idiotic, you know, as a species. Yeah.
[00:36:09] Aline: I, I have to look at that one. I have not seen that one, one thing here's something that's maybe a little more believable or like intuitively believable. If an interviewer is good mm-hmm.
[00:36:18] Their candidates are more likely to do well not because they're lenient Yeah. But because they are engaged. Yeah. And I think we talked earlier about how. LLMs and cheating are forcing function for interviewers to get better. And all that chaos and all those variables and the interpersonal dynamics and did something clicked, did something not click.
[00:36:39] A how are you feeling? Did you eat breakfast? I think a good interviewer can like dampen those highs a little bit. Like that is their job. Their job is to reduce as much of that noise.
[00:36:52] Zubin: 1000%. Yeah. One. And one of the things that, you know, when I was, when I interview and when I sort of talk to my team about interviewing is I'm like, you [00:37:00] know, a lot of our job is managing the energy in the room, whether it's virtual or physical, it's managing that state and, and, and interacting with them in a way that makes them feel comfortable, warm, engaged, you know, engaged with the topic and enthusiastic about the next step, regardless of how they did in the previous you know, is to create that warmth and that energy.
[00:37:16] And it's, it's a hard thing to do. You've gotta be very tuned into the room to do it, and you've gotta be quite empathetic to do it right. You know, and you've gotta not have your calendar on your mind and your next call and your mind, like you have to be present. It's hard you know for sure. Now, on that topic,
[00:37:28] one of the other posts again, gosh, your post is so good, El first impressions, predictive early performance is.
[00:37:35] You know, more or less by the 13 minute mark and over half of interviewers have already made up their minds by more or less that point. I, I, I've guys, I've, I've extracted the key salient points from the post. There's much more nuance to the post,
[00:37:47] but tell us about that Aline. 'cause this is, is quite important, you know?
[00:37:51] Aline: Yeah. So one of the other things, we we're very fortunate that we do have a lot of data.
[00:37:55] And one of the things we do is during interviews. We'll periodically check in [00:38:00] with the interviewer and just say, Hey, just quick gut check, how is this candidate doing? And I think we do one at the five minute mark, one at the 15 minute mark.
[00:38:08] And then at any time, an interviewer can also react to something a candidate is doing. The candidate won't see it during the interview because that would be really distracting, but when they watch the replay of the interview later. It's kind of like Facebook Live where it's like, here, right here you did something awesome, you know, hearts and or here, you know, I shouldn't have done this.
[00:38:29] This is where everything went wrong from here. And then you dug yourself in a hole. How useful.
[00:38:34] Zubin: Fantastic. Yeah.
[00:38:35] Aline: Cool. Right? And we, we put that in a while ago and we didn't really do anything with that data until I started writing Beyond cracking the coding interview. I'm like, it's been 10 years. I bet we have enough stuff here to like.
[00:38:46] Say something. And we saw that that, you know, interviewers do tend to make up their minds fairly quickly. I think usually even in anonymous mock interviews, the first few minutes are chitchat. We have not seen that chit chat be [00:39:00] particularly predictive of anything. So you take away, you know, a few minutes of chitchat.
[00:39:05] So that 13 minutes maybe is really like 10 or eight minutes. And. You know, we looked at what, how they reacted to the candidate and those reactions. The funny thing is for both positive and negative sentiments, like if they, if somebody really likes an interviewee originally or if they really don't like an interviewee, over time, sentiment just drops no matter what.
[00:39:25] Like, you, you start up here and it drops. It's just that it may not drop enough to flip your opinion of the person. But it, it does keep dropping throughout the interview, but people sentiments at roughly the 13 minute mark most of the time tend to be the same as the interview outcome.
[00:39:43] Zubin: Fascinating.
[00:39:44] And, and do you have any any data or any sort of hypothesis around the likability of a candidate? And people sort of assume that this is a, or it's some sort of favoritism. It's not being likable is, is just a, a trait, you know, it's, it's a dynamic Is, does, is [00:40:00] that present at all?
