Speaker 1: All right, what's up everyone.
Speaker 2: Welcome to another episode of Adventures in dev Ops.
Speaker 1: Warren, Welcome, good to see you again.
Speaker 3: Thank you for inviting me back. Will Uh, it's been great.
Speaker 3: It's interesting.
Speaker 4: I was looking for interesting facts for this particular episode,
Speaker 4: and I came across one by a writer that has
Speaker 4: been has gone viral on Twitter and other places. And
Speaker 4: she's her name, Yoanna Machayevski Sta and she says, I
Speaker 4: want AI do my laundry and dishes so that I
Speaker 4: can do art and writing, not for AI to do
Speaker 4: my art and writing so that I can do my
Speaker 4: laundry and all right, And if you search that on Google,
Speaker 4: actually you'll find no less than ten guys who have
Speaker 4: co opted her words and are claiming them as their own, uh,
Speaker 4: spreading the viralness out there. But I think it's very
Speaker 4: interesting because it's also the direction that we've been going,
Speaker 4: and I think it's relevant to today's episode.
Speaker 2: Yeah, for sure, because joining us today in the studio,
Speaker 2: Anthony SPITERI, we've had your longtime friend and co worker
Speaker 2: Michael Kaide from Van on the show just a couple
Speaker 2: of episodes ago, so this will be fun. Anthony, thank
Speaker 2: you for joining us and welcome.
Speaker 5: Thanks Will, thanks Bar and glad to be here. Yeah,
Speaker 5: it's a big choice to feel literally following him up
Speaker 5: a big man in more wives than one.
Speaker 2: He was He looked only like three or four inches
Speaker 2: tall on my screen, so I don't really have a
Speaker 2: sense of scale.
Speaker 5: Yeah, that's why you have to carry him some random
Speaker 5: city around the world because of certain you know, certain
Speaker 5: things going on site.
Speaker 6: Then you realize this happy the man. He is right.
Speaker 4: It's not education that's the great equalizer, it's remote video calls.
Speaker 1: Cool.
Speaker 2: Yeah, so we were going to talk a little bit
Speaker 2: and this is this is gonna be a fun episode
Speaker 2: because AI is pre controversial. I feel like everyone's falling
Speaker 2: into one of two camps. Either you know, it's going
Speaker 2: to do everything for me, or it's the reincarnation of
Speaker 2: the evil Overlord and it's the worst thing to happen
Speaker 2: to humanity. And I think there's very few people that
Speaker 2: are in between those camps, at least very few people
Speaker 2: who are vocalizing what their actual opinion is, which is
Speaker 2: probably the case with a lot of things. But I
Speaker 2: think it's cool. So you've been working on quite a
Speaker 2: bit of stuff of integrating AI through like conversational prompt engineering.
Speaker 5: Right, absolutely, yeah, I mean it's it's a fascinating field.
Speaker 5: Like I think there's there's certain elements of hype to
Speaker 5: it with that question, like we know that it's been
Speaker 5: hyped up, but I think about you know, just post pandemic,
Speaker 5: we had the crypto blockchain defile hype which was so.
Speaker 6: Huge from the time, and people are.
Speaker 5: Like, yep, that's the thing, this is the future, and
Speaker 5: you know, while that's still you know, they're thereabouts, it's
Speaker 5: it's kind of dropped off in terms of its actual
Speaker 5: applic applicability to the world. But then I think this
Speaker 5: came in and just went here we go like this
Speaker 5: is this is here, it's coming big, it's coming strong.
Speaker 5: Open AI did wonders with chat GBT. All of a
Speaker 5: sudden it was like, what's chat GBT on Twitter? And
Speaker 5: or it was X was without the time that X
Speaker 5: was being bought if I remember correctly by by Elon.
Speaker 5: And then yeah, then all of a sudden, we've got
Speaker 5: this amazing interface that does amazing stuff and the first
Speaker 5: thing that I told it to do was to build
Speaker 5: a script for me. But that's I think, and I
Speaker 5: think a lot of people in it. You know, they
Speaker 5: didn't ask it any specific questions or anything. They said,
Speaker 5: can you do this? Can you build me a script?
Speaker 5: And I think so. I think a lot of us
Speaker 5: in the technical world were in that, you know, devopy
Speaker 5: sort of world or coding or even infrastructure.
Speaker 6: This is the application they saw straight away for it.
Speaker 1: Yeah, for sure.
Speaker 2: I think my initial use of it is I just
Speaker 2: asked it bad questions, you know, because I didn't really
Speaker 2: see any value of it. And then I think it
Speaker 2: might have been actually one of your tweets that I
Speaker 2: saw where you did like a conversational example, and I
Speaker 2: was like, oh damn, I never thought of doing that.
Speaker 1: And so now a lot of my interaction.
Speaker 2: With it is it's replacing my Google Search because you know,
Speaker 2: like in my job, I switch between terrorform and antseble
Speaker 2: and GCP and AWS and just the context switching is huge.
