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Tech interviews with NeetCode · The Pragmatic Engineer

0:00
SPEAKER_01: What separates strong engineers from everyone else?
0:02
SPEAKER_01: Navdeep Singh,
0:03
SPEAKER_01: or as many call him NEAT,
0:05
SPEAKER_01: he created NEAT Code,
0:06
SPEAKER_01: the coding preparation platform that helps countless devs get hired at big tech.
0:10
SPEAKER_01: In today's episode,
0:11
SPEAKER_01: we cover what preparing for data structures
0:13
SPEAKER_01: and algorithms interviews teaches you that's useful on the job and how it's more
0:17
SPEAKER_01: about the mindset than the algorithms.
0:19
SPEAKER_01: The growing difference between engineers who can still think without AI
0:22
SPEAKER_01: at their fingertips and those who freeze without it.
0:25
SPEAKER_01: NEAT's
0:25
SPEAKER_01: intentious hot take that some people should just give up on tech careers and many more.
0:30
SPEAKER_01: If you want to understand which entering skills compound over a career and the ones
0:34
SPEAKER_01: that AI is quietly eroding, this episode is for you.
0:37
SPEAKER_01: This episode is presented by Antisysys.
0:39
SPEAKER_01: Antisysys runs your whole system in a hostile simulation and finds every bug
0:43
SPEAKER_01: before your users do.
0:44
SPEAKER_01: It sounds like science fiction, but it's actually hardcore engineering.
0:48
SPEAKER_01: Understand how at antisysys.com slash pragmatic.
0:51
SPEAKER_01: This episode is brought to you by Sentry.
0:54
SPEAKER_01: Sentry is a tool I use for application monitoring back when I worked at Uber,
0:57
SPEAKER_01: and I now use it on all my projects, including the Pragmatic Engineer backend.
1:01
SPEAKER_01: One new feature I'm really liking about Sentry is their SEER AI agent,
1:04
SPEAKER_01: which helps investigate production errors.
1:06
SPEAKER_01: For example, here's an actual error I had in my application.
1:09
SPEAKER_01: I can just ask here what might be the root cause,
1:11
SPEAKER_01: and it brings context,
1:12
SPEAKER_01: and it can also make a plan to fix it, all from the web interface.
1:15
SPEAKER_01: Oh, and it also works in Slack as well, not just the web.
1:18
SPEAKER_01: One place that I find even more handy to use Sentry is from codex or clockcode using SentryMCP.
1:23
SPEAKER_01: After you hook
1:24
SPEAKER_01: If you look up the MCP server, you can do some very useful things.
1:26
SPEAKER_01: For example,
1:27
SPEAKER_01: when an already resolved Sentry is your resurfaces,
1:29
SPEAKER_01: you can kick off an agent to investigate the regression,
1:32
SPEAKER_01: read the relevant code, and open a PR with a suggested fix.
1:35
SPEAKER_01: There's a little work involved to get all of this going.
1:37
SPEAKER_01: You need to connect Sentry to your
1:39
SPEAKER_01: Neat, welcome to the podcast.
1:40
SPEAKER_01: Yeah, I'm happy to be here.
2:09
SPEAKER_02: it's awesome to have you here let's start with something that i've been thinking
2:12
SPEAKER_02: about so there's been so many predictions that if and when ai will be good enough
2:17
SPEAKER_02: to like write code you know coding interviews will be dead because of
2:21
SPEAKER_02: the day-to-day we will not be writing code now most engineers are not writing code
2:25
SPEAKER_02: they're prompting at work and yet at
2:27
SPEAKER_02: the same companies coding interviews are still not dead what is your take on this
2:32
SPEAKER_00: Yeah, I think it's really funny with how much coding has changed the last few years
2:36
SPEAKER_00: and especially the last few months that coding interviews are the one area
2:41
SPEAKER_00: that have surprisingly stayed pretty consistent.
2:44
SPEAKER_00: I know some people like talk about them changing a lot.
2:47
SPEAKER_00: And so far they're kind of like changing a bit
2:50
SPEAKER_00: with like AI assisted coding interviews companies are trying that but surprisingly
2:55
SPEAKER_00: the coding interview format of data structures
2:57
SPEAKER_00: and algorithms is really really sticky and it's confusing to a lot
3:02
SPEAKER_00: of people myself included because like we've gotten to a point
3:06
SPEAKER_00: where you can ask like an AI bot any question about a code base it can give you
3:11
SPEAKER_00: a pretty good answer you can ask it to implement some feature you can
3:15
SPEAKER_00: It goes back to...
3:30
SPEAKER_00: How do you even evaluate if somebody's a good hire or not?
3:33
SPEAKER_00: Because there's one aspect of it, which is, do they have the hard skills?
3:38
SPEAKER_00: Do they have the technical skills?
3:39
SPEAKER_00: Can they think?
3:40
SPEAKER_00: And DSA interviews were never the best for that.
3:44
SPEAKER_00: Well, thinking, sure.
3:45
SPEAKER_00: But in terms of, does that skill translate to what you're doing on the job?
3:49
SPEAKER_00: It never really translated to that.
3:50
SPEAKER_00: It was more about evaluating, does somebody think?
3:53
SPEAKER_00: So I think that's one of the reasons.
3:55
SPEAKER_00: And the second reason that it's stayed sticky is that
3:58
SPEAKER_00: companies just have no idea how to evaluate like and they probably never did.
4:02
SPEAKER_00: I think I was talking to a friend of mine,
4:04
SPEAKER_00: Steve from Amazon,
4:06
SPEAKER_00: and he mentioned that like they've ran some studies and it's like very hard
4:10
SPEAKER_00: to know like whether somebody like when you hire somebody, no matter
4:13
SPEAKER_00: No matter how much data you have,
4:14
SPEAKER_00: no matter how you run the interview process,
4:16
SPEAKER_00: that it's just very hard to know how somebody's actually going to perform on
4:19
SPEAKER_00: the job.
4:20
SPEAKER_02: If they're going to work out, right?
4:22
SPEAKER_00: It's a very hard problem.
4:23
SPEAKER_00: Because even if somebody is good, how do you know they're going to be motivated?
4:25
SPEAKER_00: How do you know they're going to enjoy the team environment, the vibe, and all that stuff?
4:29
SPEAKER_02: So I think it's just really complicated.
4:31
SPEAKER_02: And could another reason be that it's been so sticky and it still is sticky
4:35
SPEAKER_02: that maybe it's just simple,
4:36
SPEAKER_02: it's there, it's kind of like, if it ain't broken, don't fix it type of mentality.
4:41
SPEAKER_00: I think so because anytime you try to change something you risk making it worse and
4:46
SPEAKER_00: so it's like first of all it's a lot of work to change that process
4:49
SPEAKER_00: at big companies like it's very bureaucratic there's gonna be a lot
4:52
SPEAKER_00: of like retraining and we're already kind of seeing that
4:55
SPEAKER_00: with companies like Meta trying to run AI coding interviews
4:58
SPEAKER_00: the training is really hard to get down
5:00
SPEAKER_00: because it's like interviewers are just not good like most interviewers do not like
5:05
SPEAKER_00: interviewing they hate it and so
5:06
SPEAKER_02: when you're saying training you mean interviewer training like training your
5:09
SPEAKER_02: interviewers like at a large company like a thousand of them
5:12
SPEAKER_00: to like be similar exactly yeah because at a big company you want the process
5:17
SPEAKER_00: to be standardized you want it to be the same for everybody and that's very hard
5:21
SPEAKER_00: to get right in general and it's even harder to get right
5:23
SPEAKER_00: when you have like more variables introduced like a new evaluation process training
5:28
SPEAKER_00: interviewers differently.
5:29
SPEAKER_00: Now you got to check the AI prompts, like all these variables.
5:33
SPEAKER_00: And so it's not like an exact science.
5:36
SPEAKER_00: It's hard to measure these things.
5:37
SPEAKER_00: It's practically impossible.
5:38
SPEAKER_00: So I think we're definitely going to see, I think, companies trying different things.
5:43
SPEAKER_00: I think we probably will see different interview formats introduced.
5:46
SPEAKER_00: I just think it is going to be a slower transition than most people think.
5:51
SPEAKER_02: I want to rewind a lot back into your early days.
5:54
SPEAKER_02: Like, how did you get into tech?
5:57
SPEAKER_02: What was your first interaction with programming coding?
5:59
SPEAKER_00: Yeah, I was actually studying electrical engineering when I was in college
6:02
SPEAKER_00: because I really liked math.
6:04
SPEAKER_00: I really liked physics.
6:05
SPEAKER_00: I know a lot of programmers don't.
6:06
SPEAKER_00: Some obviously do, but a lot of programmers don't.
6:08
SPEAKER_00: But they really like programming.
6:10
SPEAKER_00: And so when I got into programming, I was just taking an intro to C class.
6:14
SPEAKER_00: It was required for electrical engineering.
6:16
SPEAKER_00: I didn't really want to take it.
6:17
SPEAKER_00: And I was not very good at it initially.
6:20
SPEAKER_00: I remember trying to learn print F and the, you know, percent S, percent C, like...
6:26
SPEAKER_00: I don't know why like I looked around everybody around me was learning it so quickly
6:29
SPEAKER_00: and to me it was just a very different way of thinking even though it's kind
6:33
SPEAKER_00: of related to math you'd think it'd be easy to pick up
6:35
SPEAKER_00: but it really wasn't initially for me but then I think a couple of months went by
6:38
SPEAKER_00: and we learned about variables conditions loops functions and all these kind
6:43
SPEAKER_00: of concepts and then it really like something just kind of clicked where it's like
6:47
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer, The Pragmatic Engineer
7:02
SPEAKER_00: of things these zeros and ones and all of
7:04
SPEAKER_00: a sudden you just have this enormous like universe
7:07
SPEAKER_00: of software solving insane problems you have databases like Google Spanner
7:13
SPEAKER_00: which not only take programming but they take physics they take like atomic clocks
7:17
SPEAKER_00: and GPS systems and all these things and they solve like these
7:22
SPEAKER_00: Really hard problems and so I guess to go back to my story
7:26
SPEAKER_00: and once I really started enjoying programming I just fell in love with it
7:29
SPEAKER_00: and I was like okay I'm gonna do this for the rest of my life I'm gonna love it and
7:33
SPEAKER_00: then I went through a transition where once I got into the real world I realized
7:38
SPEAKER_00: that programming is not something you can just kind of do
7:41
SPEAKER_00: the way you enjoy like it's a business at the end of the day and so that
7:44
SPEAKER_00: in a lot of ways took some of the fun out of it for me where it's like you don't get
7:47
SPEAKER_00: to work on the languages that you like the problems that you enjoy solving you have
7:51
SPEAKER_00: to focus on like the business problems and so yeah I have a love-hate relationship
7:57
SPEAKER_00: with programming because of that reason and I think a lot
7:59
SPEAKER_02: of people do it's interesting how you know you got really excited it was boring and
8:04
SPEAKER_02: then you got excited about the complexity and the possibilities and you kind
8:07
SPEAKER_02: of came back to down to earth
8:10
SPEAKER_02: One thing we were just talking about before we started the podcast is
8:14
SPEAKER_02: the CAP theorem on how you also had a similarly weird relationship with it.
8:19
SPEAKER_02: Can we talk about it?
8:20
SPEAKER_02: And also for those of us,
8:21
SPEAKER_02: those listening who don't know exactly what the CAP theorem is,
8:23
SPEAKER_02: let's start with that.
8:24
SPEAKER_00: Yeah, of course.
8:25
SPEAKER_00: So it's a pretty simple theorem.
8:28
SPEAKER_00: It's kind of described awkwardly sometimes where you have like three choices
8:32
SPEAKER_00: and you can pick two of three.
8:33
SPEAKER_00: There's consistency and that's data consistency.
8:37
SPEAKER_00: So in like a distributed system where you have
8:40
SPEAKER_00: Data that's like partitioned in different regions or something it can go out
8:43
SPEAKER_00: of sync like one database might be more up to date than the other and
8:46
SPEAKER_00: then there's availability where are both of these you know servers
8:51
SPEAKER_00: or databases available to read or maybe one went down and then
8:55
SPEAKER_00: the third one is partition
8:57
SPEAKER_00: or partition tolerance and so that basically means in a distributed system
9:01
SPEAKER_00: if there's a partition if maybe
9:03
SPEAKER_00: the system becomes disconnected one thing goes down how is it going to behave and so
9:09
SPEAKER_00: the two out of three framing is used a lot it's not super accurate and
9:13
SPEAKER_00: the entire theorem is very like incomplete and so when I was learning it for
9:17
SPEAKER_00: the first time I thought
9:19
SPEAKER_00: I like to go deep into things.
9:21
SPEAKER_00: I like to really, really understand things.
9:23
SPEAKER_00: That's what I love about programming.
9:24
SPEAKER_00: It's deterministic.
9:25
SPEAKER_00: If you really want to,
9:26
SPEAKER_00: you can go down to the exact line of code,
9:29
SPEAKER_00: the line of assembly, the zeros and ones to know exactly what was happening.
9:34
SPEAKER_00: So cap theorem felt like hand wavy to me and I didn't really like that and
9:39
SPEAKER_00: that goes back to why I don't like certain things about working on the job
9:42
SPEAKER_00: because I think like you don't get to go as deep as you like you have to solve
9:45
SPEAKER_00: the business problem even if it means you don't really understand some of
9:48
SPEAKER_00: the technical details but that's fine
9:50
SPEAKER_00: But later on,
9:51
SPEAKER_00: I felt so validated when I saw a blog post from Martin Kleppman talking
9:56
SPEAKER_00: about how much he didn't like cap theorem.
9:58
SPEAKER_00: And it was actually a little bit controversial where I think there were plenty
10:02
SPEAKER_00: of smart people in the comments of that blog post that
10:06
SPEAKER_00: I think that's a personal preference but I just felt very validated
10:15
SPEAKER_00: that somebody like him agreed with me and I think it's kind of funny because I think
10:20
SPEAKER_00: I never saw anybody mention that about Cap Theorem before.
10:22
SPEAKER_00: I saw posts on Stack Overflow.
10:24
SPEAKER_00: Nobody really mentioned it.
10:25
SPEAKER_00: It's kind of incomplete.
10:26
SPEAKER_00: And so the thing that came after it is like pack else,
10:30
SPEAKER_00: which is like if there's a partition,
10:32
SPEAKER_00: you can choose either availability or consistency.
10:35
SPEAKER_00: But if there's not, then there's still a tradeoff to be made, which is latency and consistency.
10:42
SPEAKER_00: So it's much more complete.
10:44
SPEAKER_00: I don't understand why anybody would learn the CAP theorem when that theorem exists,
10:47
SPEAKER_00: because it's just more complete.
10:48
SPEAKER_00: It's not that much more complicated.
10:50
SPEAKER_00: I think it's more simple to understand.
10:52
SPEAKER_02: I wonder if it's only a smaller subset of people who actually go deep,
10:55
SPEAKER_02: you know,
10:56
SPEAKER_02: CAF theorem, you actually like, all right, let me understand the whole thing.
10:59
SPEAKER_02: And then you realize it's incomplete.
11:00
SPEAKER_02: But most people might have just like looked at it, you know, took it.
11:04
SPEAKER_02: Okay, it's the law, two out of three, simple enough, move on.
11:07
SPEAKER_02: And I guess in most part of their lives,
11:08
SPEAKER_02: it's enough or they might not even use it or when they think they know,
11:12
SPEAKER_02: but they don't know it exactly.
