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Hey, folks, welcome back to another episode of JavaScript Jabber.
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This week, I'm your host, Charles max Wood, and I'm
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here with Itamar Friedman.
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Now it tomorrow.
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Do you want to let people know who you are
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and what you do? I see your shirt it says quoto,
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So you want to talk about what they do? And yeah,
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and then we dive in and talk about whether or
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not to trust your age AI generated code or code review.
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Really happy to talk about that topic and being here,
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Charles like, really a pleasure. Developing community are really awesome,
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especially as they're more.
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Specific you get into the details. COODO stands you mentioned.
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The name CODO stands for Quality off Development, and the
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most most focused of our platform is around AI code review.
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In general, we deal with quality, different AI quality workflows,
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et cetera. Basically helping enterprise professional a depth teams to
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standardize their quality via the review process or.
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Shift left, cold review and testing.
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It's okay, yeah, serving for example thousand around the world.
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But I think, like really really exciting, for example, just
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to talk is cold review important and all right now
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and in the future.
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Forget about CODO. That's I think like a cool topic.
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Yeah, absolutely, Well you know, you kind of hit on
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something that I think a lot of people are talking
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about with the AI stuff, you know, whether it's a
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code review or AI generated code. Maybe it's AI generated
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code that's code reviewed by AI.
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I mean, I don't know.
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It seems like there's a lot of concern as well
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as far as Okay, well, if I've got you know,
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an AI n LLM generating code, am I even going
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to have a job or you know, maybe they just
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downsize most of my team and so I have to
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be the most elite of the awesome elite at my company.
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And so yeah, there is a
Load full transcript
lot of concern there.
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And then the other piece of it is is, okay,
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I've got this powerful tool, Am I using it right?
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And so let's talk about the code reviews first, since
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that's where you're kind of living these days. And I
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don't think we've really gotten into the AI code reviews,
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to be perfectly honest. I saw that GitHub does some
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like you can turn this on for some of your
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repos and I haven't even tried it because it's just
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like I don't know, I mean, at work, you have
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to have a human code review your stuff anyway.
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And then on my personal stuff.
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It's like, like, I guess I did turn it on
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on one project and I was not that impressed. So
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so tell me where this fits in and maybe where
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I'm not using it to its full potential.
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Yeah, these are good pointers. So you touch an interesting point.
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They're related and different. So it's suckles almost theim like
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first about losing your job, Like you get to gut
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that point really quickly. I think like, first of all,
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the next few years. I'm not talking about fifteen years.
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That's it's hard to predict, especially the future. So let's
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focus on five years and we can we can go
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to further and out. I do have like my opinion,
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strong opinion about about the five next year. Forget about it.
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Sorry.
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Dario froment Tropic and Sam Altman cleaning. I don't know
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half a year ago, one year ago to the twenty
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twenty five. You don't need more developers. Ninety percent of
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code generated by developers. Yea sweet bench if you know
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this benchmark software engineering benchmark going to be ninety nine
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by nine of the year.
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We're far from all of that.
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And now I claim that while they decrease their predictions
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for twenty twenty six lower than twenty twenty five. I
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think even there they're wrong. So but that's not doesn't
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mean that the future is not going to be strong
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right now. It all already is, and it's going to
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be more and more AI empowered. And yes, at some
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point there is going to be a flection point where
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we're actually going to see end to end automation of
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software certain aspect of software development. But we can talk
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about it via a core review specifically about you know, Copata,
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et cetera. We are actually good friend, Like we have
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multiple clients that we are partnering together. They focus more
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on code generation and agentic workflow out of or pushing
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like get up issue into a PR much quicker. We're
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more focused on how do you note that that you
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can trust that line code? And I'll elaborate about that now.
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Sometimes there are some you know, feature like think about
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the cloud, like cloud have cloud observability tools, but still
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you have the data dog off the world. And that's
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a difference between like a few features that the cloud
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have around observability to a full fledged platform that gives
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you the confidence. So I won't go deeper on that
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competition because it's actually quite a symbiotic like, the more
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people use copilot, the more you need CODO in order
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to trust the code, So it's actually good.
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But code review specifically, I think like some might.
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Think about a synonym for where you deal with software quality,
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but actually difference like it is part of it. But
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let's think about quote review the purpose. It's meaningful purposes
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first like owning the code and learning two different very
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close relate buckets, like as a team. First the developer
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mostly single handed, like developing a feature or a sub feature.
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And then there's that moment where the team take responsibility
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if that person is going to be in vacation, who's
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going to do root code analysis if something happens right,
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So it's a moment where you you like, learn to
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each other code, learn the software, and own it together,
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even if an AI
generated it completely one hundred percent
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from one prompt to a PR which is rare. But
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let's assume we're going to maybe talk about aspector and development,
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et cetera. But let's say you did you respect. Yeah,
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let's say you did write respect completely like perfect man,
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and you've got a feature. Even then a human needs
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to take over at least for the next few years
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and take responsibility. So that's one one thing about Court
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of View, and the second thing is that like until now,
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and it's going to be in the next few years,
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even if you do the best spec and the best
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like work, you're still going to have AI that needs sorry,
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code that needs to be reviewed. And there's a different
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between a tool that is meant to help you review
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and startulize quality to the one that is trying to
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help you generate code. Same off, how there's a difference
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between code of reserveability and thought. So to till the
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r hey, don't worry, in my opinion about your job.
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I would even recommend people as different than all other I.
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Think nine and a half the industry.
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Do go learn like you know computer science, but do
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like work with AI as one of your main tools
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throughout the STLC and code generation, Code of view will
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cot analysis, generating your respect everything and you will just
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alleviate together with with the profession, and that I think
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my point about that.
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Yeah, I agree with a lot of what you said there,
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and I'm going to back up through some of it,
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and then I've got a couple of questions as we
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go through. But one is you know, because yeah, you
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kind of wove in the am I going to lose
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my job?
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Or where where do I go with my job? Along
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with hey, where.
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Does the code you stuff fit in? And and that's
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where I have more questions. But I just want to
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reiterate a couple of things. One
is is that we're
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not getting away from people having to be involved in
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the process. Is what I heard you say. And so
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to a certain degree, yeah, you may have tools that
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take on certain part aspects of the job or you know,
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do some of the things that are involved in you know,
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understanding and figuring out what's going on. But at the
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end of the day, yeah, you need a human that's
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you know, that's going to take responsibility for this stuff
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and and shepherd it through. And if you're looking to
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enhance your career, you need you need to understand and
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be able to use these tools because they do make
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people more productive. And so the adoption of this stuff
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is inevitable basically. And so those are the triitions and
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the role right and the role of soffer interrupting of
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software developer right now in five year is completely different.
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I love the word that you shepherd, like like basically
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you're going to deal a lot with writing spects, writing
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your rules, writing your best practices, following down like navigating
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your army of agents, specializing in how to deal with
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specific problems that might.
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Come around with that. How everything I said is going
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to be evolved, et cetera. So it's going to be
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completely different, like higher level, more architecture, guardrails stuff. And
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it's a process that happened throughout the last twenty thirty years.
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Right.
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We used to punch cards and write notes and assembly
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and etcetera. So it evolved. So it's going to be different.
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But there's a lot of things for a human to
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take responsibility and ownership.
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Yeah, and wherever we end up, right, because I think
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it's optimistic of anybody to say, well, this is really
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where I think we're going to be even in two
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or three years, because things are moving so quickly, right,
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And you pointed out, you know, Sam Altman and some
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of these other folks, they thought they knew where this
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was going, and they just you know, it's impossible to
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really predict it. But if you're on top of what's
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going on today, then it's a whole lot easier to
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adapt to whatever comes later. And then as far as
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the like the AI run code reviews and things like that.
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So you talked a little bit about somebody's going to
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have to understand and maintain and take ownership of the code,
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and a lot of that knowledge has passed through a
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code review, right, So are you are you advocating that
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you have a human reviewer and an AI reviewer or so? So, yeah,
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how does this fit into the life cycle of my
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features and things like that.