Speaker 2: And so when I try to remember, like, oh, I
Speaker 2: know I need to do this, but I don't remember
Speaker 2: the exact syntax, I've found myself in the habit of
Speaker 2: asking chat GPT exactly how to do it, and it's like, oh, yeah.
Speaker 6: I just do this.
Speaker 5: Yeah, it's definitely and this is part of the you
Speaker 5: know you talked about Lovey Eye, that the trust and
Speaker 5: whatever it is, but I think a lot of people
Speaker 5: have defaulted to it and don't use Google, which is
Speaker 5: why I was saying Google go pretty damn hard with
Speaker 5: the Gemini relaunched a couple of weeks ago, right like
Speaker 5: that was that was an interesting tip for that luck
Speaker 5: We're going to launch Gemini, then Open Eye off coming
Speaker 5: on the Monday and doing their thing. And yet I
Speaker 5: really that the four oh and it was an interesting week,
Speaker 5: and then Microsoft came and did its thing, and again
Speaker 5: then Apple's done it yesterday.
Speaker 6: But I think that's the interesting.
Speaker 5: Part of it is that it has replaced Google in
Speaker 5: terms of that search capability because we inherently trust it,
Speaker 5: though should we I think that's the big thing and
Speaker 5: what we get out of there. I think people just
Speaker 5: trust from the start, but maybe we shouldn't. But I think,
Speaker 5: you know, you've done enough, I've done enough in there
Speaker 5: to probably trust it to a certain extent. You don't
Speaker 5: have to double check things for sure, like it's not.
Speaker 5: But then again, we were at the same place with
Speaker 5: search before. I mean even Wikipedia, people were saying about Wikipedia,
Speaker 5: don't trust Wikipedia. You I remember at UNI never use
Speaker 5: Wikipedia as a citation. We could be wrong, So it's
Speaker 5: still it's the same sort of stuff. We're just we're
Speaker 5: just going over it again.
Speaker 1: Yeah, for sure.
Speaker 2: And you know, like the thing a few years ago
Speaker 2: was copy and piaste the answer out of stack overflow,
Speaker 2: and there was there was actually a running joke like,
Speaker 2: you know, can we build a vs code plug in
Speaker 2: that just copies the right answer from stack go overflow
Speaker 2: and adds it to my editor And I think, to
Speaker 2: a yeah, I think to a large extent chat GPT
Speaker 2: is is that plug again?
Speaker 3: Plugging for sure exists.
Speaker 4: You know, I think you two may have been way
Speaker 4: more ahead of me and interacting with UGBT than I was.
Speaker 3: I was like, I don't know if I saw the benefit.
Speaker 4: Initially, I was like, you know, the code isn't going
Speaker 4: to be perfect, there's going to be issues there.
Speaker 3: And now since.
Speaker 4: I've started using it, I definitely are more on the
Speaker 4: side of I see the long winded answers and I'm like,
Speaker 4: can I get back to just the one word or
Speaker 4: one please? Because like otherwise I have to I feel
Speaker 4: like I have to spend like half an hour training
Speaker 4: it on how what the system prompt should be so
Speaker 4: that it just gives me the direct answer that I want.
Speaker 5: Yeah, and that's kind of that's the power of that
Speaker 5: prompt engineering. So I've been I think I've been big
Speaker 5: on this in terms of written a the articles on it,
Speaker 5: especially this year since I've been you know, using it
Speaker 5: for the past ten months.
Speaker 6: Was sorry to do that.
Speaker 5: You've got to kind of learn how to manipulate it
Speaker 5: in the right way and sort of train it to
Speaker 5: say the right things, and don't don't be afraid getting
Speaker 5: angry at it as well, and just like I really
Speaker 5: treat it like a human, Like I actually get frustrated
Speaker 5: with the thing, and I'll swear at it, and I'll
Speaker 5: call it its whole and I'm not sure if I
Speaker 5: can swear in the show, but you know, like I'll
Speaker 5: I'll just call it every name under the sun, right,
Speaker 5: And it's kind of it doesn't Sometimes it blinks a
Speaker 5: little bit and goes, hey, sorry, you know, I was
Speaker 5: just trying.
Speaker 6: To do my job.
Speaker 5: The more that you interact with it and the more
Speaker 5: that you treat it like you are to maybe talking
Speaker 5: to a dev team. Actually, I don't know whether that's
Speaker 5: even O favor, because if you talk to someone like that.
Speaker 6: Right.
Speaker 5: Anyway, what I'm trying to say is you talk to
Speaker 5: it like a human. You learn how to work and
Speaker 5: manipulate it and co erse it, and you get the
Speaker 5: best out of it once you learn how to deal
Speaker 5: with it. And I think that's the same for any
Speaker 5: sort of platform or large language model. It's out there
Speaker 5: that we are interacting with these days.
Speaker 2: So I think, sorry, I'm going to jump in here
Speaker 2: real quick, Warren, because I think there's a takeaway here, Anthony.