11:14
SPEAKER_02: So it's interesting because we're talking about software engineers
11:16
SPEAKER_02: and you would think that most software engineers go into the details,
11:19
SPEAKER_02: but I guess maybe not.
11:20
SPEAKER_00: Yeah I think it goes to like solving the business problems
11:25
SPEAKER_00: and just like this is what I didn't like when I started working professionally
11:29
SPEAKER_00: because okay so you go through like documentation right you're going
11:31
SPEAKER_00: through onboarding like at Google there's so much documentation there's
11:34
SPEAKER_00: so many internal tools and
11:35
SPEAKER_00: and I want to go deep like I want to do depth first search on all
11:40
SPEAKER_00: the document links like you know you have one blog or you have one site
11:43
SPEAKER_00: and it has it references like five others that one is going
11:46
SPEAKER_00: to reference five others like I want to go through every single one have like
11:49
SPEAKER_00: a complete understanding of
11:50
SPEAKER_00: of everything.
11:51
SPEAKER_00: But that's just not how it works at jobs.
11:53
SPEAKER_00: Even a codebase,
11:55
SPEAKER_00: no one person is going to understand this massive codebase unless you write all
11:58
SPEAKER_00: of it by yourself, which is just not how companies work.
12:01
SPEAKER_00: And now we're kind of seeing a similar transition I think a lot of people are going through now
12:05
SPEAKER_00: with like agentic coding because it's kind of a similar concept
12:09
SPEAKER_00: where it's like now you might not even be looking at the code
12:11
SPEAKER_00: that you're actually producing yourself so it's kind of similar
12:15
SPEAKER_00: and I think this whole transition kind of reminds me of that
12:18
SPEAKER_00: where it's like you don't get to do some of the things that you used to enjoy
12:21
SPEAKER_00: but it's it's still you know that's that's life that's business
12:25
SPEAKER_02: So you graduated from University of Washington and you started work at
12:29
SPEAKER_02: the most obvious choice in Seattle.
12:31
SPEAKER_02: I guess in Seattle the two obvious choices are Amazon or Microsoft and you got
12:34
SPEAKER_02: into Amazon and it should have been a smooth ride.
12:36
SPEAKER_02: Like you made it into big tech, into the big leagues and then you quit after two months.
12:41
SPEAKER_02: What happened there?
12:42
SPEAKER_00: Yeah, first I want to say I actually went to Washington State University
12:45
SPEAKER_00: because I was actually not accepted to University of Washington.
12:48
SPEAKER_00: I wanted to go there, but I was not the best student in high school.
12:51
SPEAKER_00: So I was fortunate enough that I grinded super hard for interviews, had a pretty good GPA.
12:57
SPEAKER_00: So
12:57
SPEAKER_00: I got some interviews at Amazon and it was DSA related so I was able to crank
13:02
SPEAKER_00: that out and then once I actually got into the world
13:06
SPEAKER_00: and this is something I was self-aware about where I knew I was not
13:11
SPEAKER_00: a well-rounded person in that like working with people people skills
13:17
SPEAKER_00: and just anything of that like I could sit by myself go
13:21
SPEAKER_00: through like documentation work on things but like working
13:24
SPEAKER_00: with other people was very very like difficult for me
13:27
SPEAKER_00: In Amazon,
13:28
SPEAKER_00: the org I was in,
13:29
SPEAKER_00: Alexa, which has kind of been gutted from what I hear nowadays with LLMs.
13:34
SPEAKER_00: But the team I was specifically in,
13:36
SPEAKER_00: and I think Alexa, the org in general, was not the best place.
13:39
SPEAKER_00: It was not a well-oiled machine.
13:41
SPEAKER_00: A lot of manual stuff going on.
13:43
SPEAKER_00: It was a really stressful environment.
13:46
SPEAKER_00: I think when I joined, I saw a message on the internal thing.
13:50
SPEAKER_00: I think they used Chime at the time.
13:52
SPEAKER_00: But
13:53
SPEAKER_00: he said this feels like a thankless job and I was like I was going through
13:57
SPEAKER_00: the history of the team channel and this was like a week before I joined
14:00
SPEAKER_00: and I was like whoa okay so this is like clearly not like
14:03
SPEAKER_00: a positive team environment right now I think they were all like decent people I
14:06
SPEAKER_00: don't blame any of the individuals I don't even hold a grudge
14:08
SPEAKER_00: against Amazon it was just a crappy situation and so I think in hindsight like
14:14
SPEAKER_00: if I had to do it over again I'd probably be able to survive like I kind
14:17
SPEAKER_00: of know things it would have been stressful and crappy either way
14:20
SPEAKER_00: but I would have been able to get through it
14:22
SPEAKER_00: But at the time, I just didn't really know.
14:24
SPEAKER_00: And I had a lot of personal issues at the time.
14:26
SPEAKER_00: And so for whatever reason, I just made a very impulse decision to just leave the job.
14:31
SPEAKER_00: Afterwards,
14:31
SPEAKER_00: I kind of regretted it because I felt a little bit of a relief,
14:35
SPEAKER_00: but then I just felt a lot worse because I was like, okay, now what do I do?
14:38
SPEAKER_02: And can you talk me through on like what it felt joining,
14:41
SPEAKER_02: you know,
14:41
SPEAKER_02: like the first impressions,
14:43
SPEAKER_02: what the onboarding was like and what were the things
14:45
SPEAKER_02: that just were like not adding up?
14:47
SPEAKER_00: It was very intense.
14:50
SPEAKER_00: So we had a meeting because there were like five or six new grads who joined.
14:53
SPEAKER_00: within a one to two week period for the same team yeah
14:57
SPEAKER_00: and I think there were like four experienced engineers already and so like they
15:00
SPEAKER_00: over doubled the team and mostly new people so you think okay well if you introduce
15:05
SPEAKER_00: a bunch of new people you're gonna obviously onboard them like get them up to speed
15:08
SPEAKER_00: but they have a lot of deadlines that they were dealing with so it was kind of like
15:12
SPEAKER_00: the experienced people were just working and the rest of us were kind of just like
15:15
SPEAKER_00: on our own and so we had a meeting where it was like one of those
15:20
SPEAKER_00: where you're just kind of like introducing the new people right and
15:24
SPEAKER_00: Like again I don't blame any of the people but they were like nobody said anything
15:28
SPEAKER_00: the experienced people like they did not like say anything the manager had
15:30
SPEAKER_00: to like kind of keep like prompting them to like talk and to be friendly and stuff
15:34
SPEAKER_00: and I think they just wanted the meeting chance so they could go back
15:36
SPEAKER_00: and like finish their work because they had deadlines to meet I saw people
15:39
SPEAKER_00: and I'm not saying one person every one of
15:42
SPEAKER_00: the experienced engineers was committing 3am and we have like 8am or 9am meeting in
15:47
SPEAKER_00: the morning tomorrow
15:49
SPEAKER_00: And some people are reviewing the PRs at the same time.
15:52
SPEAKER_00: So I don't know if it's this culture where like,
15:55
SPEAKER_00: I don't think the manager told them you have to do this.
15:57
SPEAKER_00: I think it's like implicit where it's like,
15:59
SPEAKER_00: you know, you kind of know that it's a stressful environment right now.
16:02
SPEAKER_00: If you're the one person who's not doing it at 3 a.m.,
16:05
SPEAKER_00: you're going to be the first in line to maybe get kicked out of the company.
16:09
SPEAKER_02: Yeah, and also, I mean,
16:10
SPEAKER_02: Amazon at the time,
16:13
SPEAKER_02: they had a target of 6% unregretted entrition every year,
16:18
SPEAKER_02: which meant that managers or like directors at their level had to have 6%
16:22
SPEAKER_02: of people leave the company and marked as unregretted,
16:25
SPEAKER_02: which meant that either people quit on their own and you said like,
16:28
SPEAKER_02: oh, actually, this person was not great, unregretted.
16:30
SPEAKER_02: or you need to put people on performance improvement plans and then have them leave
16:34
SPEAKER_02: and say like, yep, that was unregarded attrition.
16:36
SPEAKER_02: So it's somewhat controlled in some of the org or most of the orgs.
16:40
SPEAKER_00: Yeah, I almost have like some conspiracy theories about that
16:43
SPEAKER_00: because I think I gave my resignation actually three times
16:45
SPEAKER_00: before they finally like accepted it in a way, which was surprising to me.
16:49
SPEAKER_00: I was like, why don't like they accept the resignation even after like the second time?
16:52
SPEAKER_00: And I was thinking like maybe this is like because like it looks
16:55
SPEAKER_00: I don't blame any of the people
17:10
SPEAKER_00: I have no grudges against any of the managers even the skip manager
17:14
SPEAKER_00: because I remember
17:15
SPEAKER_00: when I was quitting they told me like yeah like sometimes we do let people go
17:19
SPEAKER_00: and stuff like that but I don't see it that way I just see it as like
17:22
SPEAKER_00: a bad culture fit and they were trying to be nice about it
17:24
SPEAKER_00: but again it was it was they weren't even trying to hide it like it was obvious that
17:29
SPEAKER_00: the culture is like intense and some people would say toxic I'll use
17:32
SPEAKER_00: the word intense to be more generous but yeah
17:35
SPEAKER_02: Later on, you were able to get into Google many months later.
17:38
SPEAKER_02: How did joining Google feel compared to Amazon?
17:42
SPEAKER_00: It was the opposite experience.
17:46
SPEAKER_00: And they're kind of opposite companies in a lot of ways,
17:48
SPEAKER_00: like the business culture or even the tech culture and all that.
17:51
SPEAKER_00: But I was kind of in like Amazon PTSD mode where I was like,
17:55
SPEAKER_00: OK, like that was my first kind of like real professional.
17:58
SPEAKER_00: professional experience.
17:59
SPEAKER_00: I extrapolated that to be like everywhere in big tech or even just professionally in general.
18:04
SPEAKER_00: So I was like, OK, you're supposed to not ask questions.
18:08
SPEAKER_00: You're supposed to not talk to people.
18:10
SPEAKER_00: You're supposed to not even be friendly.
18:11
SPEAKER_00: You're supposed to just work and just be as intense as possible.
18:14
SPEAKER_00: But people were very friendly to me.
18:16
SPEAKER_00: And so I kind of reciprocated that.
18:17
SPEAKER_00: But I didn't ask questions.
18:18
SPEAKER_00: I was very scared to.
18:19
SPEAKER_00: So I worked on my own for the most part.
18:21
SPEAKER_00: And I was given a project from my manager that turned out to be a
18:25
SPEAKER_00: The Pragmatic Engineer
18:43
SPEAKER_00: The manager and team saw me as independent,
18:45
SPEAKER_00: which is what you need to do to get promoted from junior to mid-level.
18:49
SPEAKER_00: I was very lucky to get promoted very quickly because of that,
18:52
SPEAKER_00: and that helped me build my confidence a lot.
18:54
SPEAKER_00: That made me realize, okay, I can start asking questions now, which is funny.
18:58
SPEAKER_00: After I got promoted is when I was more comfortable asking questions
19:02
SPEAKER_00: when you'd expect that from a junior engineer more.
19:04
SPEAKER_02: But this is so interesting because you've only at that point had maybe two months
19:08
SPEAKER_02: of professional experience working at Amazon
19:10
SPEAKER_02: when you joined Google another six months or so later and how you can have a lot of
19:16
SPEAKER_02: reflexes ingrained in you coming into a company.
19:19
SPEAKER_02: So you can almost imagine like another engineer who had like two or three jobs
19:24
SPEAKER_02: before,
19:24
SPEAKER_02: you know,
19:25
SPEAKER_02: they might have built up all these unsaid things that are coming
19:27
SPEAKER_02: from other companies' cultures or what they've learned.
19:29
SPEAKER_02: And when they join, it can be hard for them to adapt to the company.
19:35
SPEAKER_02: I'm not sure we think about this in the industry.
19:37
SPEAKER_00: Yeah,
19:38
SPEAKER_00: I think it's kind of funny you mentioned that because I was in Google Cloud where
19:41
SPEAKER_00: a lot of the leadership was from other companies like Amazon and we had a VP or GM.
19:47
SPEAKER_00: He joined from Amazon for a few months and he actually left shortly after that as well.
19:51
SPEAKER_00: Like, I don't know the exact stories behind that.
19:52
SPEAKER_00: Yeah, but I think there is a lot of like in the industry, a lot of culture can get like mapped.
19:58
SPEAKER_00: A lot of people at Google didn't like the Amazon managers because it's like, oh,
20:01
SPEAKER_00: they're going to be less likely to like take us on a trip or pay for us
20:04
SPEAKER_00: because they know that Amazon has like the frugality and Google doesn't.
20:08
SPEAKER_00: But slowly,
20:09
SPEAKER_00: like while I was there,
20:10
SPEAKER_00: it slowly started to get a go in that direction,
20:12
SPEAKER_00: especially with the layoffs and all that.
20:14
SPEAKER_02: Getting promoted at Google, what does it take?
20:16
SPEAKER_02: What does it mean?
20:17
SPEAKER_02: I know there's promotion packets.
20:18
SPEAKER_02: I know there's committees.
20:19
SPEAKER_02: What did you see from your perspective?
20:21
SPEAKER_00: Pretty straightforward.
20:21
SPEAKER_00: I think like going from junior to mid-level is probably easier,
20:25
SPEAKER_00: I think, than from mid-level to senior and as you get higher and higher.
20:29
SPEAKER_00: In my case, it was mostly just about like
20:31
SPEAKER_00: working independently.
20:32
SPEAKER_00: And then once like I was lucky to get promoted,
20:35
SPEAKER_00: I think in about a year, almost exactly a year, which is very uncommon at Google.
20:40
SPEAKER_00: And I could sit here and probably humble brag and act like I'm just like this super genius.
20:45
SPEAKER_00: But I think it
20:46
SPEAKER_00: it was really I think there's like you have to one put in the work
20:50
SPEAKER_00: to be reasonably smart but I think vast majority
20:53
SPEAKER_00: of people are reasonably smart enough I think it's it goes into the other things
20:56
SPEAKER_00: where it's just right team right project because if you don't get
21:00
SPEAKER_00: the right project there's no way you can
21:02
SPEAKER_00: and if you're working on relatively easy stuff you can't really say
21:07
SPEAKER_00: that you solved like a really hard problem.
21:09
SPEAKER_00: So I think it takes a lot of that.
21:11
SPEAKER_00: Google has a lot of like documentation where it's like every single thing needs
21:14
SPEAKER_00: to be supported with like some metrics or
21:16
SPEAKER_00: artifact like some design doc and so they have like this culture
21:20
SPEAKER_00: of probably producing too many design docs for really simple things
21:23
SPEAKER_00: and people don't love that but i think in terms of like processes it's just
21:28
SPEAKER_00: a necessary evil at google because otherwise some engineers might just like work
21:33
SPEAKER_00: on stuff that they just feel like working on there's no impact to the business and
21:36
SPEAKER_00: so it's hard to kind of like quantify that
21:39
SPEAKER_02: And then on the side,
21:40
SPEAKER_02: even before Google,
21:41
SPEAKER_02: you started what is now known as Neat Code and a lot of people watching
21:45
SPEAKER_02: or listening will know you or even your voice from there.
21:48
SPEAKER_02: Can you tell me how that all started and how it continued as you were working at Google?
21:54
SPEAKER_00: Yeah, so I initially started after I quit Amazon, I think, like six years ago.