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Yeah, we'll talk about a bit about nostalogy just before,
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Like you remember those days, like I think me and
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you are like sorry for iman selling old model enough
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to remember today's where we actually use books.
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Do you remember before.
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We went to Google or whatever was other websites, we
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were actually using books to learn about things, et cetera.
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And I still use books.
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That's kind of my maybe I'm going to sound old,
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but and mostly yeah, if it's technology, I do spend
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a lot of time on the internet kind of picking
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up the new things, right because the books get out
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of date kind of fast, depending on how quickly technology moves.
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But yeah, I prefer picking up the books and just
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getting kind of the classic ideas that don't change it.
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Everything runs on.
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So so we are nostalgic about it.
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But if I have to force you, like choose one,
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I think I know what you're going to choose. And
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think about how much we delegate to Google or other
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technologies on the Internet and interanet to that we're not
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going to validate right now to the deepest like part
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of it. So I think it's right now already like
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a very human digital we call it I or not
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like technology based like development already as it is today.
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And I think like the same thing is going to
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happen with court review, where actually if you have a
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one hundred and we are seeing customers that have more
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than that, like a set of guardrails, rules, standards, and
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I can give you an example like having a human
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reviewer doing that like all at once in the time
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the limited time is actually problematic and if you can
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free the time to focus on what you know where
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could be the most useful for that courde review, it's
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actually going to have better learning, especially if the tool
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AI tools that are using cares about learning. So even
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if it caught something, it's just like, oh I fix it.
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It's rather hey, this is the reasoning, and that's what
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we learned from it, and by the way, this is
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how we changed our second brain like that. And so
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let me give you an example, like there could be
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that someone in the company really cares about you know,
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eat your flags, and they didn't manage to tell everyone no.
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And the five hundred developers seven thousand developer organizations, that's
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what you care about. You can configure that in the
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system and I system. And now if you developed a
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new feature, you get an alert, Hey you didn't put
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a feature flag. Oh show you shouldn't you know? It's
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this is also learning, like it's like, hey, oh, the
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company cares about feature flag. I have a one hundred
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example like this, but just one, you know, very I
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think famous, funny or scary or not funny. Min I'm
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a developer. This is like I'm a developers. I wake
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up in the morning, I have a slot forty minutes
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to review a PR, a pull request. If you give
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me five lines of code in that PR, I'll give
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you fifty comments. If you give me fifty lines, I'll
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give you five comments. If it's five hundred lines, looks
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good to me? Right, Like, in forty minutes, how can
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I five on lines of Code's that's a reality. And
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by the way AI could even help us with classifying
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like which review which pr?
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You can actually do it for forty minutes.
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That's how Also doctor works like they get an AI
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tool that helps them prioritize, not necessarily tell them if
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that person is whatever diagnostic, but just serve facing. Right,
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So the opportunities are awesome. Actually, the problem is real
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already existing around learning and catching issues. Tell me if
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you didn't have a major issue, which developer did not
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have a major issue?
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And production?
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I can tell a few horror stories myself, and so
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the opportunity is not reducing the learning or increasing bugs,
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is actually trying to to you know, get this better.
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But we do need to write you xui and the
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right mindset of the team. That's the building the dev tool.
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So obviously like.
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Sorry, shameless plug bug code, but it's not only us, okay,
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like or not going to be the only one who
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cares about it.
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Yeah, I think I think the how do I put it?
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So?
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Programmatic checking of your code has been around for a
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long time, right, So you have the static analysis tools,
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You've got the linters and stuff like that. The electronic
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with those is that typically at least the ones that
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I've seen they break your code down with the abstract
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syntax tree and then they look for patterns.
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Is basically what they do.
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And the AI systems they kind of do that. But yeah,
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you can be a lot more I guess, a lot
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less prescriptive as far as what the patterns are, right,
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and so you can train it on how to look
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at code and what to look for, but you can
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be a lot more broad and it'll figure out how
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to do
some of the stuff that you have to
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explicitly tell the other the static analysis tools and the
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linters how to do right. And so you can say,
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like your feature flag example, right, you know it can
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you know, I mean, depending on the training of the
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system and you know how good the data set is
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and things like that, and how good your prompt is.
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But you can teach it.
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Essentially, this is how you identify a new feature and right,
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and then you have to make sure that there's a
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feature flag around it. And it can figure out the
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other steps, like it can infer what to do and
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so on a lot of those things where you're saying
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you set up a rule. Effectively, what you're saying is
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is my code has to conform to these ideas, and
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then it can with its latent space, you know, the
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stuff that it's been trained on, anything else that you
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add to the mix, with its context and things like that,
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it can then go in and intelligently figure a lot
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of that stuff out where trying to figure out how
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to explicitly program it to say, here's how you find
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something like this in the abstract syntax tree, or here's
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how you break down this idea. Especially on you mentioned
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like the five hundred line pr right, it's like across
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all of these files and all of these changes. That's tricky.
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But the LLM can consume it and figure it out
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much much easier than you can figure out how to
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explicitly program it for every case you're going to run into.
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And it's not going to be perfect, but it can
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do a lot better job, and it can do it
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a lot more quickly and a lot more thoroughly.
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Than I can.
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Yeah, just kind of browsing through it and going, you know,
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hoping my pattern matching brain goes, yeah, that's a problem,
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and that's also a problem. And hey, there's so much
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context in here that I'm just trying to understand that.
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You know, I'm going to pick out all these little things.
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But then when I go in as an experienced programmer
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to do the code review, you know, I can see
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what it caught and I can say, yeah, ninety nine
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percent of this is great. You don't have to worry
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about these couple of things that you know, it may
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not be quite right on that or it's close, but
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you should do this instead of that. And then the
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other thing that I can see with it is that
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there may be things that are just kind of aesthetic
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or organizational or other things that we do that we
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haven't codified into the rules where it's like, no, we
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do things this way, not that way. And then you
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can also go back and you can retrain your you know,
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you can rewrite your prompt to include those on the
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next one. But yeah, I can see where the LM's
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looking at the code could do a much broader and
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more nuanced analysis than you can get out of some
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of these other tools and be more thorough than a
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programmer doing it.
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I'd love to relate to the you know, traditional quote
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unquote old world satical analysis versus the semantic AI. But
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just just before that, about philosophy around like, how would
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you how would you actually exploit l lams like AI
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to work with rules or standard standards that are written down.
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So it's for those who are watching the video I'm
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actually having I have here like a black shirt that
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it's really where you really I wear purple. And I'll
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explain why, because I think the world's roughly sticking divide
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into too and I'm relating to how you use rules
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in the standard and mark down, et cetera. I'm going
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to explain it's connected, I swear. So there's the blue
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team and the Red team. Like principle at CODO, we
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see yourself like more in the red with mixed. That's
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one of the reasons we chose the purple. So I'll
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explain the
blue team, which we had like the Winter
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of the Cursor of the World, et cetera. Right QUOD
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code compile everyone Basically they take those rules you can
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write them in different and they mostly put it as
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part of their context, which in many cases is actually
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listening to.
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But there's two problems. One I said many cases.
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The second is that maybe it took it as part
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of the context. But as you usually it doesn't work.
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You don't finish a feature like at least not a
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meaningful feature from promptu code, even if it's an agent
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that runs for ten minutes usually or one hour, and
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how sometimes you usually like get to prompt it over like.
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Like navigate it.
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Then at your tenth you know, prompt it actually very
372
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very probably like missed something from your spec or or
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your rules or your even your own prompt. And the
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second one that's a blue way of thinking, would they
375
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prioritized like their KPI is like u x ui. We
376
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admire on them on that and speed from prompt to code,
377
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et cetera. The red team is how we take every
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fact that is there as an intent they're functional or
379
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non functional, and verify it. That's a totally different process.