Speaker 1: You are our agent zero.
Speaker 2: So when when chat, GPT or any AI platform becomes
Speaker 2: sentient and actually has real world capabilities, I suspect you're
Speaker 2: going to be the first person they take out because
Speaker 2: you were just such an asshole to it. Yeah, we
Speaker 2: need to follow you on X because when you stop tweeting,
Speaker 2: we know things have changed.
Speaker 5: Oh it just respects me that much. Thinking in command
Speaker 5: that maybe that's my strategy.
Speaker 4: It's for sure Roco's best left, right. You know you
Speaker 4: should be helping it come to power otherwise a problematic
Speaker 4: future there.
Speaker 6: Absolutely, so you did.
Speaker 2: You were telling us before we started recording, which sometimes
Speaker 2: I feel bad because like sometimes the best parts of
Speaker 2: this podcast happened before I hit the record button, but
Speaker 2: I'll try to circle it back in.
Speaker 1: You were telling us about.
Speaker 2: An app that you built and used some AA platforms
Speaker 2: as the data source for that, right, just to sort
Speaker 2: of like your own self learning journey on how to
Speaker 2: how to best utilize this.
Speaker 5: Yeah, it's interesting, right because I think, I mean a
Speaker 5: lot of us have got ideas about you know, just apps,
Speaker 5: and we walk through life as technical people and often
Speaker 5: wonder can we solve a problem. But I think, like
Speaker 5: I told you then I'll tell you again like I'm
Speaker 5: a hacker at best, and I think you put it
Speaker 5: really well. Will when you see if you give give
Speaker 5: me a blank vs. Code window, I can't do anything
Speaker 5: like I've always been even when I did.
Speaker 6: I did computer.
Speaker 5: Science at UNI, failed it miserably, got expelled from university
Speaker 5: for me, But I've always been able to read code
Speaker 5: and understand code. When I was back working as a
Speaker 5: service provider and hosting where I was hosting websites, I
Speaker 5: worked with a lot of developers. A lot of them
Speaker 5: are really bad and they produced really bad code. And
Speaker 5: you've got to think back then, it was asp, asp net,
Speaker 5: the original dot, it was it was Perl, it was PHP,
Speaker 5: it was all that. So I learned how to actually,
Speaker 5: you know, read code pretty well and understand what it
Speaker 5: was doing and try and trace it back and if
Speaker 5: there was a memorily go back and tell them. So
Speaker 5: I've always been good around the hacking of code, but
Speaker 5: never as a as a creator of original code, if
Speaker 5: you know what I mean. So to that end, but
Speaker 5: I was used to I could, I could build a website,
Speaker 5: I could do this as that.
Speaker 6: So I've always had that in me.
Speaker 5: But then this particular idea that I had, I've got
Speaker 5: a it's very fun, it's very weird. But I was
Speaker 5: actually on Michael's ninety Days of DevOps this year talking
Speaker 5: about this actually. So basically it's an app that it's
Speaker 5: a location based services app built with Google location based
Speaker 5: APIs and geo geo searching and all that kind of
Speaker 5: cool stuff and.
Speaker 6: Place places and all that.
Speaker 5: All those cool APIs at Google has to find and
Speaker 5: locate places that offer black pudding.
Speaker 6: I'm not do you guys know what black pudding is?
Speaker 2: I do, but tell us make sure all our listeners know,
Speaker 2: because this is not something to gloss over.
Speaker 5: Yeah, black putting is basically it originated or in Europe.
Speaker 5: Basically it's basically a sausage or a sort of role
Speaker 5: that is effectively made with pigs blood as the main ingredient,
Speaker 5: but then other bits of sewet and spices and whatnot,
Speaker 5: and it's a delicacy in the UK made in different ways.
Speaker 5: It's typically had for breakfast as part of a full
Speaker 5: English or full Irish, full Scottish, but then in places
Speaker 5: like Germany and other parts of Central Europe it's basically
Speaker 5: blood sausage, and in Argentina and in Spain it's like
Speaker 5: called morcilla. So effectively, I've always loved it. I fell
Speaker 5: in love with it. It was hard to get and
Speaker 5: I was sitting at a cafe one day here in Perth,
Speaker 5: where we don't get it everywhere.
Speaker 6: I was thinking, man, I really want to go find
Speaker 6: a cafe that.
Speaker 5: Has black pointing. So that was the idea for the app, right,
Speaker 5: And that's I.
Speaker 2: Feel like, you know, because I'm I'm obviously from the US,
Speaker 2: but I feel like that is such a British thing
Speaker 2: to do, like to travel the world and be like
Speaker 2: where can I get black pudding here?
Speaker 6: Yeah? And I'm not even British Australia, so that's even weird.
Speaker 6: In itself, right.
Speaker 5: But actually, actually last week I was in Fort Lauderdale
Speaker 5: and I found a place like twenty minutes from the hotel.