22:00
SPEAKER_00: And I was doing it really just for fun and for the love of the game.
22:04
SPEAKER_02: You were recording videos, right?
22:05
SPEAKER_00: Yeah, I was making these tutorial videos.
22:07
SPEAKER_00: I was like, I'm studying this right now.
22:09
SPEAKER_00: I got nothing better to do I might as well like help some other people
22:12
SPEAKER_00: and I found it very difficult because there weren't really tutorials at
22:15
SPEAKER_00: the time there was just a lot of like forum posts
22:19
SPEAKER_00: of these really like complex solutions and I'm like sitting there banging my head
22:23
SPEAKER_00: against the wall trying to understand
22:24
SPEAKER_00: I think most people didn't understand the solutions because it's very hard
22:29
SPEAKER_00: to like I think most people just looked at the algorithm kind of had
22:31
SPEAKER_00: a high level understanding of it didn't quite know why it worked
22:34
SPEAKER_00: but it was good enough usually to if you saw that question in
22:36
SPEAKER_00: an interview you could probably pass the interview
22:39
SPEAKER_00: And this goes back to like deep thinking,
22:42
SPEAKER_00: which I think was a skill that is more of a personality trait for me,
22:45
SPEAKER_00: but I think it helped me a lot with like the LeetCode stuff.
22:48
SPEAKER_00: I went really deep into things,
22:49
SPEAKER_00: but at the time felt kind of meaningless where it's like you make this video
22:52
SPEAKER_00: for 50 people watching.
22:54
SPEAKER_00: and you did a great job, but clearly it's not worth the several hours it takes to do that.
22:59
SPEAKER_00: But I kept doing it because I enjoyed it.
23:01
SPEAKER_00: About a year after I started making the videos consistently, I think I did get into Google.
23:07
SPEAKER_00: Very fortunate to do that.
23:08
SPEAKER_00: Interview process was pretty easy at that point, thankfully.
23:10
SPEAKER_00: So I kind of backed off the videos.
23:12
SPEAKER_00: I was like, this is kind of a, like,
23:14
SPEAKER_00: like it was fun but I'm at Google now for the rest of my life and then I saw
23:21
SPEAKER_00: that actually like I made a video telling people like hey guys I got into Google by
23:25
SPEAKER_00: the way you might not see me as much anymore and funny enough after that
23:29
SPEAKER_00: the channel like went exponential because I think it added like so much credibility.
23:33
SPEAKER_00: It's like, OK, this guy didn't make these videos after he got into Google.
23:36
SPEAKER_00: He actually made it before.
23:38
SPEAKER_00: And so this is what he did.
23:40
SPEAKER_00: And then he got in.
23:40
SPEAKER_00: So it's like it's like the best sales pitch in the world.
23:43
SPEAKER_00: Like I proved it.
23:44
SPEAKER_00: I went from zero to one.
23:46
SPEAKER_00: And so it was, I guess, a really good selling point.
23:50
SPEAKER_00: And it kind of bothers me personally because it's like the videos didn't change.
23:52
SPEAKER_00: Right.
23:52
SPEAKER_00: Like the branding change.
23:53
SPEAKER_00: But that made like a really big difference.
23:55
SPEAKER_00: Yeah and since so after it went exponential I was like okay maybe I'll make like
23:58
SPEAKER_00: a website and then the website was completely free at the time which is really
24:01
SPEAKER_00: a catalog of the videos to make it easy to use and that went viral as well and then
24:08
SPEAKER_00: so pretty shortly after
24:10
SPEAKER_00: After I got promoted,
24:11
SPEAKER_00: I was like, huh, like maybe I can like try this full time because I really loved it.
24:16
SPEAKER_00: I couldn't go as deep as I wanted to at Google.
24:18
SPEAKER_00: I had to solve business problems.
24:19
SPEAKER_00: But with algorithms and data structures,
24:21
SPEAKER_00: I can go super deep,
24:23
SPEAKER_00: more deep than most people would ever want to go into those things.
24:26
SPEAKER_00: But I had a reason to because it's like, OK, I can explain these things to people.
24:30
SPEAKER_00: And so, yeah, I think it was just it was like the right timing for me.
24:34
SPEAKER_00: And then afterwards, thankfully, it's like worked out so far.
24:37
SPEAKER_02: But you were at Google,
24:39
SPEAKER_02: you just got promoted to L4,
24:40
SPEAKER_02: which is still mid-level,
24:42
SPEAKER_02: but you now had a path to L5,
24:44
SPEAKER_02: which, I mean, it used to be the terminal level at the time.
24:47
SPEAKER_02: Now L4 is a terminal level.
24:48
SPEAKER_02: But, you know, in Google, you go to L6, L7, L8, principal assessor.
24:51
SPEAKER_02: You had that path of, like,
24:52
SPEAKER_02: stay inside Google,
24:54
SPEAKER_02: do this stuff,
24:55
SPEAKER_02: or start your business or turn this into business and go deep into algo coding.
25:00
SPEAKER_02: How were you thinking of the two options and what you would give up or what kind of,
25:04
SPEAKER_02: you know, how much risk would one or the other have?
25:07
SPEAKER_00: Yeah, I thought about that a lot because even though I didn't love certain things
25:12
SPEAKER_00: about Google, I actually really liked the company.
25:14
SPEAKER_00: I liked the people and it wasn't this super stressful environment.
25:18
SPEAKER_00: And when I was leaving my TL, who was basically my manager at the time, he's my manager.
25:24
SPEAKER_02: TL being tech lead, right?
25:25
SPEAKER_00: Yeah, tech lead.
25:26
SPEAKER_00: And he kind of asked me,
25:27
SPEAKER_00: he said,
25:28
SPEAKER_00: I'm a bit perplexed that you're leaving because you got promoted very quickly
25:32
SPEAKER_00: and you could probably get promoted again.
25:33
SPEAKER_00: And like, I did think about that a lot because
25:38
SPEAKER_00: It seemed like, because that was what I was going to do the rest of my life.
25:41
SPEAKER_00: I was going to work at Google.
25:41
SPEAKER_00: I was going to get promoted.
25:43
SPEAKER_00: I was going to be like the best engineer I could be.
25:44
SPEAKER_00: But I just felt like the timing of it, like I had a chance to try something by myself.
25:51
SPEAKER_00: Maybe that opportunity isn't going to be there.
25:53
SPEAKER_00: forever Google does make it easy for people even to this day
25:57
SPEAKER_00: if you leave you can usually come back within a year if you're on like good standing
26:01
SPEAKER_00: with your team and stuff like that which thankfully I was so that kind of made it
26:04
SPEAKER_00: a little bit easier I have friends all the time that are making like
26:07
SPEAKER_00: the same decision they're asking me like should I leave Google I just had
26:09
SPEAKER_00: a friend last week she wanted
26:11
SPEAKER_00: to like do content creation full-time I think it's like a trend almost these days
26:15
SPEAKER_00: where everybody's quitting their job to do their own thing but
26:18
SPEAKER_02: Well, I mean, I think we always live in bubbles, right?
26:22
SPEAKER_02: But in certain bubbles, it is.
26:24
SPEAKER_02: How was the switch?
26:26
SPEAKER_02: Can you tell me, like, you actually went from, okay, you made the decision.
26:29
SPEAKER_02: You went from having a really structured workday, a team, everything was figured out.
26:33
SPEAKER_02: Google has amazing internal infra.
26:34
SPEAKER_02: You can just focus on, okay, the business problem is still coding.
26:37
SPEAKER_02: And now you're like, okay, you have the website, you have Git repository.
26:41
SPEAKER_02: What was the switch like?
26:42
SPEAKER_02: And what was interesting about it or good and fun?
26:46
SPEAKER_02: What was difficult?
26:47
SPEAKER_00: I had a tough time with the learning curve at Google but once I left I had
26:51
SPEAKER_00: a tough time like transitioning away from some of the tools because you get used
26:54
SPEAKER_00: to it very quickly like they have like GitHub I'm just not a huge fan of it I know
26:59
SPEAKER_00: a lot of people are hating on it nowadays with like the uptime issues and stuff like
27:01
SPEAKER_00: that but I have other issues
27:02
SPEAKER_00: with it around like UX and stuff I think Google has certain internal tools
27:06
SPEAKER_00: that aren't so great but they have some that are just like super super good
27:11
SPEAKER_00: and there's been a lot of companies that have been started just
27:13
SPEAKER_00: because like somebody at Google built something that they're like hey we could make
27:17
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer
27:32
SPEAKER_00: The Pragmatic Engineer
27:47
SPEAKER_00: The Pragmatic Engineer
28:12
SPEAKER_00: and I hated it before but now I actually really love it because it's like
28:16
SPEAKER_00: when it does work when you find somebody and they're a good fit
28:19
SPEAKER_00: and you feel like you can contribute to their growth you can like guide them
28:24
SPEAKER_00: a little bit like you can steer the ship a little bit and you see like how much of
28:27
SPEAKER_00: a difference that makes to them like now I finally understand what leadership means
28:31
SPEAKER_00: when you like when it works like when you're an effective leader like you can make
28:36
SPEAKER_00: a magnitude of difference in like
28:39
SPEAKER_00: Even in a small team,
28:40
SPEAKER_00: but I imagine as you get to higher and higher levels, it can make a huge difference.
28:43
SPEAKER_00: And you see that with CEOs.
28:44
SPEAKER_00: Sometimes when a new CEO takes over,
28:46
SPEAKER_00: the entire company either can go up or maybe it goes in the other direction.
28:51
SPEAKER_02: When you started, you hit Google, you had a website that listed your videos.
28:54
SPEAKER_02: I guess very simple.
28:54
SPEAKER_02: HTML, CSS, maybe a bit of JavaScript.
28:57
SPEAKER_02: What did you build and what was the tech stack behind it?
29:00
SPEAKER_00: Yeah.
29:00
SPEAKER_00: So initially when I made the free site, I was still working at Google.
29:03
SPEAKER_00: So I just chose some like random Google tools.
29:06
SPEAKER_00: I was using Google Cloud Firebase because it was so easy to use.
29:09
SPEAKER_00: I regret that one because now I meet so many people.
29:12
SPEAKER_00: I'm like, oh, maybe I should do Convex now.
29:14
SPEAKER_00: I should have done something
29:16
SPEAKER_00: but I also did Angular at the time which is what I used at Google
29:20
SPEAKER_00: so I was like it makes sense maybe I can just learn it at the same time regret
29:24
SPEAKER_00: that one as well but thankfully we've gotten to a point now with LLMs
29:29
SPEAKER_00: so like migrating things has become relatively trivial
29:32
SPEAKER_00: so like maybe that's something I'll do but
29:35
SPEAKER_00: In terms of building the application itself,
29:38
SPEAKER_00: for whatever reason,
29:39
SPEAKER_00: I just didn't find that super interesting because there's usually not
29:42
SPEAKER_00: that many deep problems.
29:43
SPEAKER_00: I think the interesting things came from innovating and doing things in a way that
29:50
SPEAKER_00: The Pragmatic Engineer
30:05
SPEAKER_02: video is the product or most of the product, right?
30:08
SPEAKER_00: Yeah, because it's education.
30:10
SPEAKER_00: So it's like if the education is bad, then nobody really cares.
30:13
SPEAKER_00: And I was very bad at building,
30:15
SPEAKER_00: but I think the idea,
30:16
SPEAKER_00: the concept,
30:18
SPEAKER_00: the value was good enough that no matter how crappy the site looked
30:22
SPEAKER_00: and how bad tech choices I made, the business value
30:28
SPEAKER_00: That mattered more.
30:29
SPEAKER_00: And so that taught me a lot about prioritizing things that actually matter.
30:33
SPEAKER_00: And then you can take shortcuts on the things that don't matter.
30:35
SPEAKER_00: I think I saw Elon Musk has this four-step or five-step process for optimizing a workflow
30:42
SPEAKER_00: and a process where you start cutting things out.
30:47
SPEAKER_00: And sometimes you cut too much out and you realize you made a mistake.
30:51
SPEAKER_00: And then you can slowly introduce that back in.
30:53
SPEAKER_00: So I took that kind of approach because I was mostly working by myself.
30:56
SPEAKER_00: I probably should have hired people to move faster, but I didn't.
31:00
SPEAKER_00: And so because of that, I took a lot of shortcuts.
31:02
SPEAKER_00: And I still take shortcuts today because there's just so much value in it.
31:07
SPEAKER_00: And I have a story I could tell that people probably get mad about
31:10
SPEAKER_00: but it's worked so far for me so i i stick with that what what's the story
31:15
SPEAKER_00: so i have this service that i was paying like three thousand a month for a service
31:20
SPEAKER_00: and then i think late last year early this year when like
31:23
SPEAKER_00: the ai vibe coding stuff went really crazy i was uh i was new i could probably write
31:28
SPEAKER_00: my own version of this service what service was it it was like for code execution
31:33
SPEAKER_00: And so I thought like probably I could write my own version of this for like
31:38
SPEAKER_00: within like a month or two.
31:39
SPEAKER_00: But the $3,000 a month,
31:41
SPEAKER_00: the opportunity of that versus like other things I could be working on,
31:45
SPEAKER_00: there were other more impactful things that I could be doing.
31:48
SPEAKER_00: Okay, with vibe coding,
31:48
SPEAKER_00: like maybe I could get this done in less time, maybe a couple of weeks if I'm lucky.
31:53
SPEAKER_00: And so I actually got it done in like two or three days.
31:56
SPEAKER_00: And it did take coding skills.
31:58
SPEAKER_00: Like if I didn't know how to code, I would not have been able to do it.
32:00
SPEAKER_00: But I got it done in like three days.
32:02
SPEAKER_00: And then so I deployed the service.
32:03
SPEAKER_00: And so now that I'm managing it, it costs me like 200 a month versus like 3000.
32:08
SPEAKER_00: But there's a bug in the service.
32:10
SPEAKER_00: I think there's a memory leak or something.
32:13
SPEAKER_00: And so what happens is I have this service deployed every couple days,
32:19
SPEAKER_00: like one or two instances will crash, right?
32:22
SPEAKER_00: It's clearly an issue.
32:23
SPEAKER_00: There's a protection issue.
32:24
SPEAKER_00: I could spend the time to go into that and fix it.
32:28
SPEAKER_00: This is like one of those things where it's like you get into vibe coding
32:32
SPEAKER_00: and you run into an issue and it's like, okay,
32:33
SPEAKER_00: now you're gonna have to actually dig into the details to really understand like
32:35
SPEAKER_00: where the issue is coming from.
32:37
SPEAKER_00: So I think it would actually take me much longer than three days probably to find the issue.
32:40
SPEAKER_00: So I haven't even bothered with that because I'm like, well, okay,
32:43
SPEAKER_00: if one instance goes down,
32:45
SPEAKER_00: like I'll just have several instances running at the same time, right?
32:47
SPEAKER_00: I'll have like four.
32:48
SPEAKER_00: So if one goes down and it doesn't happen that frequently,
32:51
SPEAKER_01: Neet was just talking about operating a service when you have
32:54
SPEAKER_01: to manage your own infra and taking care of spinning up new instances
32:57
SPEAKER_01: when one crashes.