380
00:19:18.759 --> 00:19:20.839
So you take the list of your one hundred rules,
381
00:19:20.880 --> 00:19:22.920
whether it's ten or or one thousand by the way,
382
00:19:23.519 --> 00:19:27.240
and you and you check them with and write lam
383
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to turn to really increase. I can't say one hundred percent,
384
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but really ninety nine whatever and free is a chance
385
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that they're actually actually checked. I just want to you
386
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know that it's important to differentiate between different philosophies and
387
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it actually leads to a different uxui, et cetera. And
388
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that relates to the first topic of ASTs. I think
389
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that they're actually very powerful tools like sonar for example.
390
00:19:50.079 --> 00:19:53.079
I would definitely I have like high expectation of that company.
391
00:19:53.119 --> 00:19:55.839
And but yeah, they do a great job within the
392
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set that they're you know, they're capable of doing.
393
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They do two and I think like eventually, if you
394
00:20:01.079 --> 00:20:04.400
want to exploit AI, really explore, move fast with confidence,
395
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you want that mix of you know LLLM AI empowered
396
00:20:09.279 --> 00:20:13.839
code review, code quality with that static and it would
397
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be the best if you can actually mix them together
398
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and you would see integrations like we have integrations with
399
00:20:20.200 --> 00:20:23.440
with those tools because they might catch in many cases
400
00:20:23.480 --> 00:20:25.400
the same thing and you don't want that annoying like
401
00:20:25.519 --> 00:20:29.920
double double reviewing double thing. So but but the basic
402
00:20:30.000 --> 00:20:33.519
of the technology, you want this technology to to to
403
00:20:33.599 --> 00:20:36.640
work together because otherwise you don't have a confidence to
404
00:20:36.759 --> 00:20:40.319
really like, okay, there's one to clock code. In my
405
00:20:40.400 --> 00:20:43.279
experience like you often I wouldn't say daily but weekly,
406
00:20:44.240 --> 00:20:47.720
Like with one prompt, I can can get one thousand
407
00:20:47.759 --> 00:20:50.920
line of code in five minutes being changed. Go review
408
00:20:51.000 --> 00:20:54.359
that right and right and and now and now, like
409
00:20:54.839 --> 00:20:58.960
you want the maximum help of AI also or sorry,
410
00:20:58.960 --> 00:21:02.799
any technology to help you navigate to getting confidence about
411
00:21:02.799 --> 00:21:08.119
that code quality, correctness, maintainability, et cetera. So I have
412
00:21:08.200 --> 00:21:11.480
like a high expectation of these twols working working together,
413
00:21:12.160 --> 00:21:15.319
being that hunter part the red versus the blue. So
414
00:21:15.519 --> 00:21:18.200
you can get a purple as a dev organization.
415
00:21:19.400 --> 00:21:22.440
So I mean going into this, right, and I kind
416
00:21:22.480 --> 00:21:24.599
of I mean, we may change the title when we publish,
417
00:21:24.640 --> 00:21:27.559
but I kind of tongue incheek put the title is
418
00:21:27.599 --> 00:21:30.559
can I trust AI generated code? And so now you're
419
00:21:30.599 --> 00:21:33.039
talking about this other end of things right where it's
420
00:21:33.079 --> 00:21:37.079
like you've got the copilots or cloud codes or Google
421
00:21:37.119 --> 00:21:39.440
Gemini or chat GPT.
422
00:21:39.400 --> 00:21:42.519
Or whatever, right or whatever?
423
00:21:42.599 --> 00:21:46.000
Right, Yeah, and so you know they're using these models
424
00:21:46.039 --> 00:21:49.039
to generate code, right and then yeah Kiro we did
425
00:21:49.079 --> 00:21:53.160
an episode with Eric Hanchett where you know, in Kiro
426
00:21:53.839 --> 00:21:58.720
it helps you generate the entire spec and then basically
427
00:21:58.799
--> 00:22:01.119
it kind of iterates through step by step and does it.
428
00:22:01.119 --> 00:22:03.119
It was funny because we did that episode. I'm sitting
429
00:22:03.119 --> 00:22:06.400
there going, well, I've been using Cursor and vs Code
430
00:22:06.400 --> 00:22:09.079
with Copilot and neither of them do it, and literally
431
00:22:09.119 --> 00:22:13.519
a week later is showed up in Cursor. And so
432
00:22:13.240 --> 00:22:16.039
so I've used that feature a handful of times and
433
00:22:16.079 --> 00:22:18.480
it's really nice. The other thing that I'm going to
434
00:22:18.519 --> 00:22:20.480
point out because you're like, yeah, you know you have
435
00:22:20.559 --> 00:22:23.200
claud code generate you a thousand lines of code or whatever.
436
00:22:23.319 --> 00:22:26.400
I was having a conversation yesterday with Obi Fernandez. He's
437
00:22:26.799 --> 00:22:30.279
a Ruby developer, but I've seen the same thing with
438
00:22:30.279 --> 00:22:34.240
from JavaScript friends, where you know, they use claud code
439
00:22:34.279 --> 00:22:37.000
and they have it generate a bunch of the code.
440
00:22:37.039 --> 00:22:39.079
Right, and then they kind of review it on their own.
441
00:22:39.640 --> 00:22:43.480
And the thing that's he basically said because he used
442
00:22:43.519 --> 00:22:46.680
to run a consultancy, one of the bigger consultancies out
443
00:22:46.720 --> 00:22:51.279
there in the Ruby space, and he was saying, I
444
00:22:51.400 --> 00:22:53.920
built this app in a month. That it would have
445
00:22:53.960 --> 00:22:57.519
taken us six months and probably five hundred thousand dollars
446
00:22:57.599 --> 00:23:01.920
of developer salary to build this, and I've done it
447
00:23:01.920 --> 00:23:04.960
by myself in a month with AI help. And so
448
00:23:05.200 --> 00:23:07.279
I'm looking at it and go, well, so to a
449
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certain level, I guess the answer to can I trust
450
00:23:09.319 --> 00:23:13.079
AI generated code is yeah, I mean to it because
451
00:23:13.240 --> 00:23:16.359
it works, right, But yeah, so if you have the
452
00:23:16.799 --> 00:23:19.960
AI generating the code, I mean, where do you run
453
00:23:20.039 --> 00:23:24.279
into problems there? And then you know, especially with what
454
00:23:24.319 --> 00:23:26.920
you're seeing, where you have this system that then reviews
455
00:23:26.960 --> 00:23:29.279
the code. So if you have the LLM generate the
456
00:23:29.279 --> 00:23:32.000
code and the LLM review the code, right, is that
457
00:23:32.119 --> 00:23:35.279
kind of a circular dependency that does or doesn't work?
458
00:23:35.319 --> 00:23:38.680
I mean, this is where I'm starting to get into. Okay,
459
00:23:39.319 --> 00:23:41.480
you know, I've got these tools that are supposed to
460
00:23:41.559 --> 00:23:46.279
empower me, but do they actually work nicely together to
461
00:23:46.319 --> 00:23:48.839
give me that red team blue team work out?
462
00:23:49.000 --> 00:23:49.400
Yeah?
463
00:23:49.440 --> 00:23:52.559
And I guess the other concern is is you know,
464
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am I actually moving faster or am I moving faster
465
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with a gun into my foot.
466
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The whole time?
467
00:23:58.799 --> 00:24:00.440
Yeah, we heard a lot last point.
468
00:24:00.519 --> 00:24:03.559
I think, like short, what I'm mostly want to relate
469
00:24:03.599 --> 00:24:06.440
about the main question off the topic of today, like
470
00:24:06.480 --> 00:24:09.839
should you trust AI generated code? I do have an answer,
471
00:24:10.319 --> 00:24:12.599
but I just say, like I do think the future
472
00:24:12.680 --> 00:24:15.000
is already happening now, like pol people of a client
473
00:24:15.240 --> 00:24:18.759
connecting CODO with cursor, CODO with COPAAD or or whatever,
474
00:24:19.319 --> 00:24:20.799
and that's more.
475
00:24:21.519 --> 00:24:23.519
Yeah. I just want to like it, Okay, Yeah, I
476
00:24:23.599 --> 00:24:25.319
just don't want to like to spend too much time
477
00:24:25.319 --> 00:24:25.440
with that.
