Why Resumes Don’t Work Anymore (And What Does)?

hi,

Yep.

Yeah. Nice

Hello, Angelina.

Yeah, yeah.

a while. Yeah, it's been a while. So, thanks for coming to, to my podcast, today. and I, I, I really, you know, I, today I wanna ask you about. you know, I want to have a discussion with you, especially around the job market and where roles was related to data science and engineers are going, especially in this age of a I everybody. I know that I have a lot of friends who are. who are looking for jobs and they're having a hard time, and, you know, there are layoffs in big companies, and also, you know, freezing of hiring of a lot of the roles. So, even when, even after we pass the new year, right, this is 20, or Q1 2025, I'm not seeing, like, it's more opening Like, as

Oh, definitely not. Like,

right?

just the start.

So, so I don't want to, you know, put formal of the podcast. [00:01:00] So I want to ask you several questions around this topic.

Of course. But I think you got to the right person, right? Because, I, like, I have the YouTube channel, I have the community, and I work at a startup. So I'm connected with a lot of data scientists that I heard here, like, like, similar horror stories as you do. But I also see like success cases in our community.

Like if you check the community post, there is someone who has been, who finally landed a job after like half a year of searching. So I see some success case and my company is actually doubling headcount. And each position we post, we get over 3, 000 applicants. So I can speak to that as well a little bit.

right? It's like, like five years ago, it's not going to, you're, you're not getting 3000 applicants or even three years ago.

Even a year ago, like even I think a year ago when we post, it was still already a tough job market, but I think we got like, several hundred [00:02:00] applicants. I think AI changed the job market. and LinkedIn has changed. like if, if we interested in the details, we can do that as well. But I also did an interview with, the hiring manager at Databricks.

And, he offered the opinion that, because everyone. today is applying to everything, right? So the signal, the precision recall, just, drastically changed. That's why the traditional, job, traditional method of applying for a job doesn't work anymore because, there is just no signal value from this process.

Right, right. Maybe, maybe later, later for this, during this talk, you can share a little bit, like, what, what, what should we do right now? So, yeah, my, my, my first question is like, where, where, where do you see data science or engineer roles heading in the next one to two years? Are companies shifting focus focuses from this, like traditional data science or software engineer to our more AI specific roles, or are they like merging these skill sets? [00:03:00] share your your your observation? Yeah,

Of course, I think I hear this, like, people complaining, like they are data analytics, and they try to switch jobs and every position they can find, the company as for large language model, related experiences, like, even if for analytics, if you don't have such experiences, it's hard to even find that opening.

I think that is, that reflects how the industry is going, right? The industry is going to AI, the money, the B2B spending, the company budgets are going to AI. And, if you look at new startups, the most successful ones, like lovable, cursor, they're going to AI. So, so, and big tag, all the. Like new initiatives or the visibility goes to AI.

So I think that reflects kind of the market trend or just where the money goes, where the value is going to be created, or at least where [00:04:00] the demand is. But meanwhile, I don't like the framing of the question. The framing of the question is typically, like, how would data scientists, how, how does the role of data scientists change?

Like, how, how should data scientists adopt to the new, dynamic, like, right? But, so let me actually ask you, what is the definition of data science? What is data scientist?

Hmm. So traditionally, I would think that data scientists are doing like, you know, data, you know, including, should we include data engineer? I'm considering the whole data pipeline. Of traditional, you know, the data function that you include, you know, etl of the data and then transformation of the data into some like analysis ready. model ready data set, and then you either do analytics on or you either do like models on and then for some predictive models and things like that, right? and, after we have, like, machine learning engineers [00:05:00] rose like a few years ago, right? The, the, the rise of machine learning engineers, then, MLOps becomes also important to, you know, becoming part of this, this, this whole pipeline, right?

From, from pulling the data all the way till like, you're going to have. operation side, like DevOps kind of thing, roles for MLEs. so data science roles have been like enriched over time, right, for all those functions. But now I think it's adding, adding new flavor, like the AI flavor to it. I just don't know how, severe it is.

I, I became like, I, I definitely, like, I, I think four years ago, I was trying to introduce, like, what are the different type of data roles at Amazon? Like, they have, I think, 10 different titles. like, I was using this approach to describe the role of data scientist. Now, I became kind of critical of this kind of definition.

Because it's fake. I think it's fake. Like, like, if you think about the origin of data science, it's [00:06:00] definitely a made up position. Like, I think the history of the, the, the role is like 20 years at most, right?