32:58
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33:53
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33:55
SPEAKER_01: And the reason they most likely did not have it is because it's a ton of work
33:58
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34:01
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34:05
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34:06
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34:08
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34:11
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34:14
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34:17
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34:20
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34:23
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34:27
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35:15
SPEAKER_01: that's antithesis.com slash pragmatic and with this back to neat and his story
35:20
SPEAKER_01: on why he's happy leaving a production bug unfixed it's an interesting trade-off
35:24
SPEAKER_00: where like engine like the engineer in me hates that because it's like there's
35:28
SPEAKER_00: an issue like fix it but
35:30
SPEAKER_00: the business value makes no difference like there has been practically zero outages
35:35
SPEAKER_00: i have less outages than leak code and i'm like like a couple people doing it
35:39
SPEAKER_00: so it's like
35:41
SPEAKER_00: I just think it's like a trade-off.
35:43
SPEAKER_00: And people could argue one way or the other,
35:45
SPEAKER_00: but I think it just makes so much sense right now for me to not fix it.
35:48
SPEAKER_02: That's so interesting.
35:49
SPEAKER_02: So you paid for an engine or license that, if I understand, it was executing code, right?
35:56
SPEAKER_02: So when people type out stuff in your editor,
36:00
SPEAKER_02: it runs it, you can check, it can run your problems as such as solutions.
36:05
SPEAKER_02: With AI assistance, you knew what you wanted to build.
36:07
SPEAKER_02: You built this engine in a way that
36:10
SPEAKER_02: You think it should work,
36:11
SPEAKER_02: you tested it,
36:12
SPEAKER_02: it seems to work everywhere,
36:13
SPEAKER_02: if you deployed it, and it took you two to three days, and now you have this...
36:17
SPEAKER_02: There's a quality regression, but it doesn't make a huge difference to the thing.
36:21
SPEAKER_02: But I want to push you on this.
36:22
SPEAKER_02: Do you not think this is a little bit...
36:25
SPEAKER_02: Typical of what we're seeing with AI assisted coding or AI coding of like a lot
36:30
SPEAKER_02: of people are like again like oh there's a sass that my company is paying for I'm
36:34
SPEAKER_02: a founder I'm paying for it I can replace it and you build up something
36:38
SPEAKER_02: that is subpar and you kind of get by and then it makes no business sense to fix it
36:43
SPEAKER_02: or it's now too difficult because you didn't write all of it but
36:45
SPEAKER_02: of course it was faster
36:46
SPEAKER_00: So the way I think about it is like if I did fix
36:50
SPEAKER_00: the issue I could probably allocate like a smaller pool of servers
36:54
SPEAKER_00: so maybe I could save like a couple like a hundred bucks more and I do think
36:58
SPEAKER_00: about like okay does this actually make a difference like I've actually thought
37:01
SPEAKER_00: about it a lot I'm like should I just fix it because like
37:06
SPEAKER_00: is it going to be an issue later on?
37:07
SPEAKER_00: And I like I initially tested it.
37:09
SPEAKER_00: I was only sending like a small amount of like traffic to this service
37:12
SPEAKER_00: and I still had most of it going to the original one.
37:15
SPEAKER_00: So I just ran it for a couple of weeks and I was like,
37:18
SPEAKER_00: I've got this like I'm pretty sure there's going to be issues.
37:20
SPEAKER_00: And I saw like literally no issues.
37:22
SPEAKER_00: Like it's just up like,
37:24
SPEAKER_00: OK, once in a while, the servers will go one of the servers will go down.
37:28
SPEAKER_00: But then it just like replaced in like a couple of minutes.
37:31
SPEAKER_00: That's just how I think about it.
37:32
SPEAKER_00: It just doesn't matter.
37:34
SPEAKER_00: My service is technically faster because I run it on better hardware.
37:37
SPEAKER_00: Yeah, I just see that nobody cares.
37:40
SPEAKER_00: No user has mentioned anything about that to me.
37:42
SPEAKER_02: It's better now.
37:43
SPEAKER_02: Yeah, so I guess maybe to look at it a bit better is it's overall better
37:49
SPEAKER_02: because it's cheaper, it's faster,
37:52
SPEAKER_02: And yeah,
37:53
SPEAKER_02: there is,
37:54
SPEAKER_02: of course,
37:54
SPEAKER_02: there's trade-offs,
37:55
SPEAKER_02: like with engineering,
37:55
SPEAKER_02: there is now a regression that crashes one of the servers,
37:58
SPEAKER_02: so you run an additional one,
38:00
SPEAKER_02: you have replication,
38:02
SPEAKER_02: if you will,
38:03
SPEAKER_02: and it's still cheaper overall,
38:04
SPEAKER_02: so overall, as you package it, it's better than before, boom.
38:07
SPEAKER_02: It's kind of an obvious business decision,
38:10
SPEAKER_02: and I guess in engineering,
38:12
SPEAKER_02: there's a question of how perfectionist you need to go when
38:14
SPEAKER_02: a problem is already solved and is good enough.
38:16
SPEAKER_00: Yeah, that's right.
38:17
SPEAKER_00: I mentioned at the beginning that it bothers me that you can't go super deep.
38:20
SPEAKER_00: And so even for this, it actually does bother me.
38:23
SPEAKER_00: But I guess I've gotten used to it in the sense of prioritizing the business
38:27
SPEAKER_00: and just thinking about the actual value.
38:29
SPEAKER_00: What do people care about?
38:30
SPEAKER_00: What's actually going to make a difference?
38:33
SPEAKER_00: Not just in the short term, but also in the long term.
38:35
SPEAKER_02: Let's talk about this,
38:39
SPEAKER_02: especially as a founder,
38:40
SPEAKER_02: but even as a software engineer,
38:42
SPEAKER_02: at some point you need to start to think about the business.
38:44
SPEAKER_02: But the interviews that people are taking with Neat Code in order to get into big tech,
38:50
SPEAKER_02: First, you need to jump the hoop of coding interviews.
38:53
SPEAKER_02: What do you think preparing for these data structures and algorithms,
38:57
SPEAKER_02: coding interviews, gives to people that is actually useful on the job?
39:01
SPEAKER_02: And I'm not talking about the algorithms,
39:02
SPEAKER_02: but actually the other things that you gain by preparation.
39:06
SPEAKER_00: Yeah, I think so.
39:07
SPEAKER_00: From my perspective,
39:08
SPEAKER_00: at least, I like went through this four year degree, but I didn't cheat through it.
39:12
SPEAKER_00: So I made sure I understood like all the fundamentals and things like that.
39:15
SPEAKER_00: And then I got an Amazon and then I left.
39:18
SPEAKER_00: And so for that year before I got into Google,
39:21
SPEAKER_00: I was really just doing leak code.
39:23
SPEAKER_00: I was I was making the explanation videos and that kind of taught me
39:26
SPEAKER_00: about like speaking and communicating and like thinking deeply about like a problem
39:31
SPEAKER_00: and maybe like the trade offs between like algorithms and data structures
39:34
SPEAKER_00: and stuff like that.
39:36
SPEAKER_00: But I didn't really do
39:36
SPEAKER_00: And so when I did get into Google,
39:39
SPEAKER_00: I was still able to get promoted,
39:42
SPEAKER_00: even though I think like in terms of just regular like raw coding
39:46
SPEAKER_00: and coding experience,
39:47
SPEAKER_00: I was probably subpar compared to most people, but I was still able to
39:51
SPEAKER_00: to like for a hard problem that I had no idea how to solve,
39:54
SPEAKER_00: like using internal tools I've never used before.
39:57
SPEAKER_00: I had the skill of, okay, I can sit down.
40:00
SPEAKER_00: I can go through this stuff.
40:01
SPEAKER_00: I can kind of make a plan of how I'm going to try things.
40:04
SPEAKER_00: And I worked a lot on my communication.
40:06
SPEAKER_00: so that I could go to my manager and say, okay, so this is what I'm thinking.
40:10
SPEAKER_00: Kind of like you do in an interview, right?
40:11
SPEAKER_00: Like, okay, this is the approach I'm thinking.
40:13
SPEAKER_00: Like, this is what I'm thinking.
40:14
SPEAKER_00: Like, I'm gonna go ahead and do it just so you're on the same page.
40:17
SPEAKER_00: Like,
40:17
SPEAKER_00: maybe you have time to like look into it and give me feedback or maybe you don't,
40:20
SPEAKER_00: but just so we're on the same page.
40:21
SPEAKER_00: Like, this is what I'm gonna be doing.
40:23
SPEAKER_00: So funny enough, like I do think I've gained a lot
40:26
SPEAKER_00: from algorithms and data structures in terms of just thinking,
40:30
SPEAKER_00: also on the communication side and also on the trade-off side,
40:33
SPEAKER_00: which I think is really what engineering is about.
40:36
SPEAKER_00: And it goes back to what people are experiencing with agentic coding and stuff.
40:41
SPEAKER_00: everything is a trade-off it's not really
40:43
SPEAKER_00: in engineering there's no correct answer like there is in math
40:47
SPEAKER_00: or science engineering is about the best solution at
40:50
SPEAKER_00: the time like like we're talking about like the the memory leak issue
40:54
SPEAKER_00: with my service right it's
40:56
SPEAKER_00: The Pragmatic Engineer
41:11
SPEAKER_00: Do I know all these little things?
41:14
SPEAKER_00: When I think they're forgetting to zoom out and look at the bigger picture
41:18
SPEAKER_00: of what engineering is even about in general.
41:21
SPEAKER_00: Because what you see in the real world is you'll see a really good engineer going
41:25
SPEAKER_00: from one domain
41:26
SPEAKER_00: to another or a really smart person like going from one to another and you see that
41:30
SPEAKER_00: and you think like what do they have
41:32
SPEAKER_00: that other people don't it's usually not some like very specific skill
41:36
SPEAKER_00: that like they know this programming language super well it's usually something
41:39
SPEAKER_00: related to that like it could be whether it's data structures and algorithms
41:42
SPEAKER_00: or like some hard skill it's like what you gain from that in general
41:48
SPEAKER_00: and I think that's like
41:49
SPEAKER_00: Education in general as well where like you go through like 20 years
41:52
SPEAKER_00: of your life learning about all sorts of subjects and that like molds you in
41:56
SPEAKER_00: a way that's very hard to articulate it's very hard to be precise
42:00
SPEAKER_00: about like what exactly did you gain by like learning math and physics and speaking
42:04
SPEAKER_00: and writing and history but clearly
42:08
SPEAKER_00: There's a lot there.
42:10
SPEAKER_02: It sounds like you're saying that the effort of learning,
42:13
SPEAKER_02: the effort of going through doing hard things that might be pointless at the time
42:17
SPEAKER_02: or like solving problems that are maybe abstract or not for a specific thing,
42:21
SPEAKER_02: they add up over time?
42:23
SPEAKER_00: I think so a lot.
42:24
SPEAKER_00: And this actually reminds me of a conversation I was having with Chip.
42:29
SPEAKER_00: I think you've also had her on the podcast.
42:32
SPEAKER_00: And she was because nobody knows what's happening with AI.
42:36
SPEAKER_00: So we were talking about it and she mentioned
42:38
SPEAKER_00: that in her opinion it's very hard to know like what hard skills are going
42:42
SPEAKER_00: to be important right like
42:43
SPEAKER_00: which programming language should you learn today like how high level should you go
42:47
SPEAKER_00: do you even need to know how to code right but as she mentioned
42:50
SPEAKER_00: that okay like those are like impossible questions to answer but the one
42:53
SPEAKER_00: And the first thing that she did understand is that systems thinking,
42:56
SPEAKER_00: this like broad concept that applies to engineering and computer science,
43:00
SPEAKER_00: but also to many other disciplines as well.
43:03
SPEAKER_00: And the way I kind of understand it to use an example is like maybe in a construction
43:09
SPEAKER_00: You walk around, you see all these buildings being built.
43:11
SPEAKER_00: You see the workers.
43:13
SPEAKER_00: And whenever I look at that,
43:14
SPEAKER_00: I see this big complex thing and all these people doing all these little things.
43:19
SPEAKER_00: And then at the end of it, you have this big building built that's so complex.
43:23
SPEAKER_00: a person could probably do that themselves.
43:25
SPEAKER_00: But it goes back to you have like the workers working on like the individual thing,
43:28
SPEAKER_00: but then you have this entire system and somebody set up that system.
43:31
SPEAKER_00: Somebody set up those rules that, OK, a worker is going to do this.
43:34
SPEAKER_00: There's going to be this procedure.
43:36
SPEAKER_00: This is what we're going to check to make sure that there's no issues.
43:38
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer
43:53
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer, The Pragmatic Engineer,
44:08
SPEAKER_00: I'm going to push you a bit on that.
44:10
SPEAKER_00: Is it really systems thinking or is it learning a domain?
44:23
SPEAKER_02: systems don't exist in a vacuum.
44:25
SPEAKER_02: You will have agricultural systems that are very specific to how
44:29
SPEAKER_02: the agricultural industry works.
44:32
SPEAKER_02: If you're in a legal industry, it's based on whatever country you're operating in.
44:36
SPEAKER_02: If you're in a legal tech startup and healthcare
44:38
SPEAKER_02: startup looks very different in the US versus like the UK versus in Romania, etc.
44:43
SPEAKER_02: The people who are who are great system thinkers
44:46
SPEAKER_02: in one domain often are are they just really understand the domain
44:49
SPEAKER_02: and your payments is one example where I worked in
44:51
SPEAKER_02: which is very interesting slash complex slash
44:54
SPEAKER_02: Once you get into it, people start to move around in it because you go there.
44:57
SPEAKER_02: So I wonder if there's abstract level system thinking,
45:01
SPEAKER_02: but there is also becoming a domain expert.
45:03
SPEAKER_02: And somehow they kind of overlap as well,
45:05
SPEAKER_02: because if you are a domain expert, you must understand the system of that domain.
45:09
SPEAKER_02: And maybe if you understand multiple domains,
45:10
SPEAKER_02: you can get better at abstract level system thinking as well.
45:14
SPEAKER_00: Yeah no I completely agree with that and I think for me it's definitely hard
45:17
SPEAKER_00: to like quantify and articulate because it's very kind of like vague
45:21
SPEAKER_00: and I think you're definitely right though that like the skills the hard skills like
45:27
SPEAKER_00: the knowledge and like the details of like certain industries and things like
45:31
SPEAKER_00: that definitely matters but
45:34
SPEAKER_00: I don't know I guess like when I think about it I think
45:36
SPEAKER_00: of it maybe you know people like this as well like there's certain engineers
45:39
SPEAKER_00: that are like they could go from one domain to another and you just trust them like
45:43
SPEAKER_00: that you just know from working with them they're smart like they think in
45:46
SPEAKER_00: a certain way where like they could go from payments
45:49
SPEAKER_00: to like some completely other industry real estate or something and
45:54
SPEAKER_00: There will be that learning curve for them,
45:56
SPEAKER_00: but some people,
45:57
SPEAKER_00: for whatever reason,
45:58
SPEAKER_00: they just learn faster, they just get it faster, they just perform better.
46:01
SPEAKER_00: And I don't think that this is something that was innate,
46:05
SPEAKER_00: that this was just handed down by God,
46:07
SPEAKER_00: that some people are just smarter than others.
46:08
SPEAKER_00: I think there's a lot that goes into it.
46:11
SPEAKER_00: I can't probably articulate it super well,
46:13
SPEAKER_00: but I think a lot of the things that people might say that like, oh,
46:16
SPEAKER_00: it was a waste of time to learn this subject
46:18
SPEAKER_00: because I didn't actually use like those details on the job.
46:21
SPEAKER_00: I think that's a very wrong way to think about it.