478
00:24:25.720 --> 00:24:29.319
I guess it's a lot of commercial today's, so it's
479
00:24:29.319 --> 00:24:30.720
possible it's going through that direction.
480
00:24:30.880 --> 00:24:31.359
More and more.
481
00:24:31.720 --> 00:24:34.720
I do want to answer the question, but with a
482
00:24:34.759 --> 00:24:38.240
metaphor before if I ask, do you you do you
483
00:24:38.359 --> 00:24:42.640
trust human code? Wait, way, don't answer, let me give
484
00:24:42.640 --> 00:24:44.839
you give you a second, but just think about a
485
00:24:44.880 --> 00:24:48.480
for a second. Do you trust human the like code?
486
00:24:48.960 --> 00:24:53.440
Think like, let's let's well think about a little bit. Sorry, no,
487
00:24:53.519 --> 00:24:56.720
because because you're you know, it's a it's a good point.
488
00:24:56.799 --> 00:24:59.319
The flip side, though, is that because I don't know
489
00:24:59.359 --> 00:25:01.559
that it's necessar do you trust human generated code? A
490
00:25:01.599 --> 00:25:04.640
lot of it depends on the human right, and you know,
491
00:25:04.720 --> 00:25:06.640
it's like, Okay, how much experience do they have? Have
492
00:25:06.680 --> 00:25:07.559
they done this before?
493
00:25:08.200 --> 00:25:08.440
You know?
494
00:25:09.039 --> 00:25:13.119
Are they taking security and scaling and all the other
495
00:25:13.160 --> 00:25:14.079
things into account?
496
00:25:14.200 --> 00:25:14.400
Right?
497
00:25:14.759 --> 00:25:18.440
But the other thing is is that I don't think
498
00:25:18.480 --> 00:25:20.799
that that's the baseline that we have to look at
499
00:25:20.839 --> 00:25:25.079
AI from because it's not whether or not I trust
500
00:25:25.200 --> 00:25:27.680
human code and whether or not I trust AI code,
501
00:25:27.799 --> 00:25:31.000
because at this point, the
human generated code is the baseline.
502
00:25:31.000 --> 00:25:32.759
We've been doing that for years and years and years
503
00:25:32.799 --> 00:25:33.359
and years.
504
00:25:33.559 --> 00:25:36.480
Is the AI code better or the other way you
505
00:25:36.519 --> 00:25:39.480
look at it from a business standpoint, is is it
506
00:25:39.640 --> 00:25:42.400
close enough? Given what I had to do to generate it,
507
00:25:42.440 --> 00:25:46.440
which is usually a prompt and not nearly as much time. Right,
508
00:25:46.559 --> 00:25:50.200
so maybe it's not as good as human code in
509
00:25:50.240 --> 00:25:55.160
a number of ways, but it's good enough and it's.
510
00:25:55.039 --> 00:25:58.200
A lot cheaper, and so it may be worth it anyway.
511
00:25:59.880 --> 00:26:00.880
I'll use you on it.
512
00:26:00.920 --> 00:26:04.119
But still still bear of me with the metaphor, like
513
00:26:04.160 --> 00:26:07.720
if I ask, and I'll borrow something that you said
514
00:26:07.759 --> 00:26:09.319
to you to make my point. If you ask, like
515
00:26:09.480 --> 00:26:13.160
do you trust human the developed generated code, written code
516
00:26:13.240 --> 00:26:17.720
or coded code, then then I think that the minute
517
00:26:17.759 --> 00:26:19.359
answer is like, yes, that's what we do. But then
518
00:26:19.480 --> 00:26:21.000
you think about it a bit more like you just did,
519
00:26:21.079 --> 00:26:24.519
and you're thinking, if I trust that person, even if
520
00:26:24.519 --> 00:26:27.759
it's a senior developer, to write the code and noteped
521
00:26:27.839 --> 00:26:30.880
plus plus on the airplane and then push it to production.
522
00:26:31.119 --> 00:26:34.039
I'm not saying I never did that, but but but
523
00:26:34.039 --> 00:26:36.519
but push a production. I think your answer is no,
524
00:26:37.319 --> 00:26:40.519
And then you're what you are saying, like a senior
525
00:26:40.559 --> 00:26:44.160
developer that is going through the processes that is required,
526
00:26:44.759 --> 00:26:48.359
uh checking also for security reviewing for the standards and
527
00:26:48.400 --> 00:26:51.279
all that and going through the review process and checking
528
00:26:51.319 --> 00:26:53.599
the CI results and et cetera. You do trust if
529
00:26:53.599 --> 00:26:56.960
you want generated code. That's my answer, Like you should
530
00:26:57.039 --> 00:26:59.799
you trust AI generated code? Spit it out, you write
531
00:26:59.799 --> 00:27:03.119
a you wrote it, prompt you via code, trust it. No,
532
00:27:03.839 --> 00:27:06.440
I suggest you don't also do that. For the most
533
00:27:06.440 --> 00:27:09.640
senior developer, you have a process, the process could be quick,
534
00:27:09.960 --> 00:27:10.720
and I think that's what.
535
00:27:10.759 --> 00:27:11.400
We're going to see.
536
00:27:11.400 --> 00:27:14.200
Also for the AI generated code, you're going to see
537
00:27:14.359 --> 00:27:18.359
AI being used to automate more and more and more parts,
538
00:27:18.759 --> 00:27:21.920
and then AI generated code is going to look like
539
00:27:22.160 --> 00:27:28.559
more more a generated code like AI generated software development,
540
00:27:28.880 --> 00:27:32.440
AI software development, where AI is going to do a
541
00:27:32.640 --> 00:27:37.680
proper process. Humans, by the way, in certain percent, which
542
00:27:37.720 --> 00:27:40.799
in the beginning would be very big, very large, sorry,
543
00:27:41.359 --> 00:27:43.880
are going to be involved throughout the process, and over
544
00:27:43.960 --> 00:27:48.160
time the process is more like you know, like verifying
545
00:27:48.200 --> 00:27:50.240
that the pipeline works, you know, like like we do
546
00:27:50.359 --> 00:27:54.319
with the MANUF in a manufacturing like a lab. Right
547
00:27:54.359 --> 00:27:58.160
like it started with human labor and slowly like more
548
00:27:58.200 --> 00:28:01.319
and more and more automatic humans are still involved there,
549
00:28:01.400 --> 00:28:04.319
et cetera. It took like fifty years to do that.
550
00:28:04.440 --> 00:28:07.720
It will take also like fifteen twenty years. We've talked about.
551
00:28:07.519 --> 00:28:10.599
Predicting for totally being machines.
552
00:28:11.079 --> 00:28:14.079
Meanwhile, like I just invent in a number like fifteen years,
553
00:28:14.119 --> 00:28:16.440
like twenty forty one just to have to guess, like
554
00:28:16.480 --> 00:28:18.400
the four and one looks like AI, so I guess
555
00:28:18.440 --> 00:28:21.079
it's a stayer for me. But until then, like we're
556
00:28:21.119 --> 00:28:23.519
just going to see like more and more portioned being
557
00:28:23.559 --> 00:28:26.640
automated and maybe some portion like automated end to end,
558
00:28:27.079 --> 00:28:29.519
and at that point you will trust it because it's
559
00:28:29.559 --> 00:28:32.640
not just like spitting statistically code and even if it's
560
00:28:32.720 --> 00:28:35.960
trained really really well, but it's also going to validate itself,
561
00:28:36.079 --> 00:28:38.319
verify itself going through the process, et cetera.
562
00:28:38.680 --> 00:28:40.759
And that's a future. That's a future we believe that.
563
00:28:40.960 --> 00:28:43.119
Code of right, that's where we focus on code quality,
564
00:28:43.160 --> 00:28:44.200
codelorification and etc.
565
00:28:44.920 --> 00:28:45.160
Right.