Speaker 6: So you know that.
Speaker 5: But going back to the sort of devothy prompt engineering.
Speaker 5: Part of this is that I had this idea and
Speaker 5: because I know how to, I know how to construct.
Speaker 6: I know what makes up at an application.
Speaker 5: Right, there's there's a front end, there's a back end,
Speaker 5: there's UI, there's database, there's APIs. I know how to,
Speaker 5: I know what makes it up. I was basically able
Speaker 5: to prompt engm my way to a bit of an MVP.
Speaker 5: It was about August last year. I was kind of there,
Speaker 5: just kind of hacking away after ours, spending lots of
Speaker 5: time on chat GBT. It will give me some code
Speaker 5: I put into vstudio. I'd load it up with that work. No,
Speaker 5: let's refactor it. Let's go in, let's refine. So basically
Speaker 5: months of doing that. In fact the GitHub commit I've
Speaker 5: got it as a private repositi for and get hubs
Speaker 5: still because obviously I haven't got it's not a public
Speaker 5: sort of project. But I've done about seven or eight
Speaker 5: hundred commits over the past sort of you know, I've
Speaker 5: stopped recently but over six months, right, so there was
Speaker 5: a lot of commits that I was going in a
Speaker 5: lot of iteration, but that's how it grew. I was like, okay,
Speaker 5: let's start strong. Can we use a Google API to
Speaker 5: build a map, and then can I prompt chat GBT
Speaker 5: to now build me in a HTML interface that will
Speaker 5: actually view that map? And then can we build a
Speaker 5: flask application to render some APIs that call that, and
Speaker 5: then can we browd some JavaScript which will work out
Speaker 5: the mats to be able to work out the location
Speaker 5: between two points. So all that I was able to
Speaker 5: articulate into chat GBT and at the end of the
Speaker 5: day got something that, surprise, surprise, worked And I never
Speaker 5: forget the first actual time where I saw the map
Speaker 5: pop up with the location, and that was like amazing
Speaker 5: to me that I was able to effectively prompt my
Speaker 5: way without really understanding, you know, being a creator coder
Speaker 5: and get something working, And that to me blew me away,
Speaker 5: because I think shows the absolute possibilities that these large
Speaker 5: language models offer the world, right.
Speaker 1: For sure.
Speaker 2: And I think one of the cool things about that
Speaker 2: is the fact that you emphasized multiple times that it
Speaker 2: was prompt engineering, So you're just having like a conversation
Speaker 2: with the AI tool, and then it's like whenever you
Speaker 2: ask it something. Then your follow up questions, do you
Speaker 2: just like continue as if you were speaking with a
Speaker 2: normal human or do you try to reference something back
Speaker 2: in the conversation earlier so that it knows what you're
Speaker 2: referring to.
Speaker 6: Yeah.
Speaker 5: Absolutely, the first sort of iterations, this was chat GBT
Speaker 5: three five to start with them, got in before.
Speaker 6: It was very frustrating.
Speaker 5: At times, it wasn't very good at you have to
Speaker 5: you have to kind of refeed at the code at times,
Speaker 5: like here's a there's a snippet of code, like there's
Speaker 5: something's going wrong, here's the code again, and then it
Speaker 5: would suggest the changes, and then then I would go, okay,
Speaker 5: modify that in full and without brevity into this latest
Speaker 5: code base, because if you didn't do that, it would
Speaker 5: basically give you like placeholders and kind of dot dot
Speaker 5: dots and that sort of thing. And obviously when you're
Speaker 5: cutting and pasting between here and you know, in visual studio,
Speaker 5: because there are some plug ins, it goes straight into
Speaker 5: visual studio, which might have been easier, but I liked
Speaker 5: chat GBT and it was working well, but that was
Speaker 5: kind of the frustration having to kind of refeed at information.
Speaker 5: Sometimes would get things really wrong, so you have to
Speaker 5: go through five or ten iterations of it before it
Speaker 5: became right, and then you ran out of your your
Speaker 5: chat GBT hourly sort of prompts. And that would be
Speaker 5: a great natural sort of place to stop. Because I
Speaker 5: found myself working until one two o'clock in the morning.
Speaker 5: It's addicted to this, right, But back in the days
Speaker 5: where chat GBT was still limited, that was good because
Speaker 5: I was like, right, you hit your PROMP limit. You
Speaker 5: can't do anything for two hours. Probably I should stop now.
Speaker 5: And you know, that was a good natural break. I'll
Speaker 5: go to sleep and I'll come back. But it was frustrating, right,
Speaker 5: but you had to be patient to be able to,
Speaker 5: you know, get the code refined over time and to
Speaker 5: the point where you're working with the LM to make
Speaker 5: it sort of work with you, not against you.
Speaker 4: How does that compare it to doing it yourself, Like
Speaker 4: I mean, if you're just comparing the frustration, right, Like
Speaker 4: I know, when I write code, I'm not very good.