It's statisticians, right? I am a statistician major, right? So

Yeah. Like a statistician, like even that, like the data scientist, the term doesn't exist 20 years ago. So it's a made up role. And, Like, for example, when you talk, for example, just a builder, right? so a construction worker, there, like, if you ask a construction worker, what do you do? They say, like, build houses.

They don't say, like, I yield hammer, or I use, a saw, right? They don't talk about the tools. They don't talk about, they talk about the results. So what is the result of data scientists? I think that we do. The result of the data scientist, unless you work on production, system, you just influence people.

You just help make better decisions. That's all.

Informed decision is a common words in in job descriptions, right?

Yeah, you make decisions, like, yeah, yeah, the [00:07:00] entire value of data science is, without the data scientist. This company does the worst decision with the data scientist. The company does a better decision than the delta of these two decisions is kind of the value of data scientist. So

with that, like, I don't care.

Like, am I using sequel? Am I using python? Am I using natural language models? Because today. Most of my SQL and Python are written by our language models. so why should I care about which, which two do I use? I just care about, what kind of decisions am I actually influencing? Can I actually, help make even better decisions?

Can I work on more important decisions? Can I influence more people? That kind of, that, I think that speaks more directly to the value.

So, it's more like performance driven now, so people are focusing more on ROIs. Historically, it's very hard to judge the ROI for a data team or data science team.

I think that was a luxury and that, that might be. because of power building, right? [00:08:00] Because at big companies, everyone wants to hire more people to get promoted, and you want to be quantitative and you want to play it safe. the the, the best way to play it safe is to let numbers speak. You don't hold any responsibility of making decision.

You just less numbers make a decision for you. Then you hire data scientist to justify it or whatever.

Right,

you don't have to actually, Justify the value of this data scientist.

right, right, yeah, that's true. But today it seems like it's changing, right?

I think because companies. Are disrupted, like especially like those profitable big tech companies, they feel a sense of urgency that wasn't there like three years ago, right? Because of because of the macroeconomics environment, because like histories, like, yeah, like they are going to have earned so much money, they have such a growing hyper growth business model, [00:09:00] they have all the free cash flow, and then they can afford to hire a lot of people and hire a lot of people.

Always the dream of, this like hiring more people leads to more growth. Maybe some domain is true, but for example, like look at Google or Manhattan, like the more people they put on a problem, the slower the progress. So, more people is not going to lead to more productivity in the long run. And, yeah, they need to do some, in reversion, they need to adjust

Well, why do you think the big techs are not hiring now? What's the

real, true, real,

Because of the pendulum, I think, because they were in power building because it's just a cycle, right? Like before they were over hiring like not it's not like they are slowing down But this is they don't need that many people like if you cut 50 percent from Google from Facebook I think they'll function even better

So they don't need, [00:10:00] they're going to work, function.

Suppose there is no moral hit. Right. But, yeah,

Right, right. So do you think that, you think it's, there, there's a possibility that, well, as you said, they're going to come back potentially. So for what reason they could come back in hiring again, like the big companies or the whole industry?

I think, I think the whole industry can hire more, but it's not the big tech, the big tech, like, unless they reinvent themselves, they're just going to like the reach to maturity. Like they're going to stay there for decades, but, I don't think they're going to grow. Like, if they're not going to grow, then they're not going to hire, like, aggressively.

So, like, it's going to be new companies that hire a lot of people. Like, if you look at OpenAI, if you look at Anthropic, they are, like,expanding aggressively, right?

Okay. [00:11:00] So let's not put our hope to, towards the traditional like big tech companies, maybe because of AI, right? There's a lot of rising stars in all those new companies, that potentially the traditional data science to data scientists or engineers could could actually go after today. Right? So, so what do you think? what do you think, like, the, the, the new grads, or just in case, like, people get laid off from big tech, what can they do, like, if they were, like, data sciences roles, right, or software engineer roles,

Yeah,

where can they go, like, for the, for the, for the newer companies? I think,

like, the mindset, right?

Go ahead, go ahead.

yeah, if they're used to the old way of looking for a job, which means like you summarize what you did, right? And then you find a template and try to improve your resume. Once you have like a polished resume. You go for the numbers, you apply for other jobs that are available on the market. You spend 10 seconds per [00:12:00] job per application or 30 seconds per application.

It's not going to work because every position is going to receive like hundreds, thousands of applicants and their resume all look the same. Everybody's going to have the same generic resume. That says, you know, I drive this much impact with this model or whatever. So you're not going to stand out. and my advice is, I think there are two folks.