566
00:28:45.200 --> 00:28:48.759
I think the point is well taken that because a
567
00:28:48.759 --> 00:28:54.279
lot of people they conflate where things are with where
568
00:28:54.319 --> 00:28:57.119
we're going to end up. And what you're saying is
569
00:28:57.119 --> 00:28:59.119
is that, yeah, we're going to get better and better
570
00:28:59.160 --> 00:29:02.279
tools to do more more things, and they're going to
571
00:29:02.319 --> 00:29:06.240
manage more and more pieces of the process, right, And
572
00:29:06.240 --> 00:29:09.480
and yeah, I think that that is
absolutely true. It's
573
00:29:09.480 --> 00:29:15.279
funny because for several years, you know, I've been using
574
00:29:15.519 --> 00:29:19.880
rock or chat, GPT, or Claude. I've used all of
575
00:29:19.920 --> 00:29:21.039
them for different things.
576
00:29:21.119 --> 00:29:21.279
Right.
577
00:29:21.319 --> 00:29:24.720
It's like, hey, I'm trying to explore this thing, right,
578
00:29:24.759 --> 00:29:28.160
and so'll it'll give me all kinds of feedback on
579
00:29:28.440 --> 00:29:31.799
you know, health or whatever, right, and so I kind
580
00:29:31.799 --> 00:29:33.640
of use it as a coach or at least, you know,
581
00:29:33.680 --> 00:29:36.720
and then sometimes I'll go fact check it or verify
582
00:29:36.799 --> 00:29:40.440
this or that, or you know, refine whatever it gave me.
583
00:29:40.599 --> 00:29:42.839
But you know, a lot of times it shortcuts a
584
00:29:42.880 --> 00:29:44.640
whole bunch of research that I would have to do,
585
00:29:44.720 --> 00:29:46.640
and so then I can just justify the pieces where
586
00:29:46.680 --> 00:29:49.559
I'm like, it doesn't seem quite right, but it's it's
587
00:29:49.599 --> 00:29:52.279
gotten more and more correct the longer, you know, the
588
00:29:52.359 --> 00:29:55.400
longer we go, because the models get better, the data
589
00:29:55.440 --> 00:29:58.039
that's you know, in that latent space gets better.
590
00:29:58.640 --> 00:30:02.200
And so that's I definitely see with software.
591
00:30:02.920 --> 00:30:05.799
Yeah, But as far as conflating where we are with
592
00:30:05.880 --> 00:30:08.440
where you know, a few years ago, I think it
593
00:30:08.480 --> 00:30:10.640
was just last year actually, I was having a conversation
594
00:30:10.720 --> 00:30:14.119
with my father in law. Now, granted he's a general contractor,
595
00:30:14.359 --> 00:30:17.000
and by general contractor, I mean like he fixes crap
596
00:30:17.039 --> 00:30:20.279
in people's houses general contractor, and you know, and so
597
00:30:20.400 --> 00:30:22.720
he just heard about like the goof ups in the
598
00:30:22.759 --> 00:30:25.240
news where it was like, well, I heard somebody ask
599
00:30:25.359 --> 00:30:28.359
chat GPT this thing and it told him this bogus thing.
600
00:30:29.039 --> 00:30:31.519
And I'm like, yeah, dad, but we've moved like four
601
00:30:31.559 --> 00:30:35.000
models ahead since then and it doesn't do that anymore.
602
00:30:36.000 --> 00:30:38.039
And he's like, yeah, well, you just can't trust it
603
00:30:38.079 --> 00:30:40.319
for anything, and you know, and again I'm looking at
604
00:30:40.400 --> 00:30:43.000
him and going, well, actually, I use it all the
605
00:30:43.079 --> 00:30:47.200
time for this other stuff because you can trust it, right,
606
00:30:47.519 --> 00:30:50.240
But yeah, I go and fact check stuff. It's like,
607
00:30:50.279 --> 00:30:52.319
you know, I think you have a bias here, and
608
00:30:52.359 --> 00:30:54.640
so I'm going to go fact check these pieces because
609
00:30:54.680 --> 00:30:56.960
I think I think the data that you were trained
610
00:30:57.000 --> 00:30:59.960
on isn't one hundred percent in line with my worldview
611
00:31:00.240 --> 00:31:02.319
or the way I think things are. But at the
612
00:31:02.359 --> 00:31:05.440
same time, yeah, it's gotten way, way, way more accurate,
613
00:31:06.079 --> 00:31:08.319
especially when it gets into a lot of the you know,
614
00:31:08.599 --> 00:31:12.359
like easier for like meal planning and you know, hey,
615
00:31:12.359 --> 00:31:14.559
I got to modify this workout or this or that,
616
00:31:14.599 --> 00:31:17.119
and it you know, it's terrific because it's got all
617
00:31:17.119 --> 00:31:19.799
that data in it. And I think that's where we
618
00:31:19.839 --> 00:31:23.880
go with the software. Is My point is so even
619
00:31:23.920 --> 00:31:26.279
where we see it kind of fall short. Is it
620
00:31:26.480 --> 00:31:29.720
a much better and much more accurate, much more thorough
621
00:31:30.039 --> 00:31:33.400
than I can be as a code review reviewer. And
622
00:31:33.480 --> 00:31:36.680
it looks like the answer to that question is undoubtedly.
623
00:31:36.759 --> 00:31:37.000
Yes.
624
00:31:37.680 --> 00:31:40.559
It may give me some false positives and some false negatives,
625
00:31:40.599 --> 00:31:43.079
but it's going to be more thorough and much faster,
626
00:31:43.200 --> 00:31:44.680
and I can pick through that and it's still going
627
00:31:44.720 --> 00:31:46.319
to save me a whole bunch of time and effort,
628
00:31:46.440 --> 00:31:49.240
and it's only going to get better. Right, So where
629
00:31:49.279 --> 00:31:51.799
we end up in a few years it may be
630
00:31:51.839 --> 00:31:54.640
completely different, but it's almost certainly.
631
00:31:54.279 --> 00:31:56.720
Going to have better data to run on and make
632
00:31:56.759 --> 00:31:57.559
the process better.
633
00:31:57.920 --> 00:32:02.680
I think, like I truly believe there is meaningful improvement
634
00:32:02.720 --> 00:32:05.440
in the LLLM. Like some people over time claim that
635
00:32:05.440 --> 00:32:11.400
it's like diminishing the velocity of right because it consumes
636
00:32:11.400 --> 00:32:14.200
what it's been putting out. I've heard that for example,
637
00:32:14.240 --> 00:32:16.680
for that reason, et cetera, I think the are meaningful improvement.
638
00:32:16.720 --> 00:32:19.920
We have internal benchmarks around quality of code, et cetera.
639
00:32:20.240 --> 00:32:22.519
It's it's it's going up. And having said that, I
640
00:32:22.599 --> 00:32:28.240
think there's other reasons where like extracted value is bigger. First,
641
00:32:28.319 --> 00:32:30.240
we use it in more areas the l ll ms
642
00:32:30.240 --> 00:32:32.400
and more areas. But specifically I want to relate to
643
00:32:32.400 --> 00:32:34.480
what you said. I think it's also we're learning how
644
00:32:34.519 --> 00:32:34.960
to
use it.
645
00:32:35.119 --> 00:32:37.119
So you know, like as a developer, you learn how
646
00:32:37.160 --> 00:32:39.319
to Google, or you you learned how.
647
00:32:39.240 --> 00:32:41.599
To use stack overflow, but now I guess, but you
648
00:32:41.720 --> 00:32:43.799
learn how to have a good Google. That's still very
649
00:32:43.920 --> 00:32:46.720
relevant and I think like we are learning how to
650
00:32:47.359 --> 00:32:49.640
you know, prompt or use it could be one of
651
00:32:49.640 --> 00:32:54.119
the differences in between you and the front or family,
652
00:32:54.160 --> 00:32:56.759
et cetera. And that's really actually to for example, what
653
00:32:56.759 --> 00:32:59.319
we talked about w as Hero and Spectra and development
654
00:32:59.440 --> 00:33:02.799
that like we we're learning that the better we the
655
00:33:02.799 --> 00:33:06.079
more information should be concise and accurate. But the more
656
00:33:06.079 --> 00:33:08.559
information we provide as part of the prompt if we're
657
00:33:08.559 --> 00:33:12.400
talking about self development, like maybe spec in most cases, the.