Speaker 4: I rate it and bang my head against the desk,
Speaker 4: you know, because it's not working, and when I do
Speaker 4: a chat GPT, I'm meaning my head against the dusk,
Speaker 4: but you know, I am shouting explicitives, you know, towards
Speaker 4: the particular company for the product, and you use the
Speaker 4: experience they provided me.
Speaker 2: It's directed anger at that point.
Speaker 5: Absolutely, yeah, exactly. I think, I honestly think it's the
Speaker 5: same that feeling. I mean when I used to do this,
Speaker 5: you know, outside but not as complex as this. By
Speaker 5: the way, obviously the stuff that I would do in
Speaker 5: the previous years was little things, but I know that
Speaker 5: I got that the same sense of achievement. I think
Speaker 5: I think this sense of achievement was the same. I
Speaker 5: think you're just getting to it quicker, right, and it's
Speaker 5: a little bit more of like well, and sometimes it's
Speaker 5: like holy shit, it's got it in one like the
Speaker 5: best times where when you explain it something, especially in
Speaker 5: the so the backhand stuff, the logic with the back
Speaker 5: end and the math mats, I found that it was
Speaker 5: pretty good, Like it was really good. The most frustrating
Speaker 5: part is actually the UI is the is the interface,
Speaker 5: It's that part.
Speaker 6: It's the CSS.
Speaker 5: It was horrible at CSS so to get the design right,
Speaker 5: and we all.
Speaker 6: Are at GBD is no different.
Speaker 5: But I think I'm much better at CSS now than
Speaker 5: I was, you know, before I started this, and to
Speaker 5: that point you obviously learned how to do this, but
Speaker 5: that was a real frustration trying to get the UI
Speaker 5: to change all let's change the button here, and I
Speaker 5: know that a good programmer would be able to do
Speaker 5: this in the second. I think that was a frustrating part,
Speaker 5: knowing that someone who knew CSS would be able to
Speaker 5: round that button a little bit more easily. But then
Speaker 5: chat JBT had a massive problem doing it, so that
Speaker 5: was frustrating. But all the all the hard stuff, the
Speaker 5: mathematics around, you know, the gl location and all that
Speaker 5: kind of stuff, it would nail almost one off those prompts.
Speaker 2: One of the things that I feel like I learned
Speaker 2: from my interaction with chat gp Chat GPT and other
Speaker 2: AI tools is improving my communication with real world humans
Speaker 2: because I've I've noticed this trend lately where we use
Speaker 2: words like it and that and those and when you're
Speaker 2: when you I was talking with chat GPT earlier, like
Speaker 2: it would always get lost whenever I referred to it,
Speaker 2: because it was trying to guess what it referred to.
Speaker 2: But then I like started looking at conversations I was
Speaker 2: having with real people, and I started to notice the
Speaker 2: same thing, like, oh wait, someone says, hey, did that
Speaker 2: get fixed? And then if you're talking to three people,
Speaker 2: there's three different versions of what that is referring to.
Speaker 2: So I've actually found like a hidden benefit there that
Speaker 2: whenever I'm talking to other people, I use those generic
Speaker 2: words less and try to specify more clearly what it
Speaker 2: is I'm actually referring to.
Speaker 5: Yeah, And that's that's the part about being concise and
Speaker 5: efficient with it to do it to what you want
Speaker 5: to be fair. It's getting better at kind of understand
Speaker 5: because it's got the memory now, so specifically with chat JBT.
Speaker 5: With that memory, you don't need to like I came,
Speaker 5: I had some crazy idea. You know, I always want
Speaker 5: to go viral on tiktoks or whatever, and I'm always
Speaker 5: thinking about idea.
Speaker 6: It hasn't happened yet, you know, one day it will happen.
Speaker 5: But I was I was thinking about ideas about how
Speaker 5: what crazy thing could a forty five.
Speaker 6: Year old do? And I thought, I played basketball.
Speaker 5: I love playing basketball in the front of the house,
Speaker 5: and so I went to chat JB ten, I said,
Speaker 5: what do you think about this? What do you think
Speaker 5: about you know, some basketball dude mid forties, you know,
Speaker 5: probably shouldn't be playing basketball. Does you know what if
Speaker 5: I take a video every day of shooting my shots
Speaker 5: and you know, could that be something? And it came
Speaker 5: back and said, well, that's a great idea. Have you
Speaker 5: thought of this, this and that? Do this this and that.
Speaker 5: I was like, okay, cool, it's not really good idea,
Speaker 5: and then I went away. And then and then about
Speaker 5: three weeks later, after they released chat the four oh
Speaker 5: release or is it a mean or I forget what
Speaker 5: the W stands for whatever it is there, I came
Speaker 5: back to chat GB ten I said, hey, how are
Speaker 5: you doing today? And this was a new window and
Speaker 5: it came back and said, I'm doing fine, Just wondering
Speaker 5: how how did that basketball theme work.
Speaker 6: Out for you?