[00:40:00] Aline: It's really hard to tease these things apart.
[00:40:02] Our, our best attempt is these sort of reactions in the moment, have different flavors. So you can react to a candidate's technical ability. You can react to their problem solving ability. You can react to their communication ability. Now, are we good as humans at differentiating what we're reacting to in the moment?
[00:40:19] I don't know, but technical sentiments tended to be much more predictive of outcome than communication.
[00:40:26] Zubin: Okay.
[00:40:27] Aline: Sentence. So if you think, oh, that was a great story. They just told me that. At least in interviewer's minds that Yeah. Doesn't map.
[00:40:36] Zubin: And again, this is very much for technical interviews. There are multiple rounds in the loop and at other rounds in the loop.
[00:40:42] Other things will probably, like, I think in behavioral interviews, I wouldn't be surprised if the right way of telling the story matters a lot more than getting too technical in the details. You know, which could lose some interviews or if you're, you know, doing a partner interview with a, with an adjacency team and they're not very technical, you don't want to get, you know, two, two step.
[00:40:58] Of course, this is
[00:40:59] Aline: just technical [00:41:00] interviews, but that's kind of the nice thing is that, you know, at least. There's some semblance of objectivity here. Yeah, we did see that for interviews below staff level, the communication score like does not matter. You can have a pretty, it shouldn't be horrible, but you can have a pretty bad one and you, you'll, you'll still pass as long as you're technical is really good, but a middling technical.
[00:41:21] Probably not gonna pass.
[00:41:23] Zubin: Yeah. And I think that's another thing a lot of people struggle with over the course of their careers is they over optimize and one, and forget about the others of what it takes to go up the ladder a bit in, in their careers. Now I know I'm, I'm very mindful of your time, so two last quick questions and I'm gonna let you go.
[00:41:37] One is you know, again, you had a great post, and I've seen this in multiple countries in previous careers as a lawyer. I see it now as an engineer, et cetera. Most places don't give candidates feedback.
[00:41:47] Right. And there's a blog that you've got about this that I'll link to it as well about, you know, constructive post interview feedback.
[00:41:53] It's a bug bear for people. They're like, you know, I put all this effort, I pull all this work, all I'm asking for is some feedback so I know what to do [00:42:00] better. Which is a fair question. And the usual answer is for legal reasons or something,
[00:42:04] but there's no evidence that there is such a thing to worry about.
[00:42:07] What do you think is actually going on here, Aline?
[00:42:09] Aline: I think hR departments tend to be very risk averse and they will make rules. I mean, you've been a lawyer, right? When you red line a contract, you're not thinking, you know, let's only worry about things that are likely to happen. You're gonna red line the hell out of that thing because if there's a, you know, thousandth of a percent chance that something catastrophic could happen, you gotta cover your act.
[00:42:30] That's it. That's a very different way. And you know, my favorite lawyers have been the ones who are like, you know what? Let's just not worry about it. This is, let's do this one thing and let's do the deal. Yeah. Exactly.
[00:42:40] Zubin: Yeah.
[00:42:41] Aline: And that, those tend, like, ironically, those lawyers that have that attitude in my experience, tend to be like the most experienced people because they're, they're so confident in their risk assessment that they can be a little bit more flippant.
[00:42:54] Zubin: Yeah. So, and, and they tend to have the confidence to tell, 'cause I used to, I used to get into trouble with some bosses, but [00:43:00] I, I'd tell my client, I'd be like, okay, there are two things that you need to consider the likelihood and the impact. Mm-hmm. Okay. If you multiply those two and it's terrible and unbearable, don't do it.
[00:43:09] Yeah. But if it, it's simple math. If the likelihood is very low and the magnitude is big, then you have to do the math and figure it out. But most things, the likelihood are low and the magnitude mag and the magnitude's pretty low. Don't sweat that stuff. Yeah. You know, and I'd get into trouble for saying that I'd get into so much trouble for saying that because you, my job go that, you know, is to just say this is the risk and not make a, not not have an opinion on it.