658
00:33:12.400 --> 00:33:13.319
Better job it will do.
659
00:33:13.480 --> 00:33:15.680
By the way, I am like once in a while,
660
00:33:15.720 --> 00:33:17.720
like giving a disclaimer or trying to be careful because
661
00:33:17.759 --> 00:33:20.079
because there's a lot of research down for example and
662
00:33:20.119 --> 00:33:22.119
Tropic are really good in it is that if you
663
00:33:22.240 --> 00:33:25.599
think that you can push like as much as context
664
00:33:25.640 --> 00:33:29.720
and instruction as you want and expected to really work well,
665
00:33:29.759 --> 00:33:32.920
they're actually seeing it diminishing returns even like worse if
666
00:33:32.960 --> 00:33:34.920
you give it like a spec of like a full book,
667
00:33:35.160 --> 00:33:37.680
even if the context is bigger. But putting that aside,
668
00:33:37.799 --> 00:33:41.200
it is a really good idea to.
669
00:33:40.559 --> 00:33:41.920
Learn how to use these tools.
670
00:33:41.960 --> 00:33:45.160
And I think we're actually consciously and consciously like like
671
00:33:45.160 --> 00:33:48.599
like doing doing that and and for software development specifically,
672
00:33:48.599 --> 00:33:51.839
and I think even JavaScript, where you know the language
673
00:33:51.880 --> 00:33:55.200
is maybe not that descriptive, et cetera. Like having a
674
00:33:55.240 --> 00:33:59.599
proper spec is a good idea. And although I have
675
00:33:59.640 --> 00:34:02.079
to say I'm not a big believer that spectrum of
676
00:34:02.119 --> 00:34:04.720
development is going to be the last thing that survived.
677
00:34:04.720 --> 00:34:06.599
It's going to be the biggest thing that actually make
678
00:34:06.680 --> 00:34:09.719
the difference. Is an important concept, but it's not going
679
00:34:09.760 --> 00:34:13.119
to be the what's solved everything.
680
00:34:13.440 --> 00:34:14.480
No, I think.
681
00:34:14.639 --> 00:34:16.599
And I'm just going to piggyback on what you're saying
682
00:34:16.639 --> 00:34:19.800
because I think you're correct in one way how we
683
00:34:19.920 --> 00:34:22.400
use the tool, right, and so you know, spec druven
684
00:34:22.480 --> 00:34:25.119
development is one way that we've you know, this is
685
00:34:25.159 --> 00:34:26.639
a new way to use.
686
00:34:26.559 --> 00:34:30.639
The LM and you know, maybe have a wider.
687
00:34:30.320 --> 00:34:32.400
Context on what it's doing and give it a step
688
00:34:32.400 --> 00:34:35.960
by step cohesive plan. But yeah, I don't think that's
689
00:34:36.000 --> 00:34:37.800
where we end up. I mean, we're going to invent
690
00:34:37.880 --> 00:34:40.480
other ways of using these tools, and this may be
691
00:34:40.519 --> 00:34:43.480
a stepping stone to something else. The other thing, though,
692
00:34:43.559 --> 00:34:46.960
is that it's not just for me. Hey, we're getting
693
00:34:46.960 --> 00:34:50.039
better at using these tools, but also as it takes
694
00:34:50.079 --> 00:34:52.719
things off of our plate, we're able to refine in
695
00:34:52.800 --> 00:34:55.679
other areas, and I think those get better because the
696
00:34:55.719 --> 00:34:58.039
next versions of the models pick up some of those
697
00:34:58.119 --> 00:35:00.880
changes to the way we do things outside of how
698
00:35:00.880 --> 00:35:03.760
we use the LLM and make it better that way too.
699
00:35:04.119 --> 00:35:09.079
And so at some point does it kind of you know,
700
00:35:09.199 --> 00:35:12.360
are we getting smaller increments of value?
701
00:35:12.559 --> 00:35:13.039
Maybe?
702
00:35:13.800 --> 00:35:18.000
But again I I just see the ingenuity of people
703
00:35:18.079 --> 00:35:22.559
as we go continue to just be really cool and awesome,
704
00:35:23.360 --> 00:35:25.360
and so for the time being, we just see these
705
00:35:25.400 --> 00:35:28.400
astronomical leaps every time we get a major version update
706
00:35:28.440 --> 00:35:29.360
on these llms.
707
00:35:30.000 --> 00:35:35.199
Yeah. By the way, I think once upon a time.
708
00:35:36.119 --> 00:35:39.920
My background is the machine learning since two thousand and six, oh, okay,
709
00:35:39.960 --> 00:35:43.880
annual networks in twenty ten, so I allow myself talk
710
00:35:43.880 --> 00:35:48.119
about a history. I think once upon a time until
711
00:35:48.840 --> 00:35:53.760
roughly speaking GPTs three three point five. Every time you
712
00:35:54.800 --> 00:35:57.440
train them all like really like nine nine percent of
713
00:35:57.519 --> 00:36:00.159
the cases. You try it to be better than the
714
00:36:00.199 --> 00:36:03.039
others in a specific niche, right, even if it's a
715
00:36:03.039 --> 00:36:06.599
big niche, still GPT three point five, I think we
716
00:36:06.679 --> 00:36:08.639
had like a year or two or more that we
717
00:36:08.679 --> 00:36:14.880
were under
assumption level matters. You're not the market that wow, Like,
718
00:36:15.079 --> 00:36:18.519
look at this GPT three point five winning every benchmark,
719
00:36:18.519 --> 00:36:21.000
even human benchmark if you remember those graphs, amazing graph
720
00:36:21.000 --> 00:36:24.840
on opening eye exacts as like different professions like from
721
00:36:25.079 --> 00:36:28.719
lawyer stuff to history and the waxes like the percentile
722
00:36:28.840 --> 00:36:33.039
on their official tests, and like GPT three point five
723
00:36:33.599 --> 00:36:37.119
like cross every model on all of these like fifteen
724
00:36:37.159 --> 00:36:40.679
different professions, and then GBT four the same, et cetera.
725
00:36:41.039 --> 00:36:43.679
At this point, it's not the case anymore. I think
726
00:36:43.760 --> 00:36:46.760
since roughly speaking, so that's three point five if you're
727
00:36:46.760 --> 00:36:48.920
familiar from on Tropic, et cetera, then and I think
728
00:36:48.960 --> 00:36:53.440
that model was suddenly better. Some claim much better than
729
00:36:53.840 --> 00:36:57.639
GPTs on coding, but probably not at all.
730
00:36:58.760 --> 00:36:59.760
Quite a few cases.
731
00:37:00.079 --> 00:37:03.400
Now, like there was a moment where seeing people thought
732
00:37:04.119 --> 00:37:06.320
only open air on Tropic or whatever, Google are going
733
00:37:06.400 --> 00:37:10.719
to generate like foundation models, but I'm seeing like dedicated
734
00:37:10.760 --> 00:37:15.079
foundation models and in health medicine, customer success, et cetera.
735
00:37:14.920 --> 00:37:18.239
And and like we do do see like GPT.
736
00:37:17.840 --> 00:37:21.280
Five for example, and maybe there's new new versions the
737
00:37:21.519 --> 00:37:24.519
the are coming that are that are better on specific
738
00:37:24.599 --> 00:37:28.000
aspect of software development, like specific in software develop right,
739
00:37:28.360 --> 00:37:32.400
So so like there is evidence that that is it's incremental,
740
00:37:32.440 --> 00:37:37.800
and I think that's actually somewhat meaning that we're not maturing.