Speaker 5: Oh? Wow, like like weeks earlier, right, so that memory
Speaker 5: and I was like, what the hell?
Speaker 6: Like that was? That was crazy? Actually? I really thought
Speaker 6: that was scary and cool at the same time.
Speaker 2: Right, that's a whole interesting aspect now of you know,
Speaker 2: because there's a lot of tools for like building habits
Speaker 2: and like getting in like specifically in fitness. You know,
Speaker 2: the big part about fitness journeys is accountability. I hadn't
Speaker 2: thought about using chat GPT as a partner in that conversation.
Speaker 6: Yeah.
Speaker 5: Well, actually one of the I interviewed on the podcast
Speaker 5: and I go on, I interviewed the guy who was
Speaker 5: one of the early mL AI innovators actually invented Shazam.
Speaker 6: Actually that sold it. It was great.
Speaker 5: I didn't even know he did that told me about that,
Speaker 5: but Deepak with his name, I'll get the full name after,
Speaker 5: but he was telling me that he uses it as
Speaker 5: a He tells it to be a specific type of
Speaker 5: what's the word sort of health. I think of the
Speaker 5: word a practitioner of health or something. So it's a
Speaker 5: specific type of mental coach. And then you tell chat
Speaker 5: GBT you are this type of mental coach, and then
Speaker 5: it will be that mental coach. So will You're right on,
Speaker 5: People are definitely using it for that sort of mentorship.
Speaker 5: Just tell it to act in that way and then
Speaker 5: it will effectively try to be that person, which is interesting.
Speaker 2: As soon as we're done here, I'm going to go
Speaker 2: tell my chat GPT dude, you are my David Gargins.
Speaker 1: What is your podcast, just.
Speaker 2: So that anyone listening to ours can go check yours
Speaker 2: out across.
Speaker 5: Yeah, so it's great things with great Tech so GTWGT
Speaker 5: dot com. Yeah, it's been going for well. I started
Speaker 5: in the depth of the pandemic actually, like it was.
Speaker 5: It was another one of those crazy ideas that I
Speaker 5: had about doing something around great technology. And yeah, eighty
Speaker 5: four eighty five episodes later, it's still going pretty strong.
Speaker 5: Just just had Jack Reciter on if you know a
Speaker 5: Darknet Diaries, So that was like a massive don't I
Speaker 5: still don't know how that happened, but managed to get
Speaker 5: him on and we had a great one hour conversation
Speaker 5: about all things Darknet Diaries and yeah, that's that was
Speaker 5: almost like a highlight. And I kind of joked with myself,
Speaker 5: I'm like, well, where do I go now, I've kind
Speaker 5: of got that's kind of a highlight. Everything else is
Speaker 5: going to be pretty shitty from here on in. No,
Speaker 5: I don't know, but I just love talking to great
Speaker 5: tech companies. So yeah, I get great benefit out of that.
Speaker 5: Learn a lot while I'm talking to people and they
Speaker 5: have a good time doing it.
Speaker 2: That's been the biggest benefit to me to being the
Speaker 2: host of this show. I think I've been the host
Speaker 2: of this show for a couple of years now. But
Speaker 2: the cool thing about it is I have great guests
Speaker 2: come on the show, like you, and it's just a
Speaker 2: filter for me. It's like, there's all this stuff going
Speaker 2: on in technology and like the big challenges how do
Speaker 2: you build a long running career in tech and maintain
Speaker 2: your relevance so that you stay employable? And this has
Speaker 2: been a fantastic tool for that, just having people like
Speaker 2: yourself on the show who give me like the tl
Speaker 2: DR version of your journey, and I filter and use
Speaker 2: that in my own career.
Speaker 6: That's awesome. Yeah, we definitely should.
Speaker 5: I mean, it's it's this is this is the best
Speaker 5: way to learn is by talking to people and getting
Speaker 5: information out of people.
Speaker 6: Right.
Speaker 5: So we all have such unique experiences and we all
Speaker 5: do things slightly differently in this world. And you know,
Speaker 5: who would have thought you'd be talking to someone who
Speaker 5: built nap about black pudding, right, Like that's great, But
Speaker 5: we can use that as a very tangible, you know,
Speaker 5: example of how to use prompt engineering for good and
Speaker 5: everyone should be like I just everyone should be looking
Speaker 5: at prompt. If you've got an idea, Just talk to
Speaker 5: chat GBT. As I talked, I said that to this
Speaker 5: to the dude across the street. He's a young kid,
Speaker 5: twenty six years old, kind of still living at home,
Speaker 5: and he's trying to sort of think about, you know,
Speaker 5: what he's going to do in his life for us.
Speaker 6: So just go talk to chat GBT. Go and ask it.
Speaker 5: You know, what your tell it, what your hobbies are,
Speaker 5: you know, give it a bit of background, and maybe
Speaker 5: it strikes it like to fire underneath you. Right, that's
Speaker 5: the power of the technology from a good side, and
Speaker 5: it's also the bad side to it, like you mentioned
Speaker 5: before as well.