46:23
SPEAKER_00: And I think that's what a lot of people are doing now with AI.
46:25
SPEAKER_00: Like, hey, what if I'm not going to be writing for loops a couple of years from now?
46:29
SPEAKER_00: I don't think those things are a waste of time.
46:31
SPEAKER_02: Sounds like you're saying that it's
46:34
SPEAKER_02: You don't think it's a waste of time to go deep and understand things.
46:37
SPEAKER_02: Yeah, absolutely.
46:37
SPEAKER_02: Especially when it's hard to do so.
46:39
SPEAKER_00: Absolutely, yeah.
46:40
SPEAKER_02: Let's talk about the hiring bar at FANG companies, the big tech companies.
46:45
SPEAKER_02: A lot of people are using neat code to prepare for these interviews.
46:48
SPEAKER_02: interviews,
46:49
SPEAKER_02: you're getting feedback from them of,
46:50
SPEAKER_02: you know,
46:50
SPEAKER_02: like they will write to you when they succeed or they will write in frustration
46:54
SPEAKER_02: after many months they haven't.
46:56
SPEAKER_02: So you get a bunch of signal here.
46:58
SPEAKER_02: What are you seeing in terms of just the algorithmic part,
47:02
SPEAKER_02: you know,
47:02
SPEAKER_02: the coding interview,
47:03
SPEAKER_02: like are things staying the same, getting harder, getting easier?
47:07
SPEAKER_00: In terms of the format,
47:08
SPEAKER_00: especially at the early levels,
47:10
SPEAKER_00: like juniors and stuff,
47:11
SPEAKER_00: it's still a lot of algorithms and data structures, the format itself.
47:14
SPEAKER_00: I've definitely seen, I think, anecdotally, people are mentioning that it's getting harder.
47:20
SPEAKER_00: At the same time, from the people who do
47:22
SPEAKER_00: past the interviews they still like at least at big tech companies like Google
47:27
SPEAKER_00: and stuff I'd say the difficulty is not that different from what it was before
47:32
SPEAKER_00: at least in the U.S.
47:33
SPEAKER_00: I think it varies
47:33
SPEAKER_00: by countries like you'll see like some countries like India it's very different
47:37
SPEAKER_00: everything is pretty much algorithms there it's like leak code hards and super hards
47:41
SPEAKER_00: and stuff like that but in the U.S.
47:44
SPEAKER_00: I don't think it's that crazy but it's uh
47:48
SPEAKER_02: Yeah, it's not too crazy.
47:50
SPEAKER_02: Yeah, but I guess like,
47:52
SPEAKER_02: you know,
47:52
SPEAKER_02: going without any support,
47:54
SPEAKER_02: like in a whiteboard, it's still hard to prepare for that.
47:57
SPEAKER_02: It's never been easy.
47:58
SPEAKER_00: Yeah, absolutely.
47:59
SPEAKER_00: And I think the one thing that's happened a lot is people,
48:03
SPEAKER_00: like there's been cheating tools for it.
48:06
SPEAKER_00: and so we've had mostly remote interviews for the last like five six years
48:10
SPEAKER_00: and that's been changing a bit I think Google is pretty much gone to on-sites
48:15
SPEAKER_00: at this point back to the traditional whiteboard format and they'll let you code on
48:19
SPEAKER_00: a laptop if you want to as well but it's going to be in person somebody's going
48:22
SPEAKER_00: to be watching you code and you're probably not going to be able to cheat your way
48:26
SPEAKER_00: through that
48:27
SPEAKER_02: What are interview formats that you're seeing?
48:29
SPEAKER_02: We're talking about other companies, especially smaller ones, experimenting.
48:32
SPEAKER_02: What are interview formats you're seeing?
48:34
SPEAKER_02: Or you think they're actually kind of promising?
48:35
SPEAKER_02: Like if you were running a small or mid-sized company,
48:38
SPEAKER_02: you might actually consider instead of the...
48:41
SPEAKER_02: And you're talking against yourself here.
48:44
SPEAKER_02: But if you had to throw away the DSA interviews,
48:48
SPEAKER_02: what is giving promise, especially with AI-assisted tools?
48:51
SPEAKER_00: Any process that you have that's going to be standardized and super scalable,
48:54
SPEAKER_00: there's always going to be ways to game that.
48:56
SPEAKER_00: And the best way to get around that would be like hire somebody who's who's
49:00
SPEAKER_00: an intern and you saw how they performed.
49:03
SPEAKER_00: And what I've spoken to a lot of companies about last week is
49:06
SPEAKER_00: a lot of small companies that can get away with it are doing like trial periods.
49:11
SPEAKER_00: It could be a few days.
49:13
SPEAKER_00: It could be like a month.
49:15
SPEAKER_00: It could be even a couple of months, kind of like an internship.
49:17
SPEAKER_00: And I've spoken to other companies that say that that's difficult for them because
49:20
SPEAKER_00: if you're trying to hire somebody who already has a job,
49:23
SPEAKER_00: that's not going to be feasible.
49:25
SPEAKER_00: You can't really do that.
49:27
SPEAKER_00: But I've, believe it or not,
49:29
SPEAKER_00: have leaned in that direction where I can get a sense
49:33
SPEAKER_00: of somebody's leak code abilities pretty quickly.
49:36
SPEAKER_00: Like I'm not going to spend four interviews going through
49:39
SPEAKER_00: and asking somebody data structures and algorithm stuff.
49:41
SPEAKER_00: I just have them do work that
49:44
SPEAKER_00: that might be similar to something that I'd give them on a job,
49:46
SPEAKER_00: or even just have a conversation with them.
49:48
SPEAKER_00: See how they think.
49:49
SPEAKER_00: Can they think through trade-offs?
49:51
SPEAKER_00: I don't even care about what answer they give me to a problem.
49:54
SPEAKER_00: I care about why did they say that?
49:57
SPEAKER_00: What can they say?
49:58
SPEAKER_00: Is it just something that they...
49:59
SPEAKER_00: saw in like a chat GPT prompt and are just regurgitating it
50:02
SPEAKER_00: or can they actually like talk through it and then when you look at like the work
50:05
SPEAKER_00: that they're doing same thing like I asked them
50:08
SPEAKER_00: about it like why did you do it this way like what's good about this like what's bad
50:12
SPEAKER_00: about this what could be improved and I think it's a hard format to like scale
50:17
SPEAKER_00: for big companies that's why I don't think that that's what's going to happen
50:21
SPEAKER_00: in terms of like big tech but it's worked for me it works for smaller companies
50:25
SPEAKER_00: but once you get to a certain size it's harder to do
50:28
SPEAKER_02: Yeah, because you're basically,
50:29
SPEAKER_02: people are doing the work and it doesn't matter what tools they use.
50:32
SPEAKER_02: In fact,
50:32
SPEAKER_02: if now everyone's using AI agents,
50:35
SPEAKER_02: then yeah,
50:36
SPEAKER_02: they're using it as well and you actually get the signal
50:38
SPEAKER_02: of how they're doing compared to others.
50:40
SPEAKER_02: Interesting because one type of company that doesn't really have trouble hiring is
50:43
SPEAKER_02: the ones who are working in open source.
50:45
SPEAKER_02: And they will often end up hiring the people who are contributing to their repos
50:49
SPEAKER_02: and adding all the features already.
50:51
SPEAKER_02: And, you know,
50:51
SPEAKER_02: the conversation will probably be more of a soft skills conversation because like,
50:54
SPEAKER_02: yeah,
50:54
SPEAKER_02: we're seeing your work like you've been selflessly pushing features to our product.
51:00
SPEAKER_02: Awesome.
51:01
SPEAKER_02: And I guess that's kind of an upside of open source.
51:03
SPEAKER_00: I think Dax mentioned this because I was speaking to a bunch of people
51:06
SPEAKER_00: that like work with him that they just got that they were either like contributing
51:10
SPEAKER_00: to open code already or Dax like knew of them from open source work
51:13
SPEAKER_00: that they had done on projects of their own and they just got a DM from him
51:17
SPEAKER_00: and they're like and he's like hey would you be interested in working
51:20
SPEAKER_00: so they already had this work that they could showcase and it's like
51:23
SPEAKER_00: if you're doing things in public people can get a pretty good understanding
51:26
SPEAKER_00: of like how you work
51:27
SPEAKER_02: Speaking of how you work at Neat Code with your business, you and your team, how do you work?
51:32
SPEAKER_02: What tools do you use?
51:33
SPEAKER_02: And how much code do you actually ban your write these days, if any?
51:36
SPEAKER_00: Yeah, so I would say over the last six months,
51:40
SPEAKER_00: actually, we've been cranking a lot of features out, a lot of code out.
51:46
SPEAKER_00: Most of it has been written by AI at this point.
51:49
SPEAKER_00: And before, that really wasn't the case.
51:50
SPEAKER_00: I was actually a really bad
51:51
SPEAKER_00: But it's not like I just try to be pragmatic about it because I think
51:54
SPEAKER_00: before I was still using the tools, but they just weren't as good.
52:06
SPEAKER_00: And now they've gotten to a point where the work that I'm doing,
52:09
SPEAKER_00: which is mostly CRUD,
52:10
SPEAKER_00: usually there's not that much crazy,
52:12
SPEAKER_00: interesting stuff other than the code execution service.
52:14
SPEAKER_00: That's probably the most interesting one.
52:16
SPEAKER_00: But I'm using pretty outdated tech even.
52:18
SPEAKER_00: I'm using Angular on the front end, a Google tool that nobody likes.
52:23
SPEAKER_00: And I'm using Firebase, which isn't horrible.
52:27
SPEAKER_00: It gets the job done, but it's a little bit outdated at this point.
52:30
SPEAKER_00: I'm using Google Cloud and TypeScript.
52:33
SPEAKER_00: I would say initially,
52:34
SPEAKER_00: actually,
52:35
SPEAKER_00: like the first few years when I was writing most of the code,
52:37
SPEAKER_00: very, very bad code quality.
52:38
SPEAKER_00: I used TypeScript, but I was not using like real TypeScript.
52:42
SPEAKER_00: I had a lot of any.
52:43
SPEAKER_00: Yeah, I had a lot of bad code.
52:45
SPEAKER_00: I was putting inline CSS.
52:47
SPEAKER_00: I was just doing all sorts of things.
52:48
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer,
53:03
SPEAKER_00: I realized that that tradeoff was so worth it because I cleaned all of that up
53:06
SPEAKER_00: with AI because that's what it's for.
53:08
SPEAKER_00: Like it can clean up a lot of like sloppy code.
53:11
SPEAKER_00: It can refactor a lot of things.
53:13
SPEAKER_00: And if I really wanted to now, I could probably migrate to other tools very quickly with AI.
53:17
SPEAKER_00: So just to go back to the tradeoffs,
53:19
SPEAKER_00: I think it's just about thinking like you might make the wrong decision,
53:22
SPEAKER_00: but even if you make the wrong decision,
53:23
SPEAKER_00: you can go back and then try to correct it just kind of by thinking about it.
53:27
SPEAKER_02: She sends a post a bunch of her hot takes on social media as well.
53:30
SPEAKER_02: I don't know if it's like a 2 a.m. thing or...
53:32
SPEAKER_02: But one of them you said is,
53:35
SPEAKER_02: as I'm quoting you,
53:36
SPEAKER_02: now in 2026,
53:37
SPEAKER_02: it's never been easier to build things,
53:39
SPEAKER_02: but I would say that it just makes 10 times harder to actually build value.
53:43
SPEAKER_00: Yeah, I think because it's so easy now to implement a lot of things
53:50
SPEAKER_00: and people weren't implementing those things before
53:53
SPEAKER_00: because they just weren't worth doing.
53:55
SPEAKER_00: Like in my case, I went to the code quality example.
53:58
SPEAKER_00: I think that was worth doing because it matters.
54:01
SPEAKER_00: It can help you go faster.
54:02
SPEAKER_00: It's more maintainable.
54:03
SPEAKER_00: But in terms of like features like a website,
54:07
SPEAKER_00: like you can just throw features in there nowadays that nobody really cares about.
54:11
SPEAKER_00: And you can do it so quickly.
54:13
SPEAKER_00: like a new feature every single day.
54:15
SPEAKER_00: But do people actually care about that?
54:18
SPEAKER_00: Is that making it better?
54:19
SPEAKER_00: It could be making things worse.
54:20
SPEAKER_00: It could be making things more confusing.
54:23
SPEAKER_00: You have like things that are cluttered.
54:25
SPEAKER_00: You're maybe making the site perform really slowly now
54:27
SPEAKER_00: with all these features you're adding that nobody's even using.
54:30
SPEAKER_00: And so I think speed matters in business, but I think decisions matter as well.
54:35
SPEAKER_00: If you're going so fast, you're not measuring the impact of the changes that you're making.
54:39
SPEAKER_00: You don't have time to do that because you're just focused on shipping.
54:42
SPEAKER_00: and then things regress and things get worse and we've seen that
54:45
SPEAKER_00: at Anthropic recently the last like month or maybe more than that
54:49
SPEAKER_00: where things have regressed and I think just a couple days ago they put out like
54:54
SPEAKER_00: a blog post acknowledging that finally but it like
54:57
SPEAKER_00: for them they were just moving so fast
54:59
SPEAKER_00: that they did not notice like I saw Boris saying like he was replying to a lot
55:03
SPEAKER_00: of comments asking like we haven't really noticed this like why is everybody else
55:06
SPEAKER_00: noticing it and now they have and I think it's again just goes back
55:09
SPEAKER_00: to trade-offs like now maybe they've realized like okay maybe they should slow down
55:12
SPEAKER_00: a little bit focus more on quality and stuff like that or maybe not but
55:16
SPEAKER_02: I guess it does give a little bit of relief that we knew pre-AI it was pretty clear
55:24
SPEAKER_02: that if you move fast, you often break things.
55:27
SPEAKER_02: Facebook even had this famous motto, or you can be more deliberate and break things.
55:31
SPEAKER_02: The Pragmatic Engineer
55:58
SPEAKER_00: Yeah, it's funny because even OpenAI,
56:00
SPEAKER_00: they did like Sora and now they're shutting it down because they realized like,
56:04
SPEAKER_00: okay,
56:04
SPEAKER_00: the social network AI videos like these cat videos that you're seeing all over
56:08
SPEAKER_00: the place.
56:09
SPEAKER_00: And so they realized like, actually, like they're doing too much like doing
56:13
SPEAKER_00: Less things now and now they're kind of refocusing on like coding and a smaller set
56:16
SPEAKER_00: of things that's actually producing more value now they're kind of going
56:20
SPEAKER_00: the anthropic route where anthropic is going like pretty quickly
56:23
SPEAKER_00: but they were focusing mostly on coding and so I think that's interesting as well
56:26
SPEAKER_00: to see that like actually playing out at the highest of scales that like this like
56:31
SPEAKER_00: the fastest growing companies in
56:32
SPEAKER_00: the world like open AI are even doing this like they are not like trying
56:37
SPEAKER_00: to do everything they're refocusing now and trying to maybe slow down a bit
56:41
SPEAKER_02: This is a bit contradictory, though.
56:43
SPEAKER_02: We're almost saying that, well,
56:45
SPEAKER_02: maybe one thing we're learning,
56:47
SPEAKER_02: observing AI,
56:48
SPEAKER_02: that focus is more important than executing quickly on a lot of things.
56:53
SPEAKER_02: Wow.
56:54
SPEAKER_00: Yeah, it's funny.