741
00:37:37.920 --> 00:37:40.159
And I'm not sure if that's what people thought. Aren't
742
00:37:40.159 --> 00:37:42.800
going to say, like we're maturing. Okay, let's get to
743
00:37:43.119 --> 00:37:46.119
let's get to those EDU cases. Let's get Okay, we
744
00:37:46.199 --> 00:37:49.599
probably need a specific LLM and a specific agent for
745
00:37:49.679 --> 00:37:53.519
specific quality measure that we want to track or help with,
746
00:37:53.599 --> 00:37:56.239
et cetera. So I think I think like it doesn't like,
747
00:37:56.599 --> 00:37:59.960
it doesn't mean that the whole solution doesn't keep evolving
748
00:38:00.119 --> 00:38:04.159
upwards right in the same or even bigger speed, you know,
749
00:38:04.199 --> 00:38:05.400
like the same.
750
00:38:05.119 --> 00:38:07.960
As uh uh.
751
00:38:07.440 --> 00:38:10.840
And book we talked about book like carts will that
752
00:38:11.440 --> 00:38:13.880
similarity is near. We're seeing like this is a notion
753
00:38:13.960 --> 00:38:17.199
that we're seeing like exponential growth and technology. But if
754
00:38:17.199 --> 00:38:22.199
you're zooming in, then you're seeing like skurve and and
755
00:38:22.239 --> 00:38:25.280
the thing is that each escort the time. The difference
756
00:38:25.320 --> 00:38:28.760
between that each escort just slower and smaller and smaller, right,
757
00:38:28.800 --> 00:38:31.719
and if you zoom out, it looks like exponential. So like, yeah,
758
00:38:31.760 --> 00:38:37.880
maybe that specific GPT sorry, specific LLM attention architecture and
759
00:38:37.920 --> 00:38:41.559
specific training, et cetera, is slightly like the low hanging
760
00:38:41.599 --> 00:38:44.599
fruits are over, but we are we will. We are
761
00:38:44.679 --> 00:38:49.840
seeing in the igentic world and and other like technologies
762
00:38:50.239 --> 00:38:52.920
more breakthroughs and I can mention more and and overall
763
00:38:53.280 --> 00:38:56.480
like the we're going to see AI like keep keep
764
00:38:56.519 --> 00:39:00.519
going upwards and so so I wouldn't like, like say,
765
00:39:00.880 --> 00:39:04.760
you know, like incremental LLM the solution we're getting, it's
766
00:39:04.800 --> 00:39:07.159
going to get better and better and we should adopt it.
767
00:39:07.719 --> 00:39:10.280
Yeah. All right, Well we're getting toward the end of
768
00:39:10.280 --> 00:39:11.920
our time. I hate to cut this short because I
769
00:39:11.920 --> 00:39:17.239
could sit here and talk about this forever. But yeah,
770
00:39:17.320 --> 00:39:20.159
so we're gonna just roll into our picks. I've got
771
00:39:20.199 --> 00:39:24.000
like five minutes before my work, Yeah, my work anyway,
772
00:39:24.800 --> 00:39:26.719
so I'm going to jump in. I have a work meeting,
773
00:39:27.440 --> 00:39:29.639
so I'm going to jump in and move to picks.
774
00:39:29.679 --> 00:39:29.840
Now.
775
00:39:29.880 --> 00:39:32.000
Picks are just shout outs about whatever it is that
776
00:39:32.039 --> 00:39:35.400
we've been up to and enjoying lately. So the first
777
00:39:35.559 --> 00:39:38.440
pick that I have is, so on Friday, I'm going
778
00:39:38.480 --> 00:39:42.760
to be teaching board games. I do this periodically. Hang on,
779
00:39:43.639 --> 00:39:48.880
So I yeah, I'm teaching board games at a board
780
00:39:48.960 --> 00:39:52.559
game conference, and I've picked most of the games we're teaching.
781
00:39:53.159 --> 00:39:56.480
The one game I haven't picked is well, there are
782
00:39:56.480 --> 00:39:58.239
two of them. One of them I'm learning tonight and
783
00:39:58.280 --> 00:40:01.719
the other one earned last week. And this one's called
784
00:40:01.800 --> 00:40:05.800
far Away. It has a board game weight on board
785
00:40:05.840 --> 00:40:08.719
game Geek of one point nine to one, which means
786
00:40:08.760 --> 00:40:14.599
that it's fairly approachable for the average board game player.
787
00:40:14.719 --> 00:40:17.519
And so what it is is you how do I explain?
788
00:40:17.559 --> 00:40:21.760
It's it's mostly cards. So you're playing cards in front
789
00:40:21.800 --> 00:40:24.199
of you, and you have like eight slots, and so
790
00:40:24.320 --> 00:40:26.440
you play your first slot and then your second slot,
791
00:40:26.440 --> 00:40:28.960
and then your third slot. But when you score it,
792
00:40:29.000 --> 00:40:32.320
you score it back the other way. And so the
793
00:40:32.400 --> 00:40:34.360
last card you put down is the first one you
794
00:40:34.440 --> 00:40:39.480
played to score, and it's it's available for scoring on
795
00:40:39.559 --> 00:40:43.000
all the other cards that you played, and then you
796
00:40:43.039 --> 00:40:45.280
flip over the next to last card that you played
797
00:40:45.760 --> 00:40:48.199
and you score it against the two cards that you
798
00:40:48.239 --> 00:40:48.719
have down.
799
00:40:49.599 --> 00:40:51.880
If you play cards. So let's say you play.
800
00:40:51.760 --> 00:40:56.079
The the twelve, and then you play the fifteen after
801
00:40:56.119 --> 00:40:59.280
the twelve, then you also get some other cards. I
802
00:40:59.280 --> 00:41:02.119
can't remember what they're called, but those ones count through
803
00:41:02.159 --> 00:41:05.280
the whole scoring process and anyway, so you just kind
804
00:41:05.280 --> 00:41:07.360
of build up this deck and then you score it
805
00:41:07.679 --> 00:41:10.679
back up the other way it is. It was really fun.
806
00:41:10.800 --> 00:41:12.280
I think it took us like a half hour. There
807
00:41:12.280 --> 00:41:14.320
were four of us playing. It says that you can
808
00:41:14.320 --> 00:41:18.920
play it with ten ages ten plus. You probably can
809
00:41:19.159 --> 00:41:24.840
if you're going to play competitively, as far as like, hey,
810
00:41:24.840 --> 00:41:26.719
you know, I'm stacking all these cards up so that
811
00:41:26.760 --> 00:41:30.159
all the resources on the earlier cards play nicely on
812
00:41:30.199 --> 00:41:33.360
the later cards. A ten year old might struggle with,
813
00:41:33.760 --> 00:41:36.599
you know, planning ahead that far figuring out what to do,
814
00:41:37.159 --> 00:41:40.320
but they definitely play the game right, and enough of
815
00:41:40.360 --> 00:41:43.440
it is common sense enough to where they can probably
816
00:41:43.480 --> 00:41:45.599
at least wrangle their way through a lot of it.
817
00:41:45.719 --> 00:41:48.280
So so yeah, so I'm going to pick that it's
818
00:41:48.320 --> 00:41:50.880
called far Away on the board game.
819
00:41:50.719 --> 00:41:53.920
Pick, and then let's see other picks.