[00:43:31] You know? So, but anyway,
[00:43:32] Aline: but I think that's exactly why, right? Is because people are covering their ass. But another practical reason why companies don't like to give feedback is the worry that candidates are gonna get defensive. Nobody wants to be on the other end of that because you think you're, do they ask you to do them a favor?
[00:43:47] You're like, okay, you know what? I'm not supposed to do this, but I'm gonna go out on a limb for you and I'm gonna tell you some stuff, and then you get yelled at. And I've, I've had that happen to me.
[00:43:57] Zubin: Yeah. I, it's happened to my, my HR business fund as well. Yeah. [00:44:00] Yeah.
[00:44:01] Aline: When I was a trial pay and I was running recruiting there I used to, you know, do this thing where I'd break the rules sometimes, and I would get on the phone with a candidate.
[00:44:08] I'm like, look, I'm not supposed to put this in an email. But let me just tell you, and maybe like one in 10 times they would just like argue with me or it was really unpleasant. Yeah. So what I learned from that and, and some of these experiences led to me starting interviewing Iowa, is this idea, like feedback is so important.
[00:44:28] How can you deliver harsh feedback without people getting defensive? And we had to iterate on that. Like we, now we, we don't really train our interviewers how to interview because. We're recruiting from top tier companies, we're only taking people who've done a ton of interviews in the wild. They know how to do it.
[00:44:46] Yeah. But what they don't know how to do is to deliver feedback because their companies don't encourage it. So yeah, the only real training we do is, hey, let's say you have to deliver harsh feedback to somebody. How do you do it in a way where you leave them better [00:45:00] than when you found them and where you don't get in an argument.
[00:45:01] Zubin: And so valuable elene, like, so life-changing, valuable for, for candidates. It's just so important. I can't, I can't state enough just how important. Interviewing IO is for people. And this is not a shill, this is a heartfelt thing. Like it is the missing piece. It is many missing pieces of the puzzle that you otherwise just would not have.
[00:45:21] Like I know what the world is without that. 'cause most industries don't have this. The fact that tech has interviewing.io is a huge bonus. It's a huge bonus. So, you know, that's fantastic. Now, last question before I let you go. I want to ask you an email just randomly and flippantly so you know.
[00:45:36] 'cause you've been working so hard to build engineering and I'm like, Celine, you know, what do you feel about burnout? Do you think, you know, do you ever feel burnt out? And your answer, and I'm gonna quote is, do I feel burnt out? Not really. I think burnout is more of a function of feeling like you don't have agency, you are stuck.
[00:45:53] And I was like, boom, 1000%. Tell people a little bit about that mentality and that that [00:46:00] idea, because I think it's hugely liberating for people to realize what Bernard is and what it's not.
[00:46:05] Aline: Yeah. I don't know when people started writing about burnout on the internet so much, I think it became a trendy topic a few years ago, and I think there have been several like, waves of burnout conversation.
[00:46:18] And I think we're now in this wave where I think like most people will agree what I said with what I said. I don't think it's that controversial, but I think the first wave was more about, oh, you know, you're, you're getting taken advantage of. You're working too many hours, you don't have balance. I don't know what balance is.
[00:46:35] I think life is about doing what you, in as much as possible. Just life is about having control of your time, right? That's, yes, that's what life is. And it doesn't matter what you do with that time, I guess, as long as you're not, you know, a killer or something. But you know, it's, it having the agency to spend your time on what you want.
[00:46:54] And that's a huge privilege. Because
[00:46:56] Zubin: huge privilege. Yeah.
[00:46:57] Aline: Time is a gift. You know, many of us don't [00:47:00] have all the time we want, 'cause we've gotta support our family. We gotta, you know, do all, all this other stuff. Yeah. But the ultimate win is just being in control of how you spend your time. And I, I believe that if you're doing something that is fulfilling for you, you can work a lot.