56:56
SPEAKER_00: I think even the paper that started it all,
56:58
SPEAKER_00: the Transformers paper, was titled Attention is All You Need, where it was funny.
57:02
SPEAKER_00: It was focusing on the certain tokens, the relevant tokens, mattered the most.
57:06
SPEAKER_02: Yeah.
57:07
SPEAKER_02: Well, one interesting experiment you did is you did a redesign contest for NeedCode,
57:13
SPEAKER_02: I think, the site.
57:14
SPEAKER_02: You offered $2,500 for whoever submits redesign.
57:18
SPEAKER_02: Can you tell me how that went?
57:19
SPEAKER_00: Yeah, so I'm still going to evaluate the results,
57:22
SPEAKER_00: but so far from what I've seen, it's been a little bit disappointing.
57:25
SPEAKER_00: I'm going to try not to get too mad at anybody or make it personal with anybody,
57:29
SPEAKER_00: but it's very obvious to me that practically all of the submissions are created
57:34
SPEAKER_00: with AI, which is fine.
57:35
SPEAKER_00: If you're going to use AI, that's completely fine.
57:38
SPEAKER_00: But again,
57:39
SPEAKER_00: Like with the few people so far that I've spoken to and asked them questions about,
57:44
SPEAKER_00: okay, like your design, like it looks like you've made certain choices, right?
57:47
SPEAKER_00: You, you moved some buttons around,
57:49
SPEAKER_00: you removed some buttons, you, you removed some content, you added certain content.
57:53
SPEAKER_00: Why did you do it?
57:54
SPEAKER_00: what's the pros and cons of like maybe doing it this way they can't answer it and
57:59
SPEAKER_00: if they do answer it it's clearly like a very vague answer where they didn't think
58:02
SPEAKER_00: about it like me looking at their site for five minutes I can articulate things
58:06
SPEAKER_00: about their design better than they can and it's just disappointing it's like
58:10
SPEAKER_00: I don't think that's like a matter of intelligence I don't think it is I think it's
58:15
SPEAKER_00: a matter of like effort and caring and probably skill set as well like if you're
58:20
SPEAKER_00: if you just have the skill set of like designing things but
58:23
SPEAKER_00: but I don't I'm certainly not a designer but like in terms of a site whatever like
58:29
SPEAKER_00: the business is you should be able to say that okay so like this is
58:32
SPEAKER_00: about coding interviews and we're trying to maybe
58:36
SPEAKER_00: Show people that this is interesting or trying to explain it in a very clear way.
58:39
SPEAKER_00: Nobody can say that.
58:40
SPEAKER_00: They're just focused on how pretty the design looks.
58:43
SPEAKER_00: They're like, oh, the colors on this, the styling looks crazy.
58:46
SPEAKER_00: But that's not what I care about.
58:48
SPEAKER_00: That's not what most people care about.
58:50
SPEAKER_00: Nobody cares how pretty a site is if they don't really understand what it's for
58:54
SPEAKER_00: or what value it's going to give to them.
58:55
SPEAKER_00: I think that's what UX is about.
58:57
SPEAKER_00: It's not about how pretty something is.
58:59
SPEAKER_02: but I guess in all fairness right like this was a contest where you're like okay if
59:03
SPEAKER_02: the winning design will get $2,500 I guess it kind of flips the incentives
59:07
SPEAKER_02: a little bit because this doesn't mean that you are paid $2,500 to create
59:11
SPEAKER_02: a redesign it means that if you win you could get that and of course
59:14
SPEAKER_02: the more people submit something the lower the chance therefore
59:19
SPEAKER_02: if I'm just being logical here like the effort that's worth me putting
59:22
SPEAKER_02: into it is let's say maybe if it's like 10 contestants it's like maybe $250 or like
59:27
SPEAKER_02: I thought that a contest would have been the right way to do it because
59:29
SPEAKER_02: then I don't have to like hire and I don't have to like filter people and stuff like
59:32
SPEAKER_02: that but
59:32
SPEAKER_02: in hindsight I think it probably wasn't I probably should have just found like maybe
59:35
SPEAKER_02: even just a small pool of people and then just paid them up front and then
59:54
SPEAKER_00: and just saw the work that may be chosen based off that
59:57
SPEAKER_00: because I think there has been a lot of low effort.
59:59
SPEAKER_00: It's been disappointing, to be honest.
1:00:01
SPEAKER_02: Well, you'll have to learn.
1:00:02
SPEAKER_02: But I guess it does prove that just giving a prompt to an AI,
1:00:05
SPEAKER_02: which is low effort,
1:00:06
SPEAKER_02: low cost, it will not result in magical effort, especially not with design.
1:00:11
SPEAKER_00: Yeah, I think so.
1:00:11
SPEAKER_00: And I think it's funny because I think somebody could just use pen and paper,
1:00:14
SPEAKER_00: just kind of describe what they're trying to do,
1:00:16
SPEAKER_00: the main choices that make something better.
1:00:20
SPEAKER_00: On my site,
1:00:20
SPEAKER_00: even the parts that I've used AI to code,
1:00:22
SPEAKER_00: I can articulate exactly why everything is positioned in a certain
1:00:26
SPEAKER_00: where I can go back to like okay like metrics like this is used the most so I want
1:00:29
SPEAKER_00: to make this prominent I want to make this like a little bit different
1:00:33
SPEAKER_00: than you've seen on other sites where it doesn't look boring stuff like that but
1:00:37
SPEAKER_00: the people like submitting the designs they can't really articulate a lot
1:00:39
SPEAKER_00: of these things to me and I think like I said if you just do it on pen and paper and
1:00:44
SPEAKER_00: then give it to an AI then
1:00:45
SPEAKER_00: the AI can just do it like I don't care how pretty something looked I told them like
1:00:48
SPEAKER_00: what criteria I actually cared about
1:00:50
SPEAKER_00: and I think you know some people just didn't follow the directions or whatever
1:00:53
SPEAKER_00: and that's that's fine I guess
1:00:55
SPEAKER_02: One of your hot takes from a few months ago, the end of coding as we know it.
1:01:00
SPEAKER_02: Let's talk about it.
1:01:02
SPEAKER_02: Tim O'Reilly wrote a blog article about a year ago where he predicted
1:01:06
SPEAKER_02: that things would change and you were reflecting on it.
1:01:09
SPEAKER_00: Yeah I think it's been really interesting because a lot
1:01:11
SPEAKER_00: of people don't really go back to actually look
1:01:13
SPEAKER_00: at things they're just like forward looking but I think it's important
1:01:16
SPEAKER_00: to like go back and see how like things played out because
1:01:20
SPEAKER_00: that can help you like see how things are going to play out in the future as well
1:01:23
SPEAKER_00: and it's been interesting like with how much coding has changed with how good
1:01:29
SPEAKER_00: the models have gotten at the same time it's kind of surprising to me
1:01:34
SPEAKER_00: that we're still in like a very wait and see mode like
1:01:38
SPEAKER_00: The Pragmatic Engineer, The Pragmatic Engineer
1:01:52
SPEAKER_00: The Pragmatic Engineer
1:02:07
SPEAKER_00: I think programming is definitely going to continue to change definitively and,
1:02:10
SPEAKER_00: you know, maybe become a completely different field.
1:02:13
SPEAKER_00: But I think a lot
1:02:22
SPEAKER_00: What's even important in engineering in general?
1:02:42
SPEAKER_02: Yeah and also like for example things like you know in programming like
1:02:46
SPEAKER_02: when you think of like what is it that we code you need to build up
1:02:50
SPEAKER_02: a feature you didn't say you know the task is add a button where I don't know when
1:02:53
SPEAKER_02: when the user hits it I know it's it files a complaint something you know like
1:02:59
SPEAKER_02: so they can report a report a bug that's a simple one you know like that is not just
1:03:02
SPEAKER_02: as simple as behind the scenes of like if button hit file a bug it will have a bunch
1:03:07
SPEAKER_02: of like edge cases
1:03:09
SPEAKER_02: It will check the state.
1:03:10
SPEAKER_02: You will need to know what the context is.
1:03:13
SPEAKER_02: What kind of user is there?
1:03:14
SPEAKER_02: Free user?
1:03:15
SPEAKER_02: Page user?
1:03:15
SPEAKER_02: There's all these edge cases, conditions, the domain, the business domain, all of these things.
1:03:20
SPEAKER_02: And they were all captured in code, which means it's captured in your head.
1:03:23
SPEAKER_02: But now that you're prompting it,
1:03:25
SPEAKER_02: the context is still there and someone needs to know how important it is.
1:03:28
SPEAKER_00: Yeah I think like change is the one thing that's not changing
1:03:32
SPEAKER_00: because change is just keeps happening and I didn't mean to make that a pun
1:03:36
SPEAKER_00: but um like I just saw I think yesterday
1:03:38
SPEAKER_00: or today Microsoft is doing I think voluntary layoffs
1:03:43
SPEAKER_00: where they are buyouts yeah buyouts yeah so basically if somebody chooses
1:03:48
SPEAKER_00: to they can leave the company and get like some severance
1:03:51
SPEAKER_00: And I saw, I haven't confirmed this, but I asked a friend and they said it's true.
1:03:56
SPEAKER_00: The buyouts are true, but not like the age thing yet.
1:03:59
SPEAKER_00: But basically,
1:04:00
SPEAKER_00: they're only offering this to a subset of people at like a certain age
1:04:05
SPEAKER_00: and certain amount of experience.
1:04:05
SPEAKER_00: The Pragmatic Engineer
1:04:28
SPEAKER_00: because now they have to move in a new direction.
1:04:31
SPEAKER_00: I think they did something very similar when Satya originally took over.
1:04:33
SPEAKER_00: I think they did,
1:04:34
SPEAKER_00: I don't know if it was voluntary at the time,
1:04:36
SPEAKER_00: but they did a lot of layoffs and it was mostly to people.
1:04:39
SPEAKER_02: That was not voluntary in 2014.
1:04:40
SPEAKER_02: Yeah.
1:04:40
SPEAKER_00: Yeah.
1:04:41
SPEAKER_00: And so that was to a lot of
1:04:43
SPEAKER_00: experienced people specifically and not to the new people.
1:04:46
SPEAKER_00: So I think just being willing to change,
1:04:48
SPEAKER_00: being willing to do things in a way that you don't maybe enjoy,
1:04:53
SPEAKER_00: kind of like when I joined Google,
1:04:54
SPEAKER_00: like having to do things not going as deep as I would have liked,
1:04:57
SPEAKER_00: I think that's going to be pretty important.
1:04:59
SPEAKER_02: Yeah,
1:04:59
SPEAKER_02: and you did say that you don't think there will be an extinction of programmers
1:05:04
SPEAKER_02: or programming, even if programming changes, right?
1:05:07
SPEAKER_00: Yeah, I think even to this point,
1:05:11
SPEAKER_00: again it's like it's impossible to guess like my guess is as good as anybody else's
1:05:14
SPEAKER_00: but I just don't see like thinking going away I don't see problem solving going away
1:05:19
SPEAKER_00: I think it'll change dramatically it is possible like we might need like less
1:05:23
SPEAKER_00: programmers but even to this point that hasn't been
1:05:25
SPEAKER_00: the case like every single time there's this like
1:05:27
SPEAKER_00: the big innovation like cloud computing like higher level programming languages
1:05:31
SPEAKER_00: for whatever reason things do not
1:05:33
SPEAKER_00: It doesn't lead to fewer programmers and I would have expected it would have like
1:05:37
SPEAKER_00: when you have cloud like cloud services that can just solve these huge problems
1:05:43
SPEAKER_00: that were so difficult to solve like Google had to work so hard
1:05:47
SPEAKER_00: to solve like certain
1:05:48
SPEAKER_00: distributed system problems.
1:05:49
SPEAKER_00: And now you can just use AWS or GCP and just have that taken care of for you.
1:05:53
SPEAKER_00: So you would think that we just have infinite software.
1:05:55
SPEAKER_00: We're just like just doing everything and everything is easy.
1:05:58
SPEAKER_00: And now we don't need as many programmers, but it just hasn't happened.
1:06:01
SPEAKER_00: And so based on that,
1:06:03
SPEAKER_00: I don't know,
1:06:03
SPEAKER_00: like you see things like replet and lovable where anybody can be a programmer now.
1:06:08
SPEAKER_00: And so I don't know if that's the direction we're going to go in
1:06:09
SPEAKER_00: where it's just very, very high level.
1:06:11
SPEAKER_02: But it's very interesting because on one end,
1:06:13
SPEAKER_02: of course,
1:06:14
SPEAKER_02: we have these primitives that are getting more and more capable like the cloud.
1:06:17
SPEAKER_02: You would think there's competition between AWS, Azure, GCP, Oracle, and so on.
1:06:23
SPEAKER_02: And so, you know, the prices will obviously be as low as possible.
1:06:26
SPEAKER_02: But then you have someone like DHH who is like, okay, well,
1:06:30
SPEAKER_02: we're in AWS,
1:06:30
SPEAKER_02: we're spending a few million dollars per year,
1:06:33
SPEAKER_02: you know, like get rid of the Amazon services and just do it locally.
1:06:36
SPEAKER_02: which everyone thinks is gonna be expensive and so on,
1:06:38
SPEAKER_02: and they do it and they're now doing a massive savings.
1:06:41
SPEAKER_02: So it's almost like these abstractions are often becoming a lot more expensive
1:06:47
SPEAKER_02: to run,
1:06:48
SPEAKER_02: which is fine for most people,
1:06:49
SPEAKER_02: but when you get to a certain scale,
1:06:51
SPEAKER_02: you might start to invest in software engineers and building your own software
1:06:55
SPEAKER_02: and maintaining it to just reduce costs, which 37signals has done.
1:07:00
SPEAKER_02: So I wonder if anything, there might always be a value in a certain scale
1:07:09
SPEAKER_02: Yeah, I think so.
1:07:13
SPEAKER_00: I think it's always interesting to see how things play out in the longer term.
1:07:17
SPEAKER_00: Engineering is not a science.
1:07:18
SPEAKER_00: There's a lot of culture that goes into it.
1:07:20
SPEAKER_00: And you have companies that, in the cloud, why did a company like MongoDB get as big as it did?
1:07:25
SPEAKER_00: I think the tech
1:07:27
SPEAKER_00: might be like a small part of it.
1:07:29
SPEAKER_00: But I think a lot of it is just sales and marketing and culture.
1:07:33
SPEAKER_00: And if like one company is using it, it can snowball.
1:07:35
SPEAKER_00: And then like you have an entire industry using a certain tool.
1:07:38
SPEAKER_00: Then maybe they realize like actually like we went too far in the direction like
1:07:42
SPEAKER_00: We don't need to have everything in the cloud.
1:07:44
SPEAKER_00: It's not better.
1:07:45
SPEAKER_00: It's not saving us that much money.
1:07:47
SPEAKER_00: In some ways, we've seen cloud services get really, really complicated.
1:07:50
SPEAKER_00: Now it's like cloud-driven development.
1:07:53
SPEAKER_00: You have all these things and it's like, okay, you solved one problem and now you got a new one.
1:07:57
SPEAKER_00: with LLMs it's like kind of the same thing and even the cost issue
1:08:02
SPEAKER_00: with AI is probably going to be like once the subsidies start running out
1:08:06
SPEAKER_00: which we're starting to see I think that's going to be a really big issue
1:08:08
SPEAKER_00: where maybe all these companies that embraced AI programming are not going
1:08:12
SPEAKER_00: to like cut back on it
1:08:15
SPEAKER_02: You had a wacky train of thought, which I'd like to talk about.