820
00:41:54.159 --> 00:41:57.599
So I think I might have I think I might
821
00:41:57.639 --> 00:41:59.159
have picked this last time, but I'm going to just
822
00:41:59.199 --> 00:42:02.159
pick it again there's a movie that came out. It's
823
00:42:02.159 --> 00:42:05.400
called Truth and Treason by Angel Studios. My wife and
824
00:42:05.440 --> 00:42:08.239
I are members of Angel Guild, so we pay every
825
00:42:08.280 --> 00:42:11.280
month to be part of that. We get to vote
826
00:42:11.320 --> 00:42:14.119
on the movies that they make, and it's also part
827
00:42:14.159 --> 00:42:18.760
of our subscription to the Angel app that Angel where
828
00:42:18.880 --> 00:42:20.760
you know, you can watch videos and you can say,
829
00:42:20.800 --> 00:42:22.800
I don't want any of this kind of profanity or
830
00:42:22.800 --> 00:42:25.280
any of this kind of content, right, so it'll cut
831
00:42:25.280 --> 00:42:27.199
all the sex scenes out of your movies and stuff
832
00:42:27.199 --> 00:42:31.239
like that. But so we wind up getting tickets as
833
00:42:31.280 --> 00:42:33.440
part of our Angel Guild membership to all of these
834
00:42:33.440 --> 00:42:35.480
movies when they come out to theater. So this one
835
00:42:35.559 --> 00:42:37.960
is a World War II film. It's the story of
836
00:42:38.239 --> 00:42:43.880
three young men become disaffected with the Nazi regime during
837
00:42:43.960 --> 00:42:48.320
World War Two after their Jewish friend gets disappeared by
838
00:42:48.360 --> 00:42:53.719
the SS and so they start distributing leaflets by putting
839
00:42:53.760 --> 00:42:57.119
them in mailboxes and stuff and on cars and things
840
00:42:57.159 --> 00:43:01.159
around Hamburg and they get caught, and the movies about
841
00:43:01.199 --> 00:43:04.320
them and you know what happened to them, and so anyway,
842
00:43:04.400 --> 00:43:06.800
it was it was really really good. One of the
843
00:43:06.800 --> 00:43:09.039
things I like about these kinds of movies. I mean,
844
00:43:09.119 --> 00:43:11.039
it's it's a sad story, you know, in the end,
845
00:43:11.639 --> 00:43:13.480
you know the way, the way that it all goes
846
00:43:13.519 --> 00:43:16.360
for them. But it's like, look, you know, how how
847
00:43:16.400 --> 00:43:18.079
willing are you to stand up for what's right and
848
00:43:18.119 --> 00:43:18.639
what's true?
849
00:43:19.039 --> 00:43:20.760
And I think in.
850
00:43:20.800 --> 00:43:24.440
Today's world, in certain parts of the world, yeah, you
851
00:43:24.480 --> 00:43:28.360
may be risking your life, you know where I live. Yeah,
852
00:43:28.599 --> 00:43:30.320
I guess they do kill people for that because it
853
00:43:30.440 --> 00:43:34.079
killed Charlie Kirk. But I don't feel like somebody's going
854
00:43:34.159 --> 00:43:37.480
to kill me for standing up. But I've had people
855
00:43:37.559 --> 00:43:40.280
come after, you know, my reputation and things for things
856
00:43:40.280 --> 00:43:43.400
that I've said. But again it's down to how, you know,
857
00:43:43.480 --> 00:43:44.960
are you willing to stand up for truth?
858
00:43:45.679 --> 00:43:46.639
Are you willing to.
859
00:43:48.239 --> 00:43:50.599
You know, do the right thing even if it costs you.
860
00:43:50.679 --> 00:43:52.639
So anyway, it's called truth and treason. I don't know
861
00:43:52.679 --> 00:43:54.280
if it's still in theaters or not. I think it
862
00:43:54.360 --> 00:44:00.559
still is probably until like Thanksgiving, So yeah, definitely worth seeing. Terrific,
863
00:44:00.639 --> 00:44:03.639
terrific film. And then my wife, My wife and I
864
00:44:03.639 --> 00:44:06.840
are still playing Jaws of the Lion, which is one
865
00:44:06.880 --> 00:44:11.480
of the gloom Haven board game setups, so and it's
866
00:44:11.519 --> 00:44:15.440
basically without a Dungeon Master, self directed D and D
867
00:44:15.719 --> 00:44:21.800
kind of game, and so you know, uh, anyway, the
868
00:44:21.840 --> 00:44:24.159
difference being that you're it's not as free form. You
869
00:44:24.199 --> 00:44:26.880
actually have cards to give you your abilities and so
870
00:44:26.960 --> 00:44:30.239
you play the cards. Yeah, anyway, very fun. So I'm
871
00:44:30.239 --> 00:44:32.840
gonna pick that as well tomorrow.
872
00:44:32.880 --> 00:44:34.239
What are yours? Okay?
873
00:44:34.320 --> 00:44:43.320
Like, I just saw the movie Good Fortune, and uh,
874
00:44:43.559 --> 00:44:45.760
the first time I'm seeing a movie in a in
875
00:44:45.760 --> 00:44:48.039
a theater in New York. I just relocated here, and
876
00:44:48.719 --> 00:44:53.159
I felt that there was a somewhat funny mix of uh,
877
00:44:53.320 --> 00:44:59.199
you know, today's a tech tech bro you know thing,
878
00:44:59.519 --> 00:45:03.519
just this cushion related to together with like all the
879
00:45:03.679 --> 00:45:09.360
apps that we're using daily and how how like people influence?
880
00:45:10.480 --> 00:45:14.760
How are these apps that are designed by supposedly tech
881
00:45:14.800 --> 00:45:18.360
bros Our influence day to day? Kenor is like playing
882
00:45:18.360 --> 00:45:21.239
there as like an angel I don't give like ten
883
00:45:21.239 --> 00:45:24.039
out of ten, but coming from the tech industry, et cetera,
884
00:45:24.199 --> 00:45:28.280
I thought like it's an interesting movie also to see
885
00:45:28.360 --> 00:45:30.760
a bit a little bit how like other people like
886
00:45:30.840 --> 00:45:32.719
see our industry, et cetera.
887
00:45:33.159 --> 00:45:35.079
So I definitely like.
888
00:45:38.280 --> 00:45:38.760
Very cool.
889
00:45:39.639 --> 00:45:42.159
All right, Well, one last thing and then I have
890
00:45:42.239 --> 00:45:43.800
to jump off for a work media, I'm already late
891
00:45:43.840 --> 00:45:47.039
for if people want to check in see what you're
892
00:45:47.079 --> 00:45:47.480
working on.
893
00:45:48.000 --> 00:45:50.280
Check out codo. Where do people go for any of
894
00:45:50.320 --> 00:45:50.760
that stuff?
895
00:45:51.280 --> 00:45:54.400
Yeah, totally so first of all qodeo, dot ai.
896
00:45:54.559 --> 00:45:57.440
From there, we have like everything we are at social
897
00:45:57.719 --> 00:46:02.519
obviously as well personally, I a mar underscore. Mar is
898
00:46:02.559 --> 00:46:07.760
my handler at x Twitter, and we have multiple open sources.
899
00:46:07.840 --> 00:46:10.320
We're actually going to contribute some of them to one
900
00:46:10.320 --> 00:46:11.960
of the open source foundations.
901
00:46:11.960 --> 00:46:14.079
Still learning which one is the best one.
902
00:46:14.400 --> 00:46:18.159
So for example, we have like a pull request code
903
00:46:18.159 --> 00:46:19.960
review agent, so you can find it.
904
00:46:19.960 --> 00:46:21.119
It's called pr agent.
905
00:46:21.400 --> 00:46:23.599
It's very different than our main product, by the way,
906
00:46:23.800 --> 00:46:26.239
very very different, but it is part of like our
907
00:46:26.599 --> 00:46:29.840
collaboration with the community. So this is a bunch of
908
00:46:29.960 --> 00:46:33.119
ways to reach out and we love like hearing the
909
00:46:33.119 --> 00:46:37.079
community code reviews subjective quality is subjective, while we do
910
00:46:37.159 --> 00:46:39.199
need to standardize that. People think about it differently, so
911
00:46:39.440 --> 00:46:43.280
hearing everyone, please reach out and anything and we'll be
912
00:46:43.280 --> 00:46:43.639
in touch.
913
00:46:44.039 --> 00:46:46.760
All right, cool, Well, thanks for coming. This was fun.
914
00:46:47.639 --> 00:46:49.079
Yeah, same here. I really loved it.
915
00:46:49.880 --> 00:46:52.280
All right, folks, we'll wrap it here till next time.
916
00:46:52.480 --> 00:46:52.960
Max Out