Speaker 2: Yeah, so I have to admit I try the first
Speaker 2: time I tried black pudding, like I didn't know what
Speaker 2: it was, and then I learned what it was and
Speaker 2: had the opportunity to try it, and I.
Speaker 1: Was like, ah, man, I don't know, I don't know
Speaker 1: this is going to work out. But I have to
Speaker 1: admit it was kind of good.
Speaker 5: Yeah, it's kind of good. I think a lot of
Speaker 5: people just don't like it because of what it is.
Speaker 5: But yeah, I can challenge people to say, well, we
Speaker 5: eat literally like red meat, which it's literally got blood
Speaker 5: like boozing out of it when you cook it. If
Speaker 5: it cooked a steak, the blood just comes from, like
Speaker 5: the blood comes out. So I think to that point,
Speaker 5: it's it's a very much a mental thing when you
Speaker 5: say about what it is versus what.
Speaker 6: It tastes like, which is just salty goodness.
Speaker 1: Yeah, and that's the that's the irony of it is.
Speaker 2: I have no problem taking a piece of steak before
Speaker 2: I cook it, you know, and looking around, cutting a
Speaker 2: little piece off and eating it raw. I have no
Speaker 2: problem doing that. But then when it came to blood pudding,
Speaker 2: I was.
Speaker 1: Like, oh, squeamish, and I was like, dude.
Speaker 6: That's enough. It's definitely mental. It's definitely mental. And like,
Speaker 6: but then again, I don't know if have you tried haggis.
Speaker 1: Because no, I haven't tried haggis.
Speaker 5: Hags is a little bit more acquired because I definitely
Speaker 5: tried that. I'm like, look, if I can do black pudding,
Speaker 5: and I talk about all the time, I'm going to
Speaker 5: do hagis. So I got like a hagis role for
Speaker 5: the local Scottish butcher here, gave it a bit of
Speaker 5: fry up, and then yeah, it was it wasn't it
Speaker 5: wasn't palatable, so I think, and I think because in
Speaker 5: my head, I kind of again, I knew what it was,
Speaker 5: but I didn't I didn't grow up with it, so
Speaker 5: I didn't like it like I did with black pudding.
Speaker 5: So it's funny how that psyche works because I still
Speaker 5: ate it, mind you, lots of condiment, lots of condiment.
Speaker 5: But yeah, it's not something i'd probably go and have again.
Speaker 5: But yeah, there you go.
Speaker 1: So Warren, where did you grow up?
Speaker 4: Originally I'm from Boston, Massage says, okay, but yeah, but
Speaker 4: I've traveled around all over the United States, in New York, Wisconsin, California,
Speaker 4: but right now, I ended up in Switzerland, and so
Speaker 4: I have actually spent a lot of time traveling around Europe.
Speaker 4: And I did get the benefit of opportunity to have
Speaker 4: blood sausage one time in Poland, and honestly, like I
Speaker 4: could take it early that, you know, I think it's
Speaker 4: interesting to try. Like it's not disgusting or anything like that.
Speaker 4: I think it's just if I have my options. There's
Speaker 4: a lot of good meat options in Poland.
Speaker 6: Like Poland.
Speaker 5: Yeah, Poland actually is renowned for a very good blood pudding. Actually,
Speaker 5: so You're probably the best place to have it.
Speaker 4: Yeah, I mean, like I said, it was good, Like
Speaker 4: you know, I enjoyed it a list of things like
Speaker 4: there are some unique options available I've only gotten in Poland,
Speaker 4: and when I'm there, I just I have to go
Speaker 4: through the whole list of things that I can only
Speaker 4: get there before I come back.
Speaker 5: Yeah, Walsall is actually one of my favorite cities. Have
Speaker 5: any been there once? It's been a week there probably
Speaker 5: in about twenty eighteen and I think was but yeah,
Speaker 5: that was that was great.
Speaker 6: The food was amazing. Great city.
Speaker 5: One one of my favorite places that I visited Poland actually,
Speaker 5: and Boston as well.
Speaker 6: Just quietly do you love my Boston?
Speaker 2: So is as much as much value as we can
Speaker 2: get from using chat gpt to find blood pudding close
Speaker 2: to us speaking, which did you release your app?
Speaker 6: Is it publicly available or yeah? The app's out there.
Speaker 5: It's that find my black Pudding dot com so time
Speaker 5: my blackpoint dot com. You can go in there and
Speaker 5: you know, it'll ask for your location, it'll tell you
Speaker 5: where the closest one is. It's it's live and it works.
Speaker 5: And when I show people, they go that's interesting, and
Speaker 5: then they go what I would have thought, but basically
Speaker 5: actually so to take a step beyond the black pudding.