1:08:19
SPEAKER_02: It involved AGI.
1:08:21
SPEAKER_02: I'm not a huge fan of talking about AGI because I feel it's very hard to talk, hard to define.
1:08:28
SPEAKER_02: But let's talk about it.
1:08:30
SPEAKER_02: You were saying, let's assume that there would be an AGI or a godlike singularity.
1:08:34
SPEAKER_02: These models would be amazing, which I think we can see they have limitations.
1:08:38
SPEAKER_02: But let's just jump forward.
1:08:40
SPEAKER_02: What was this thought on how we were chasing it?
1:08:43
SPEAKER_00: Yeah I think like on a philosophical level like it feels like you're trying to get
1:08:47
SPEAKER_00: to like infinity and it's like the closer you get you're the same distance away
1:08:51
SPEAKER_00: from it right and it's like that's what I feel like it feels like
1:08:55
SPEAKER_00: as like technology has gotten better you would think that like
1:08:58
SPEAKER_00: We've solved life at this point.
1:09:00
SPEAKER_00: We have abundant resources.
1:09:01
SPEAKER_00: And if we don't,
1:09:03
SPEAKER_00: we're not that far away from having enough food, water, and shelter for everybody.
1:09:06
SPEAKER_00: But it's something about life.
1:09:08
SPEAKER_00: Maybe it's human nature or something.
1:09:10
SPEAKER_00: It just doesn't change.
1:09:10
SPEAKER_00: It's like you want more.
1:09:13
SPEAKER_00: Okay, now there's higher levels of programming,
1:09:16
SPEAKER_00: so now people are competing at the higher level,
1:09:18
SPEAKER_00: and as it gets higher and higher,
1:09:21
SPEAKER_00: people are still going to be competing on some level.
1:09:23
SPEAKER_00: Maybe it's easy to build an app now,
1:09:24
SPEAKER_00: but there's going to be a new problem to solve on marketing and edge cases
1:09:28
SPEAKER_00: and things like that.
1:09:30
SPEAKER_00: But I also think
1:09:32
SPEAKER_00: Maybe this is like a politics thing because I think there's like technology,
1:09:36
SPEAKER_00: which we should all be happy that technology is improving.
1:09:39
SPEAKER_00: Like if AI keeps getting better,
1:09:40
SPEAKER_00: if it really does replace every job,
1:09:43
SPEAKER_00: in some ways that has to be a good thing
1:09:44
SPEAKER_00: because now you could do something you couldn't do before.
1:09:47
SPEAKER_00: farming.
1:09:47
SPEAKER_00: A lot of people were sad about that when that went away, I'm sure.
1:09:51
SPEAKER_00: But it's been a net positive.
1:09:53
SPEAKER_00: And I think the only reason it wouldn't be a positive is because
1:09:57
SPEAKER_00: if your livelihood depends on it and politics and you can't make money and then
1:10:02
SPEAKER_00: the government isn't going to take care of you,
1:10:03
SPEAKER_00: I think that's where it becomes an issue.
1:10:06
SPEAKER_00: It's more of a politics problem than a technology problem.
1:10:09
SPEAKER_02: Yeah, but I think my interesting observation is
1:10:12
SPEAKER_02: as we're seeing AI could make things better, I'm still waiting for
1:10:17
SPEAKER_02: the software to file taxes to be accessible to a normal person.
1:10:22
SPEAKER_02: Why do I,
1:10:23
SPEAKER_02: in every country I live,
1:10:25
SPEAKER_02: I have to hire an accountant to file my taxes,
1:10:28
SPEAKER_02: even though I don't have very complicated taxes, and that's one.
1:10:32
SPEAKER_02: We're talking about utilities, when your pipe is broken, when you want to find a plumber.
1:10:38
SPEAKER_02: So there's some everyday things where I would welcome software making things easier,
1:10:44
SPEAKER_02: but I haven't seen much progress in the past 15 plus years,
1:10:47
SPEAKER_02: and not even right now with AI.
1:10:49
SPEAKER_02: So it could be a nice trigger to see those things improving.
1:10:53
SPEAKER_00: Yeah, I think it's funny because like you look at history and I think one thing
1:10:57
SPEAKER_00: that I always take for granted is that like progress always happens
1:11:01
SPEAKER_00: and things always get better.
1:11:03
SPEAKER_00: But if you look at like most of history for thousands of years, things didn't always
1:11:08
SPEAKER_00: Another one of your hot takes is how overhyping AI tools just creates slop
1:11:11
SPEAKER_00: and erodes people's skills.
1:11:38
SPEAKER_00: Yeah, I think a lot of people,
1:11:40
SPEAKER_00: especially students, are unfortunately learning everything through LLMs.
1:11:45
SPEAKER_00: A lot of that isn't really learning.
1:11:47
SPEAKER_00: They're just kind of cheating and they're just doing everything like that.
1:11:51
SPEAKER_00: And then they lose a lot of their skills.
1:11:52
SPEAKER_00: And I think long term,
1:11:54
SPEAKER_00: that's going to be really interesting because we're seeing that
1:11:56
SPEAKER_00: with even experienced programmers.
1:11:58
SPEAKER_00: I had a friend tell me that he's preparing for interviews now
1:12:02
SPEAKER_00: and he hasn't handwritten much code in several months.
1:12:05
SPEAKER_00: So it's very hard for now him to get back into that.
1:12:08
SPEAKER_02: Yeah, this will be a longer timeframe,
1:12:10
SPEAKER_02: but I do wonder if one side effect of this could be that
1:12:14
SPEAKER_02: a lot more companies will be doing in-person interviews
1:12:16
SPEAKER_02: because you eliminate any AI assistance,
1:12:19
SPEAKER_02: you can actually talk with a person,
1:12:21
SPEAKER_02: and in in-person,
1:12:22
SPEAKER_02: you can actually tell the difference between someone who has put in the effort
1:12:25
SPEAKER_02: and can think and is sharp and can put things together versus someone who gets like
1:12:32
SPEAKER_02: frozen without the AI being there
1:12:34
SPEAKER_00: at their fingertips I think it's really interesting
1:12:37
SPEAKER_00: because I think like maybe companies won't care I think I'm probably one
1:12:39
SPEAKER_00: of those people that would get frozen even when I was working I was very bad
1:12:43
SPEAKER_00: at like writing code from scratch but if I'm like looking at a file I see all
1:12:47
SPEAKER_00: the imports I see the decorators and stuff like that like I'm pretty good at coding
1:12:50
SPEAKER_00: that I was kind of a copy and paste programmer where I'm just like copy and pasting
1:12:53
SPEAKER_00: a lot of snippets and just replacing the variables and things like
1:12:56
SPEAKER_00: that I guess maybe my hot take is that like maybe
1:12:59
SPEAKER_00: Certain things actually will be less tested like maybe like people just won't care
1:13:04
SPEAKER_00: that much if you can actually handwrite the code as long
1:13:06
SPEAKER_00: as you can understand like that's what I'm seeing with like some of
1:13:09
SPEAKER_00: the AI assisted assessments it's like okay like you can actually just go ahead
1:13:12
SPEAKER_00: and like implement this with AI
1:13:14
SPEAKER_00: I like all of it, if you want to, if you're able to do that.
1:13:18
SPEAKER_00: But then if the interviewer asks you, OK,
1:13:21
SPEAKER_00: this array of integers,
1:13:24
SPEAKER_00: what do the integers actually represent in the context of like this code?
1:13:27
SPEAKER_00: Like maybe it's like data points on something or it's like the short
1:13:29
SPEAKER_00: You have to be able to articulate that and figure that out.
1:13:36
SPEAKER_00: So I think it's interesting.
1:13:39
SPEAKER_00: Maybe I'm giving the same answer where I have no idea,
1:13:41
SPEAKER_00: but it's interesting to see the anecdotes that are happening.
1:13:45
SPEAKER_02: Well, one other take you have is you said
1:13:47
SPEAKER_02: that personality traits you think are now more important than coding skills,
1:13:52
SPEAKER_02: or actually most skills.
1:13:53
SPEAKER_00: Yeah, maybe personality traits isn't the best way to phrase it,
1:13:57
SPEAKER_00: but I think there's something about like a person that you're hiring.
1:14:01
SPEAKER_00: They're not a machine, right?
1:14:02
SPEAKER_00: They're not like, okay, like you look at the resume, like, okay, Java programmer or whatever.
1:14:08
SPEAKER_00: They're not that.
1:14:09
SPEAKER_00: I think people,
1:14:10
SPEAKER_00: especially in fields like software development,
1:14:13
SPEAKER_00: which are very open-ended and decisions matter,
1:14:18
SPEAKER_00: trade-offs matter, communication, all that stuff matters.
1:14:20
SPEAKER_00: When I'm hiring people, I hired somebody a few months ago, and they had certain skills.
1:14:27
SPEAKER_00: Obviously, they were going through their CS degree.
1:14:30
SPEAKER_00: They still haven't even graduated yet.
1:14:32
SPEAKER_00: But they are far better than practically anybody I've ever hired before,
1:14:37
SPEAKER_00: including people that are experienced,
1:14:39
SPEAKER_00: including people that I probably could have hired that are working at big tech
1:14:43
SPEAKER_00: and have these really big resumes.
1:14:45
SPEAKER_00: And then I ask myself,
1:14:46
SPEAKER_00: what is it that makes this person good and another person bad or less effective?
1:14:52
SPEAKER_00: It just goes back to the person.
1:14:53
SPEAKER_00: I think in startups,
1:14:54
SPEAKER_00: the term is agency,
1:14:56
SPEAKER_00: somebody who's high agency,
1:14:56
SPEAKER_00: who's just going to get things done, who's never going to say no to something.
1:15:00
SPEAKER_00: I think like that attitude is really important of like okay
1:15:04
SPEAKER_00: if I don't know something I'll just learn it like I'm not gonna say like that's not
1:15:08
SPEAKER_00: my job or I'm not gonna like dig deep into that like anytime I give this person
1:15:12
SPEAKER_00: a task even if they have no idea like how to start it like
1:15:15
SPEAKER_00: a week later they'll have like they'll have learned like everything
1:15:18
SPEAKER_00: about it it's like a completely new domain to them they just like learn everything
1:15:21
SPEAKER_00: and I think like those types of personality traits it's hard to describe
1:15:25
SPEAKER_00: that maybe like
1:15:26
SPEAKER_00: agency is the best term for it.
1:15:28
SPEAKER_00: But I think that matters the most because any information that you need
1:15:34
SPEAKER_00: at this point, you can kind of just prompt, right?
1:15:35
SPEAKER_00: Like, OK, like I have like this programming specific question.
1:15:38
SPEAKER_00: You can just you can just get it out of a prompt if you know the questions to ask.
1:15:43
SPEAKER_00: And knowing the questions to ask is just a matter of like, I think, putting in the effort.
1:15:46
SPEAKER_02: Yeah, so I like agency.
1:15:49
SPEAKER_02: I'm also sensing,
1:15:50
SPEAKER_02: you didn't mention it,
1:15:50
SPEAKER_02: but it's like energy, focus, wanting to solve something specific.
1:15:57
SPEAKER_02: And this is something interesting.
1:15:58
SPEAKER_02: I've been talking to a few people who are building startups right now.
1:16:02
SPEAKER_02: Obviously,
1:16:02
SPEAKER_02: a lot of them are to do with AI or like they're building AI infra
1:16:05
SPEAKER_02: and how they're struggling to find that product market fit,
1:16:08
SPEAKER_02: even though they can build faster than ever.
1:16:11
SPEAKER_02: but it has not gotten any faster to get teams to adopt and simple things start
1:16:17
SPEAKER_02: to matter for example talking to a potential customer in person like going to
1:16:21
SPEAKER_02: a tech meetup living in a tech hub or
1:16:23
SPEAKER_02: where you can go more regularly getting feedback getting your first customer inside
1:16:27
SPEAKER_02: a big company and none of these have to do with the code itself and
1:16:31
SPEAKER_02: of course they created something that they think is cool and innovative
1:16:34
SPEAKER_02: and different
1:16:36
SPEAKER_02: But there's now so many things that are similar, by the way.
1:16:39
SPEAKER_02: They all have competitors.
1:16:40
SPEAKER_02: They now need to convince them why they are more trustworthy,
1:16:44
SPEAKER_02: they're worth betting on, and so on.
1:16:46
SPEAKER_02: And a lot of it does have to do with a charismatic founder who is good
1:16:51
SPEAKER_02: at convincing people, all right, try my stuff.
1:16:53
SPEAKER_02: It actually helps.
1:16:55
SPEAKER_00: Yeah, that's one of the things I actually learned from YouTube as well,
1:16:57
SPEAKER_00: because if you're making like a video, try to explain something.
1:17:01
SPEAKER_00: Nobody cares how correct you are.
1:17:04
SPEAKER_00: Nobody cares how smart you are.
1:17:06
SPEAKER_00: Nobody cares, like in the leak code forums, if you have this super like crazy
1:17:10
SPEAKER_00: The Pragmatic Engineer
1:17:25
SPEAKER_00: certain things and I just try to make the video just very digestible whether it's
1:17:30
SPEAKER_00: a DFS or sliding window
1:17:31
SPEAKER_00: or whatever algorithm anybody can technically get it correct you can
1:17:36
SPEAKER_00: at this point have an LLM just kind of spit it out to you but I think like
1:17:40
SPEAKER_00: the human part
1:17:41
SPEAKER_00: of it just knowing like people figure out like what exactly they're actually looking
1:17:45
SPEAKER_00: for what they actually care
1:17:45
SPEAKER_00: about I think that's something that's uh yeah that's pretty important
1:17:49
SPEAKER_02: YouTube is interesting because YouTube,
1:17:52
SPEAKER_02: today at least,
1:17:53
SPEAKER_02: I would hope it's mostly watched by humans,
1:17:56
SPEAKER_02: people, so every single view, I would hope, is a human.
1:17:59
SPEAKER_02: I'm sure there's some bots there, but Google is fighting them.
1:18:01
SPEAKER_02: And it's a real attention economy, right?
1:18:04
SPEAKER_02: the MrBeast who's the most subscribed
1:18:07
SPEAKER_02: or watched YouTuber he captures more eyeballs more people's attention which is
1:18:12
SPEAKER_02: the currency there's 8 billion or so or a bit more people maybe fewer
1:18:15
SPEAKER_02: of them having access to the online videos but it's kind of like almost a game
1:18:19
SPEAKER_02: if it's a game you've been pretty good at it in this niche
1:18:21
SPEAKER_02: which is software engineering
1:18:23
SPEAKER_02: What is something that you've learned on what works in becoming successful
1:18:28
SPEAKER_02: on YouTube where people pay attention to you,
1:18:30
SPEAKER_02: they give you their time,
1:18:31
SPEAKER_02: which is an important valuable currency,
1:18:33
SPEAKER_02: that might be relevant for tech companies, startups especially?
1:18:38
SPEAKER_00: I think a lot of companies struggle with it.
1:18:39
SPEAKER_00: I was speaking to a couple dev relations people that are working on the same thing
1:18:45
SPEAKER_00: where it's kind of a game sometimes where it's a little bit of like politicking
1:18:50
SPEAKER_00: where it's like, you know, like it's about packaging, right?
1:18:53
SPEAKER_00: Like how you say things, how you present things, how you kind of like present yourself
1:18:58
SPEAKER_00: And it's also about, I think, being authentic.