[00:47:16] I mean, there is the kind of burnout where like you're sleeping under your desk and you, you know. You can't not sleep for, for many days in a row, you will be like, literally burnt out or you'll go insane or you'll die. Yes. But I think that the reason many people feel burnout is because they're not doing things that are consistent with what they want.
[00:47:34] Mm-hmm. Or they're being thwarted by a bad boss or by coworkers or, you know, they, they feel like they don't matter or their work doesn't matter, or, they know what needs to get done, but the inertia of doing it is like too great where, you know, you just can't fight the system. The system's gonna win. It's hard as a founder, I'm very fortunate that, you know, I'm in charge, right?
[00:47:58] And sometimes I [00:48:00] still feel that. Where there's some kind of, I'm like, oh my God, what have I done? Like our company has a bureaucratic thing. Why do I feel this bureaucratic pressure? I'm in charge. Yes. And then I'm like, okay, there's something wrong here. I did something wrong. I have to dismantle whatever made me feel like this.
[00:48:13] Because if I'm feeling that this, my employees are feeling this a hundred times more because they're not in charge. But I think that's, that's really why and I don't know if I could just give anybody a gift. It would just be the gift of, of time and agency
[00:48:27] Zubin: time and age. Oh my God. And. I could not agree with you.
[00:48:30] Let me, let me just share two small stories so that people can contextualize how powerful it is. It is a place of privilege that, you know, it, it comes from, but I'll, I'll give people the example I give my students. 'cause a lot of them will come to me. I'm feeling burnt out and I'm like, first and foremost, there's a difference between being really tired and being burnt out.
[00:48:46] Mm-hmm. Tired is a very, a physical thing and it can be a mental thing as well. But burnout is very much an emotional and psychological thing. And and the example I give them is when I was a lawyer I used to work really long hours. [00:49:00] 'cause you know, lawyers are famous for doing time billing. Yeah. Yeah.
[00:49:03] Yeah, it's really long hours. I work more now elene between my work and the coaching and just my own, you know, things that I like to do. I work so much more now on average per week, so much more I have, so I don't take holidays. I'm so, but I am and I'm what, 15 years older than when I started, or, you know, 20 years older than when I started.
[00:49:22] I am less burnt out today than I was 10 years ago. Far less. Right. And because of that alignment, like you said, that it's because I'm doing, I'm aligning with what I want to do and I'm able. To do more of what I want to do. But that didn't happen overnight. That took, you know, I had to be I until 38. I lived with that friction.
[00:49:41] And then the third year I said, enough, I want to do what I want to do. And it's taken all this time to get to that point, right? So it wasn't like an overnight thing, but the heart of it appears to be friction. The more friction there is in your environment, in your life, from the things that you naturally want to do, or you seem to, you know, be impelled towards and everyone's different the more you're gonna experience, because friction [00:50:00] does produce burnout.
[00:50:01] It does. So in an engine, in a, in a piston, it's gonna do so in a human being as well, you know? And the second thing is so many students come to, and, and we completely burnt out from trying to learn to code, and that's why we are joining your program. And then I give them the minimum number of things and I take away all the options.
[00:50:16] Curate the plan. I says, just do this. Just do this a few hours a day, and you should see them come alive. They've got all their energy back. They're better with their family. They're better with their spouses because all the friction, the mental friction and load of what do I do? What do I not do? Am I doing the right thing?
[00:50:29] Am I not doing the right thing? Why am I not getting results? All of that's gone. You know, you're not battling yourself all the time. You're just doing the thing you were meant to do. And that release, that lack of friction just helps people get so much energy back.
[00:50:41] Aline: That's like the, yeah, just do five interviews.
[00:50:43] Like don't, don't suspend disbelief. I remember when I started my recruiting business and you know, this is some of the best advice I ever got, it was from my dad. You know, I had left a pretty good coding job and I was just kind of sitting around and nothing was happening. [00:51:00] And he was like, look, give yourself a deadline.