One, look for quality instead of quantity in today's day and age. Because everyone is generic. So

the status

reverse,

have to look for the position you want to go to first.

building

any product, right? You want to look for the market.

like finding the product market fit is not like I just built in a vacuum

to find the market.

The first step is I need to understand the market.

a small bit of

In the first process I described, they spent like zero,

hours understanding the market. So [00:13:00] actually need to understand which companies are hiring. What I'm good at, which positions I want to apply is if realistic and then schedule like, like connections, coffee, chat, use your own connection or whatever.

I've tried to understand as much as possible. That's the inside information, do research, watch episode or watch YouTube, listen to a podcast, listen to whatever, like I'll just learn these roles and to learn their specific requirements. What kind of people do they, do they need? And then try to organize your past projects, experiences towards this requirement.

And if there is a gap, go fill the gap. Like if they need

specific domain knowledge, try to learn that specific domain knowledge. If they need, like for example, certain skills, learn that skill. And put on a resume, right? Ideally, you want to learn the skill while building in public. So you have a track record that the interviewer can just check and understand your quality.[00:14:00]

I think once you do that, your success rate is a thousand fold. And I'm not kidding because Most of the resumes we receive today, we just throw out without even taking a look because they're too generic. Clearly, like that candidate use AI to apply and never look at our job, job description. even if you just spent like five minutes to look at the job description and write, write, you know,

don't know,

resume,

one

of success increase 50 fold

fold.

So like, and then if you do like, bridge the gap and, like, to the insiders, I think it's very easy to get another, like a tenfold of, success rate.

it's much like I think the time spent on understanding the market and developing the proper skills, not playing the numbers game, but, you know, be very specific on what you want to apply,

well.

meanwhile,

[00:14:58] Yuzheng: you do get the job, it's [00:15:00] the kind of job you want to go to, go to, right? Like the first approach, the problem is you're probably going to end up in a bad job because that's kind of the, the, the worst job that can match with you.

then, then you're probably not, not, not going to be happy there because you know nothing about the job going in.

That's the only job you can get to.

you'll be miserable. The second approach, the job you get into, like you understand. You'll be happier.

How many, how many, resumes are you receiving now for your roles at StatSig,

Like, I'm not a higher manager, but, 3, 000 per row.

per role? 3, 000 per role. How, how, how do hiring managers are sifting through that resume to find the high quality ones?

We use high precision filters, like for example, first, are you local? Because we are an on site company, you have to come five days per week. Or do you, [00:16:00] like that screens out a lot of candidates. and, do you have the relative like we have criteria is like to have the relevant experience. if not, then that screens out a lot of candidates.

and, then we look at, for example, the application like, to do actually read our job description. If not, yeah. So in the end, like only. Less than a hundred applicants can actually, you know,

back.

to take a serious look like for the hundred applicants, then we spent like two to five minutes per applicant to actually read their resume.

Oh, okay. So, is this an internal tool that you're using? Because it sounds different, like the filtering system.

I don't know. I think from the way it was described, I feel like it's an internal screening. Maybe they use some too. Maybe they use internal too. I don't think it matters that much.

Okay, okay. I [00:17:00] see. Because I was wondering if, I want to ask you, does the resume still matter? Like, because I'm competing with 3, 000 people, right?

Yeah, it matters. Oh, yeah, it matters, like the quality of the resume matters, like you don't want a resume that is the same as, that's me, like the analogy is like online dating app, right?

Hmm. Yeah.

online dating app, like

me, I

most of the people actually look the same,

of messy.

there are like 1 percent of the people that actually are very popular.

You want to be that 1 percent of people by actually learn how to make a good resume. Like, it's very easy to spot a good resume. The good resume can stand out. In like a hundred or a thousand resumes, there are not that many, especially a resume that speaks to our industry, [00:18:00] speaks to our company. Like it's very easy to, yeah, spot.

What are other to be, to become the popular 1 percent other than, you know, polishing your resume, do the, do the homework and stuff like that and find your own interest?

So now Polish our resume. So first understand the market, then Polish our, like build our project and then update our resume accordingly. So the step is not first Polish our resume. Polish resume is the last. But, but, the other thing is, like, do some AI projects,because since everyone is interested in AI, of course, if you can be an expert in this field, you have a better advantage.

And the good thing is, everyone kind of start at the same time, right? There's no people with 10 years of, larger language model experiences. Most people have about two years. When they put on their resume and you can easily prove that you actually understand large language [00:19:00] models by building some projects and posting on GitHub.