1:19:00
SPEAKER_00: I think that's a big thing that companies maybe don't always get right.
1:19:04
SPEAKER_00: Sometimes they try to,
1:19:05
SPEAKER_00: even with the AI labs,
1:19:06
SPEAKER_00: you're saying that nowadays OpenAI and the Codex people,
1:19:10
SPEAKER_00: they're on Twitter all the time.
1:19:11
SPEAKER_00: They're interacting with people.
1:19:13
SPEAKER_00: They're even interacting with people that sometimes criticize them.
1:19:17
SPEAKER_00: And I think that matters.
1:19:18
SPEAKER_00: That authenticity usually matters a lot.
1:19:20
SPEAKER_00: People
1:19:21
SPEAKER_00: can like smell the fakeness they can they can tell like even for me
1:19:25
SPEAKER_00: if I'm like saying something I don't quite like believe I think it's so obvious
1:19:30
SPEAKER_00: and people can tell then they just get turned off like they're not going to listen
1:19:33
SPEAKER_00: to a word you say at that point and then
1:19:35
SPEAKER_00: so it doesn't really matter what you say you have to like build their trust in a way
1:19:39
SPEAKER_00: that it's hard to build it takes time to get there but once you have it it matters
1:19:45
SPEAKER_00: a lot it matters a lot
1:19:47
SPEAKER_02: It's interesting because I do wonder if clot code had become as big as it has
1:19:53
SPEAKER_02: if it was not created by Boris.
1:19:55
SPEAKER_02: Boris, the engineer who you can see on YouTube channels, he was on this channel as well.
1:20:00
SPEAKER_02: He's a very relatable and I think humble person.
1:20:02
SPEAKER_02: At least that's how I got to meet him.
1:20:05
SPEAKER_02: He is on social media.
1:20:06
SPEAKER_02: He shares how he uses clot code.
1:20:08
SPEAKER_02: There's a lot of Boris.
1:20:10
SPEAKER_02: This is not just a tool that is
1:20:12
SPEAKER_02: like some by the corporate called Antropic no it's actually Boris created it
1:20:16
SPEAKER_02: and he's working on it and he's fixing your bugs and you say like oh it had this bug
1:20:21
SPEAKER_02: and he replies he's in your mentions and I now notice OpenAI it's Codex used
1:20:25
SPEAKER_02: to be this thing built by OpenAI but now it's Tebow who is the head of Codex
1:20:29
SPEAKER_02: and he replies and he does the same similar things as Boris I do wonder
1:20:34
SPEAKER_02: if this you know like the personal angle where it's here oh and Cloud Code is one of
1:20:39
SPEAKER_02: the biggest businesses in the software world in terms of
1:20:42
SPEAKER_02: revenue I think they crossed multiple billions it's hard to track how much
1:20:45
SPEAKER_02: so like it's a huge business tied to a person yeah I hate the word influencer
1:20:51
SPEAKER_00: but it does seem like everything is going in the direction of like even
1:20:56
SPEAKER_00: for companies like they have to be a person now like they have to be
1:20:59
SPEAKER_00: a personality they have to
1:21:00
SPEAKER_00: approachable maybe yeah yeah like approach relatable yeah relatable like
1:21:04
SPEAKER_00: a human right like not just a corporate figure even
1:21:07
SPEAKER_00: for CEOs sometimes like you know it helps
1:21:10
SPEAKER_00: the sometimes it does sometimes it can backfire but I won't name any names
1:21:15
SPEAKER_00: but um yeah I think it's it's funny
1:21:18
SPEAKER_00: and I I think even companies like some companies uh have that
1:21:21
SPEAKER_00: a lot I think Meta you probably know more about this than I do but Meta has like
1:21:25
SPEAKER_00: a internal like Facebook or something where it's like when you ship you have
1:21:28
SPEAKER_00: to like kind of workplace
1:21:29
SPEAKER_00: Yeah you have to like show it off you have to like mention it you have to like try
1:21:31
SPEAKER_00: to like brag about it yeah exactly promote it that's a skill I didn't expect
1:21:36
SPEAKER_00: that I'd ever be you know an influencer or a YouTuber but it's a skill
1:21:39
SPEAKER_00: and I think it's something that everybody should lean in
1:21:42
SPEAKER_00: towards like not everybody has to do YouTube but whether it's like LinkedIn
1:21:45
SPEAKER_00: or Twitter I think it's worth you know putting yourself out there
1:21:48
SPEAKER_00: and slowly forming opinions and like interacting with people and things like that
1:21:52
SPEAKER_02: Yeah, well,
1:21:53
SPEAKER_02: you said like maybe not everyone has to do it,
1:21:55
SPEAKER_02: but related to this,
1:21:57
SPEAKER_02: you've had a pretty contentious hot take,
1:21:59
SPEAKER_02: which was some people should just give up on tech careers.
1:22:02
SPEAKER_02: Let's talk about like that.
1:22:04
SPEAKER_02: It's a pretty big statement.
1:22:07
SPEAKER_00: That's a very strongly worded statement and I definitely don't encourage people to give up.
1:22:13
SPEAKER_00: So I want to make that clear.
1:22:15
SPEAKER_00: The only reason I suggested that even in the title was that I think if you have
1:22:21
SPEAKER_00: an attitude of like you don't want to try hard or you don't like you don't want
1:22:28
SPEAKER_00: to do things yourself and you don't want to dig deeper into things.
1:22:32
SPEAKER_00: like you need to do that you need to do certain things and if you're not willing
1:22:37
SPEAKER_00: to do that I think you should know like what you're getting yourself into because
1:22:41
SPEAKER_00: a lot of people don't know like they go through these college degrees like they kind
1:22:45
SPEAKER_00: of just cheat their way through it and then they expect to have a job at the end
1:22:47
SPEAKER_00: of it and I think you have to evaluate that if you're going to be one
1:22:51
SPEAKER_00: of those people that does that because it it might not be the best
1:22:55
SPEAKER_00: for you people have unfortunately just gotten into the habit of doing it like
1:22:58
SPEAKER_00: when I made that video I had a lot of people that were
1:23:01
SPEAKER_00: Pissed off at me.
1:23:02
SPEAKER_00: But surprisingly,
1:23:04
SPEAKER_00: the vast majority of people,
1:23:06
SPEAKER_00: they said that maybe I could have been a little nicer, and I think that's true.
1:23:08
SPEAKER_00: But they actually agreed with a lot of my points.
1:23:10
SPEAKER_00: A lot of people said that, you know what, you're right.
1:23:13
SPEAKER_00: I realized I was going too far in the direction of just prompting things
1:23:16
SPEAKER_00: I'm not trying to offend anybody when I did that.
1:23:31
SPEAKER_00: I left that video up.
1:23:32
SPEAKER_00: Maybe I took a reputation hit from that.
1:23:34
SPEAKER_00: I'm not sure, but I left that video up.
1:23:46
SPEAKER_02: Yeah, but I think it goes back to effort, right?
1:23:48
SPEAKER_02: Yeah.
1:23:50
SPEAKER_02: And as advice,
1:23:51
SPEAKER_02: what would you advise for software engineers,
1:23:54
SPEAKER_02: early career,
1:23:55
SPEAKER_02: mid career,
1:23:56
SPEAKER_02: maybe even later, who are either working at a company and they just want to be
1:24:04
SPEAKER_02: when you think of the standout engineers that you work with either at Google or
1:24:07
SPEAKER_02: the people who you know, or they're working at a startup.
1:24:11
SPEAKER_02: What do you think it takes today to actually increasingly stand out from
1:24:15
SPEAKER_02: a crowded space to have your work speak for itself?
1:24:19
SPEAKER_02: What does that mean?
1:24:20
SPEAKER_00: Yeah, I think it's a really, really good question.
1:24:22
SPEAKER_00: I think there probably is some like general advice that would apply to like most cases.
1:24:28
SPEAKER_00: I don't know if I can think of that.
1:24:30
SPEAKER_00: So what I'm going to say is I think, like you said, like the effort matters, like even in
1:24:37
SPEAKER_00: You're not just living in your own head, taking this standardized test.
1:24:43
SPEAKER_00: You're talking to somebody.
1:24:44
SPEAKER_00: You're seeing how they're reacting to what you're saying.
1:24:47
SPEAKER_00: Maybe they're not on the same page.
1:24:48
SPEAKER_00: There are certain shortcuts you can't take.
1:24:51
SPEAKER_00: You're at a company Know the people around you Know what they care
1:24:54
SPEAKER_00: about Ask them questions Have meetings with them Don't just make assumptions
1:24:57
SPEAKER_00: If you don't have to Talk to them Figure out what's important Get a sense of things
1:25:03
SPEAKER_00: And then go very hard In the direction that you think is correct Maybe you'll have
1:25:07
SPEAKER_00: to recalibrate things Maybe you'll go too far Maybe you'll make some mistakes
1:25:11
SPEAKER_00: But I think it's just kind of like That iterative process of like
1:25:15
SPEAKER_00: that feedback loop of like, okay,
1:25:16
SPEAKER_00: figure out what you're supposed to be doing and then work very intensely to do that.
1:25:21
SPEAKER_00: And then just keep doing that.
1:25:22
SPEAKER_00: It requires a lot of changing.
1:25:23
SPEAKER_00: It requires a lot of like course correction.
1:25:25
SPEAKER_00: And that's difficult.
1:25:26
SPEAKER_00: Nobody wants to do that.
1:25:26
SPEAKER_00: Like one thing I always did with my manager, I always ask them like,
1:25:30
SPEAKER_00: if you have feedback for me please tell me like I will not get offended I want
1:25:33
SPEAKER_00: to know I want to know like what I could be doing better and
1:25:36
SPEAKER_00: the way my like all my managers ever reacted to
1:25:39
SPEAKER_00: that is they were very surprised they're like well first of all if you just joined
1:25:42
SPEAKER_00: the company you're asking this like that's a very good question to be asking
1:25:45
SPEAKER_00: because like that tells me that you actually care and you actually want to get ahead
1:25:48
SPEAKER_00: so I think like that aspect of it that matters a lot because then people know you're
1:25:53
SPEAKER_00: on the same page they kind of know about you they don't have
1:25:55
SPEAKER_00: to guess what you're thinking in your head you're kind of like communicating that
1:25:58
SPEAKER_00: with people
1:25:59
SPEAKER_02: Yeah,
1:26:00
SPEAKER_02: so a trend that I felt coming through our conversation is we just keep coming back
1:26:04
SPEAKER_02: to it is effort.
1:26:06
SPEAKER_02: And don't take the shortcuts.
1:26:09
SPEAKER_02: I mean, take the shortcuts when you have like a business outcome,
1:26:12
SPEAKER_02: especially when you're building your business or you have a goal that you're going
1:26:15
SPEAKER_02: to achieve, but otherwise just put in the work.
1:26:18
SPEAKER_00: It sounds simple.
1:26:20
SPEAKER_00: It sounds really easy on paper, but it's hard to put into practice.
1:26:23
SPEAKER_00: Especially for me, I am not a person that likes change, actually.
1:26:26
SPEAKER_00: I am very resistant to change.
1:26:28
SPEAKER_00: I hate change.
1:26:29
SPEAKER_00: It takes me years to change.
1:26:31
SPEAKER_02: You still have Angular on your website.
1:26:34
SPEAKER_00: but it's so important and it's so worthwhile when you actually do it
1:26:39
SPEAKER_00: and I think it just matters a lot and it's like it becomes a way
1:26:42
SPEAKER_00: of life like you start like I put in a lot of effort on the coding side and then
1:26:46
SPEAKER_00: in professional life I realized okay like the soft skills matter a lot took me
1:26:49
SPEAKER_00: a long time to learn them I'm still learning them to this day but that's
1:26:53
SPEAKER_00: the reason I probably got promoted that's the reason my like my team liked me
1:26:56
SPEAKER_00: and it's important to be likable like you don't want to be hated
1:26:59
SPEAKER_02: And as closing, what are places that you get inspirations from?
1:27:02
SPEAKER_02: This could be books, this could be videos, this could be YouTube channels.
1:27:06
SPEAKER_00: Yeah, I think you just had Martin Kleppman on.
1:27:09
SPEAKER_00: I'm a huge fan of him, huge fan of his book because he goes deep.
1:27:12
SPEAKER_00: And even as deep as he goes, then you'll have a hundred references at the end of every chapter.
1:27:17
SPEAKER_00: And I just love that because I'm the type of person, I just always have a follow-up question.
1:27:21
SPEAKER_00: I always want to go a little bit deeper to understand things.
1:27:24
SPEAKER_00: And so seeing people like him
1:27:27
SPEAKER_00: like I relate more to like scientists people like PhDs researchers more than I do
1:27:31
SPEAKER_00: to engineers so I just really like that I think it's important to have people
1:27:35
SPEAKER_00: that you can like look up to and aspire to be like and I think there's plenty
1:27:40
SPEAKER_00: of people I take inspiration
1:27:41
SPEAKER_00: from including other YouTubers including technical people including people I've
1:27:45
SPEAKER_00: worked with in the past and I always look at a person and I try
1:27:49
SPEAKER_00: to see like qualities about them that I really like and then I try
1:27:51
SPEAKER_00: to like replicate them and imitate them as well
1:27:54
SPEAKER_00: Neat, it was awesome to have you here, especially in person.
1:27:57
SPEAKER_02: Yeah, it was great.
1:27:58
SPEAKER_00: Great meeting you.
1:27:59
SPEAKER_01: I'm glad we finally got to record this episode with Neat.
1:28:02
SPEAKER_01: I love how honest he is and I hope this came across as well.
1:28:05
SPEAKER_01: I found it interesting how Neat hires these days.
1:28:07
SPEAKER_01: He runs one of the biggest coding preparation sites and then he hires
1:28:10
SPEAKER_01: for skills outside of coding.
1:28:12
SPEAKER_01: He cares about motivation, whether the person
1:28:14
SPEAKER_01: can explain their own thinking, and most importantly, for agency, or for getting things done.
1:28:20
SPEAKER_01: It was also nice to talk with him about how he believes
1:28:22
SPEAKER_01: that companies have no idea how to validate candidates, and they probably never did.
1:28:27
SPEAKER_01: The lead call-style interview survived not because it predicts job performance,
1:28:31
SPEAKER_01: it just doesn't, but because nobody has found anything better that scales.
1:28:35
SPEAKER_01: And finally,
1:28:36
SPEAKER_01: I appreciated Neat's observation that the effort is becoming
1:28:39
SPEAKER_01: a differentiator exactly because AI has made everything else cheap.
1:28:43
SPEAKER_01: Anyone can prompt a design,
1:28:44
SPEAKER_01: a feature,
1:28:45
SPEAKER_01: or an answer,
1:28:45
SPEAKER_01: but what you cannot prompt is caring,
1:28:48
SPEAKER_01: and your ability to defend your choices when someone asks,
1:28:51
SPEAKER_01: why did you do it this way?
1:28:53
SPEAKER_01: Do check out the show notes below for the related Pragmatic Engineer and deep dives
1:28:56
SPEAKER_01: that go even deeper into tech interview-related topics.
1:28:59
SPEAKER_01: If you've enjoyed this podcast,
1:29:01
SPEAKER_01: please do subscribe to our favorite podcast platform and on YouTube.
1:29:04
SPEAKER_01: A special thank you if you also leave a rating on the show.
1:29:07
SPEAKER_01: Thanks, and see you in the next one.