[00:51:02] Say, you know what, if I don't make any money or I don't make a placement after three months. Mm-hmm. Then maybe I'll consider going back to engineering, but for the moment just put that out of your mind and just do the work. Shut up. Yeah. Yeah. You didn't say that, but and, and just that was so freeing.
[00:51:23] Yeah. So I'm just putting this line in the sand and until then, I give myself permission not to self flatly.
[00:51:29] Zubin: Yes,
[00:51:30] Aline: yes.
[00:51:31] Zubin: Yep. Give yourself permission. I think that's really the heart of it, because without that, you constantly in this inner battle, this turmoil. So looklin as always, love speaking to you could go on for hours.
[00:51:41] I promised you like a 20, 30 minute thing, and we end up going for almost an hour. I apologize, but was, was
[00:51:46] Aline: delightful. Thank you. Thank you so much for your support and for reading my stuff and for helping spread the word about some of, like, I, I hope that this data gets out into the world.
[00:51:57] Zubin: Oh, it, it will, it, it can't, we can't keep [00:52:00] this good thing down.
[00:52:00] I send it to my students quite regularly and say, Hey, read this blog, read this post. You know, 'cause if you're not gonna listen to me, that I get it. You know? You know me. So sometimes that, that breeds a certain amount of contempt, like, oh, you know, zooms, you know, getting again, I get it. I get it. Okay, just ignore me.
[00:52:16] Listen, Alene, you know, fine. Ignore me for now. Listen to lean the fact that I'm gonna come back and say, I told you so and Alene told you so, you know, I'll come back and say that, but you know, in the meanwhile, read this so, you know, to become a standing joke now. But look, thank you so much. I've said this the last time as well, for everything you do for, for pulling back the curtain.
[00:52:33] And I really, really hope people just pay attention. You don't need too many advisors. You don't need too much input. You don't need more information. You need. The right information, and then you need focus and you need to tune out all the noise. Elene will give you the right information. It's up to you to tune out the noise.
[00:52:48] Everything else is noise guys. Follow her on LinkedIn. And you guys have a podcast tune now, right?
[00:52:52] Aline: We started one recently. Yeah. Yes. It's called Whiteboard Confidential. It's, it's pretty cool. Actually. I'll, I'll plug it for like 10 seconds or maybe, oh, we'll see. So [00:53:00] we have two kinds of episodes.
[00:53:01] One is just a full on interview replay where you just get to listen to people's mock interview. In four weeks. Oh, okay. The second is a deep dive where we sit down with somebody ask them about their job search, have them listen to their mock interviews, compare them to the real ones. The first one we did is with a guy who was a pro poker player and was trying to break into tech and was just having a miserable time of it.
[00:53:23] And then he randomly got an email from Meta and then like spent months studying and like, he had never done ML before, and he like ended up being a senior MLE. Or he, I guess he like self-taught and he was doing some computer vision stuff to help him with quote group, but that was it. So pretty interesting guy.
[00:53:38] And we, a person a month. So check it out. It's, it's good. Perfect.
[00:53:43] Zubin: Fantastic. So whiteboard confidential links, whiteboard,
[00:53:46] Aline: confidential
[00:53:47] Zubin: links will be in the notes. That's awesome. Look at you continuing to sort of just generate all this incredibly valuable information. It's information, it's inspiration, and it's clarity, which I think is just.
[00:53:58] Just a holy trifecta. So [00:54:00] good on Elene. Very happy that you exist and that you do the things that you do. Big fan. Thank you so much for your generosity, not just with your thoughts and your ideas, but also with your time today. And I'm sure we'll have another conversation at some point or time that's gonna be every bit as interesting.
[00:54:13] So thank you. Thank
[00:54:13] Aline: you. Maybe we'll have you on our podcast next
[00:54:16] Zubin: that whenever you like, not a problem at all. All right, I'm well, I'll see you next time, Elene. Thank you. Thank you.
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