Like, did you actually build a project to do actually overcame, overcome a lot of, challenges to do actually have some unique insights or do you have a GitHub that has commit every day to do actually make a working product. Like those are some low hanging fruits that I don't see many people are doing.

Is this something I should put on my resume and, know, publishing the resume itself,

Of course. Like, like, but, but I mean, depends if you are, if, if the company you are looking for do not care about ai, the, the no. But, if company you care about,if the company cares about ai and if you. Developed a credible project when I say credible project, I'm not saying like class project.

Don't do class projects. Just go to YC company. Like, this is, advice that I heard, go to the first, the latest YC batch, right? And look at what [00:20:00] we do. Most of their, ideas. you can do it by yourself. then try to, just do that.

Yeah, that's a really good idea. Right,

legit, like, like legit projects. So don't work on fake projects. The class projects are projects

right. I'm just wondering at what point I can showcase, like, let's say you're the hiring manager. I'm trying to apply a job on your team. and I've, let's say I've had industry experience. I have my work experience. And I'm doing some side projects on the side, I put both on my resume, which one would really pick interest?

pick your interest. we are, it'd be testing of platform. So for, if I'm giving advice for someone who are applying to our company, yes, I project better to be about AB testing. If you can successfully apply AI in AB testing. In, like a useful way. [00:21:00] Right. That certainly would get a lot of, attention.

that's a good point. So, so again, this market research is important. Like, are you really interested in what you're applying to? there's a, there's a, there's a, time needed, right? For, for doing that research and building up your project. So what, what do you, what, what's your suggestion for people who are out of a job right now?

So they could spend like three months doing that research, basically find like employee market fit kind of thing

Employee market fit. Yes. Yeah. Like, applicant market fit. I think regardless they're spending a lot of time, right? You are talking about, like, the people. I'm like the people who are going to receive this advice. Other people who are going to spend a year. looking for a job, but cannot find one.

So they have the time. It's not like they need to. The alternative is they can get a job in a day. So they have the time. I'm just, [00:22:00] suggesting how to prioritize and how to allocate your time. The allocation should be spent at least 50 percent understanding the market. Right. And then spend it, spend the rest, like 30, at least 30 plus percent on developing projects, enhancing our resume, and then 10 percent on polishing our resume and 10 percent on application.

the reverse, like most people do it by like spending 30 percent on, polishing the resume, but there is nothing to polish. Like, you just change the word that doesn't mean anything. then the

know.

just hit apply.

It's just a waste of time you are becoming more valuable in this process, right?

Right, right, right. I can imagine that somebody can build something like this, like, you know, keep hitting on applying, and then you can just [00:23:00] compare your resume with a job description and try to see if you can adapt automatically. And then you can see, right? And then somebody can actually build something like to automate a little bit of that, because applying can be very time consuming as well

There are so many like AI tools. Like, it's yeah, yeah. I don't like that affirmation because, you

you can hallucinate so for sure

know, because everybody else is doing the same thing. So you are making yourself just the same as everyone else. And in a competitive market, you are not going to get picked by doing the same thing.

that's I feel like it's almost the same thing as if you're building a product you're looking for how do you differentiate yourself, right? If I'm a data scientist, how can I find my unique value?

I think everyone [00:24:00] is unique, right? Everyone, the definition is unique. And, because we conform to the kind of,best practices or just how we think other people think, we became more generic. We start off very unique and we became generic, like your passion, your own projects, how you go to market with our product, how you build a personal brand, like how you choose to handle like decisions, like they are all different.

So I think intrinsically very different.

Right, right.

we all try to make our resume look the same, which is

right. which is bad. Unfortunately. Yeah, we are like cookie cutter, but we were trained and told to do that. Like, you have to have your resume that look like that.

who said that it's bad coach. It's medical track.

that's a better advice.

right. That's what I heard, [00:25:00] like, from, like, when I was in grad school, and then the student counselor, like, you have to write your resume one page and has to look like this. You know what I mean?

I think format. So, so I think there are two, like those are advice I don't, I I'm not against. So first you want to follow a certain format. I, I certainly would advise like, just do one page, right? Don't write five page resumes and follow certain formats. I think formats, mix the, like the architect there are like, there are going to be.

A best way to format things and you should use the best way. Don't try to, do too much. you mentioned here is the substance that matters. The substance should be different. The projects you work on, like your experiences, their passion, like how you, describe yourself. That should be different, but the format I think should be the same.

And the second, remember the device changed because of the market environment, like. Even three years ago, there is a huge,like there is much more demands than supply. And today there are much more supply than [00:26:00] demand, or maybe it's a better market. Maybe it's much more supply. So the past experience, like the device that works three years ago, doesn't apply today.

Mm, that's true. Do you see titles change?

I don't, I don't really care about titles. Like, right.

title defines those schools.

The titles, the titles are, are made up. So,nave says this, so there are three skills that, are, are real, build, sell, and, get lucky. So like, are you building, are you selling, are you, trying to, get lucky or helping others to get lucky?

yeah. but, whether you are data scientist, oh, and there are three types of, kind of types. Entrepreneur, technician as manager. So the three by three matrix gives you kind of, where you map, and then you have some expertise, right? Yes, you can, you can, you can develop expertise, but I don't want to, limit our thinking in job [00:27:00] roles because like we are chasing a vanity, target.

Right. Maybe I'm still in the old mindset of, if I'm applying for jobs, right? The way I do, do which, which roles I apply is by searching for the title, right? Once I have the title, then I can search, okay, a hundred, a hundred jobs opening, and then I can click on each one of them and hit apply

Yeah,

that is the language of the language is important. I think what you do is valid, right? Like, you should apply to kind of a certain definition of the job that resonates the most with you or aligns the most with you, but know that the language is fake.

Language is just a, yeah, just, just, yeah,

It's just language.

don't know how to describe it. Yeah. mentioned the skill of getting lucky or helping others to get lucky. How do you do that?

Oh, glad you asked that.[00:28:00]

Navao, I think, characterized that into four different kind of luck. Only, blind luck, that is something you cannot control, right? You just got lucky. the second one I think is, luck by, like developing better judgment. So when there is an opportunity, you can see the opportunity, others cannot see that opportunity.

So that's you being more lucky. and the third one, I think is luck, luck by trying. Like, for example, if you are going out fishing every day, you're going to get more fish. If you cast the net,

going to get more fish. So by just, by being more diligent, get more lucky.

the fourth one I think is attracting,like the lack of attraction or whatever.

suppose you are the,

this campaign.

I think he's, he's,

I'm really

was,

suppose you are the world expert in. Diving into like a sink ship to find treasures and then someone discovered a sink ship, they're going to call you, right? Like, you don't, you just sit at home [00:29:00] and opportunities will come to you.

You develop this reputation and, yeah, other people will come to you. So the first kind of lucky, the blind lab cannot be developed, but the rest three, you can actually, get better.

The last one is really interesting. Basically, you're talking about inbound. So can we, as a, as a people who are seeking for seeking for job opportunities, get inbounds, right? I'm sure you

All the time,

all the time, right? So

like

can you

suppose

and get more inbounds?

you develop interesting projects, you actually study how to make it better. You publish a github and the github people found it useful and you get a lot of stars. We all get have got a lot of stars, you get opportunities and or suppose you publish. you know, building process and share your insights.

I suppose your posts are actually insightful and help others get more opportunities. or, okay, by, [00:30:00] through the process, you develop certain skills or develop certain, you know, you, you, you, you, you, you found a new way of solving things. You can, you know, tell others, you can help others, because you are, you can actually solve the problem, you get more opportunities.

Right. And you have to showcase that you have the ability to solve the problem to the world. So not just to your employer, but to the whole wild world, right? That's the, that's different. Like you being a YouTuber, right? You're telling everybody you're changing, you know, you're, you're, you're teaching all these mindset shifts and things.

So

The reason we thought we don't need to tell the world is because we were used to getting exams. Like in exams, you don't need to broadcast. Everyone gets the same exam and everyone is judged by the right answer. And other things are more valuable. But, in reality, nobody is going, going to give you an exam [00:31:00] and, hold the standards, make it fair for everyone.

You have to be the one to, you know, showcase our work and, broadcast for ourself. Otherwise, who, who, who, who would know, like, how, how do you know anyone, any company that is doing amazing things? No. Unless they actually tell you.

right, right. That's a huge mind shift, too, as well, especially for, like, the technical maybe population. know, the probability of being a little bit more introverted who

introverted technical people are trained because of the quantitative school and the quantitative in quantitative school. We kind of look down upon the people who, you know, broadcast themself because,

about

yeah, you don't have to because I'm the best at doing this exam. My score shows that I'm the best.

No, I don't have to do that. But that's just a mess up system is teaching all the wrong things.

right, right. What do you think? what do [00:32:00] you think are the most important skills to have in this age of AI as a, let's say, you know, trained machine learning or data scientists or software engineer? What should we think? What's the mind shift? Say that again? Build, sell.

Build, sell lucky, like, pick one that

luxury. Okay.

like, you, you are not gonna do all three. Well, but, pick one that actually you can do. Well,

Yeah. Yeah. I, I, yeah. Yeah. I, I, I think it's,what percentage do you think is, let's, let's say with allocated percentage to these three skill sets, are these 30, 30, 30, you think the importance, or if you can do one of them, like 100 percent really well, that's good enough.

you remember I'm doing the Maven course with, yen. Right? With

Right,

he is definitely the build. I'm definitely the seller. And I, I think without me. [00:33:00] Like if he do the main course by himself, he'll have all the material, but, when tense at most, the, the revenue, if I'm doing it myself, without his, material, maybe one tense myself.

So combined, builder plus seller gives us 10X the return, more than 10X the return, I think. So, so

great example.

yeah, each of us are like, Necessary conditions. but together. yeah.

Speaking, sorry, I have to calm my cough. speaking of your Maven course offerings, because I know that you have really, you're one of the most popular AI, you know, features on Maven, and you have really well received the courses. And I think you're having, Multiple courses. Now you want to share with with my with our audience that to set a I because our audience are, you know, data scientists, engineers and developers, builders. Yeah, we want to have this mind shift off, [00:34:00] especially, know, learn a little bit about cells and to get our own luck.

Oh, nice. Nice. God, you mentioned a bunch of message shift. so there are two things we teach, in this course, first, just how to use AI and, behind how to use AI. We, through the applications, we teach the mindset of how you should work with AI. For example, you should actually act as the manager of AI.

You should understand how it works, but you should try to make AI as automatic as possible and try to get out of the way. Like, and don't overdo things like there are a few best practices and man, a mindset we teach. And I think our students found this particularly useful because even students from that we taught a year ago,

they can still keep up with

latest development.

Of course, we have this community and we share the latest community,development and we also like, keep updating our course [00:35:00] and all the past students have

access to our future course.

But still, like, because they have the mindset,

they can always.

Catch up. Versus if you learn certain tricks, like, for example, if you learn long chain, then what's the use of that?

Like

tomorrow is going to get obsolete. So the math set is important. But even beyond that, because AI is a paradigm shift. And in any paradigm shift, it's very important to understand what the paradigm shift is and how to deal with the paradigm shift. and the way to deal with paradigm shift is building.

So we teach the builder's mindset. That's kind of the overarching theme of our course. the proposition is unless you are building otherwise in this new dynamic, fast changing,

Yeah. Yeah.

there's so much information overload, and there's so much cognitive burden, like you have to unlearn to learn a new thing.

[00:36:00] Yeah.

develop first a sense and a judgment and kind of a feedback loop. Because, for example, GitHub project, right? Suppose you are working on a GitHub project that propels you to learn, because by learning you can make your projects better, and you get more stars, more opportunities, more returns, and that propels you to learn even more.

So if I were to ask you to use one sentence to summarize the core, idea of your course, how to keep updated in this fast paced world today.

The builder's mindset. Building.

basically building.

Yeah, building with AI.

And don't chase, don't, there's no need to chase the latest AI or hype.

We are chasing it for you. So we distill the information for all our students, then you can stay focused on building. And whenever you have questions, like for example, is this new model better? We'll give you an answer. We have like a community [00:37:00] and we constantly share. Yeah. So we help you distill the information.

Awesome. So this sounds like a really great community to be part of as well. And, for the, for the, for the, for the benefit of our audience, we'll share the link of the, of your, of, Eugene's course and also the community, the community. and then if you have like, you know, discount code share with us

Oh, yeah, sure. Happy to provide a unique discount for your audience.

Yeah. Sounds good. Thank you so much. I don't have more questions. Thank you so much for Eugene. I really appreciate you sharing your very. You know, a unique perspective. I think you're one of the most unique perspectives in the space of AI in terms of like, you know, having the background of both data scientists, machine learning engineer and in data and in like, you know. A B testing as well. So I really like the I really like how you I feel like you are one of the person who has jumped out of the box. So few people have done that [00:38:00] and you're one of them. And that's why I want to share your perspectives with with our audience as well. Thank you so much.

Thank you for having me.

Did I miss any questions? Anything else I should ask?

Oh, no.

It's more casual chat. I have only one question. I asked you the first question, that was the only one question. And I just go with the flow. Yeah, I like your style.

Yeah.