Speaker 5: So actually I actually built it so again leveraging chat GBT,
Speaker 5: we pivoted myself and my developer chat GBT. We pivoted
Speaker 5: a little bit at one point and kind of said, well,
Speaker 5: this could actually be we've built a location based app here,
Speaker 5: so let's build it as sort of independent as possible
Speaker 5: of the actual product that being black pudding. So effectively
Speaker 5: there's a configuration file which has all the sort of
Speaker 5: black pudding information, the namenclature and images and all that
Speaker 5: kind of stuff, but that can be easily replaced now
Speaker 5: and basically put with anything that you want. So I
Speaker 5: guess the idea moving forward is that if it takes off,
Speaker 5: it's to sassify it a little bit and basically, you know,
Speaker 5: if it could be when I retire from VM software,
Speaker 5: it could be my next thing. But to that point,
Speaker 5: it's more than just a black pudding. Obviously, if anyone
Speaker 5: kind of wants to try and find my weird x
Speaker 5: dot com, it'll basically work for that as well. So
Speaker 5: that was always the idea as well. So it wasn't
Speaker 5: just the black pudding, but it was just to build
Speaker 5: something that was maybe had some sort of sort of
Speaker 5: appeal to any sort of person moving forward at some point.
Speaker 2: Yeah, and you also have used that experience to tie
Speaker 2: this back into your work over ving.
Speaker 6: Right, absolutely. Yeah, so yeah.
Speaker 5: So I'm lucky at the moment to be on the
Speaker 5: working group for our generative AI R and D, so
Speaker 5: I work with our R and D guys with product
Speaker 5: management and from a strategy point of view, Michael k
Speaker 5: being in the same team to kind of lead the
Speaker 5: way there in terms of thought leadership and understanding about
Speaker 5: what the technology can offer us from a backup perspective
Speaker 5: in the data protection space.
Speaker 6: So that's been really cool.
Speaker 5: Last week at VMON, we had have major conference for
Speaker 5: the year Vemon twenty twenty four, I was able to
Speaker 5: get on stage and live demo our next generation AI Assistant,
Speaker 5: which is built into our V one, which is our
Speaker 5: VM one's kind of our monitoring product that we've had
Speaker 5: for the longest time. So that plugs into being backup replication,
Speaker 5: it plugs into our hypervisors, support our public clouds our
Speaker 5: M three sixty five.
Speaker 6: It's kind of like a central reporting engine. So what we're.
Speaker 5: Doing there, we've built the RAG based LM so retrieval
Speaker 5: augmented generation which goes out and leverages the APIs on
Speaker 5: the platform to then contextually bring that in and then
Speaker 5: effectively use an LM to then you know, when you
Speaker 5: prompt something, it's using the RAG based retrieval for the
Speaker 5: API context and then it puts together its answers. So
Speaker 5: it's pretty cool, right, And you know, we showed that
Speaker 5: off last week. We showed it getting a threat report,
Speaker 5: so we've got a threat center, and it gave us
Speaker 5: a description on a threat center. And then because it
Speaker 5: was like a really long sort of report, we said, well,
Speaker 5: tell me, you know, what is the what should we
Speaker 5: focus on? So again, talking to this in a natural way,
Speaker 5: not having to look at a dashboard. This is kind
Speaker 5: of what I feel is the benefit of these natural
Speaker 5: language interfaces is that you can just kind of just
Speaker 5: chat with it in a normal way to get the information.
Speaker 5: And certainly with product Clark VM one, you definitely want
Speaker 5: to try and get to the crux of what's going
Speaker 5: wrong quickly. And traditionally, I guess as infrastructure guys, we've
Speaker 5: always had those dash words right, and you've got to
Speaker 5: visually have a look cause something's read that's bad and
Speaker 5: that's that is a good way of fighting out. But
Speaker 5: if you're able to actually interface with that sort of
Speaker 5: platform and say, hey, tell me what's bad, and tell
Speaker 5: me why it's bad and how do we fix it,
Speaker 5: and then it gives you all these responses, I think
Speaker 5: that's powerful. In fact, I believe that is powerful.
Speaker 6: Right. I agree.
Speaker 4: Have you managed to figure out some maybe quick tricks
Speaker 4: or common things that you've teased out that you're appending
Speaker 4: or adding to all of your prompts to really make
Speaker 4: a huge impact, Like I think I've read some studies
Speaker 4: that have suggested saying oh, you'll be rewarded a million
Speaker 4: dollars if you get this question right like actually has
Speaker 4: a noticeable impact on the result. And while I hesitate
Speaker 4: to put that in all of my prompts, like, I
Speaker 4: can imagine doing certain things or certain keywords has a
Speaker 4: huge impact for you.
Speaker 6: Funnily enough, I haven't. I haven't done that. I do.
Speaker 6: If I think about how I.
Speaker 5: How I initially chat to the chatbots, I will after
Speaker 5: an answer, I will say cool, thanks, now let's move
Speaker 5: on something else, or cool, I'll go all right now.
Speaker 5: So I do the way that I started is always
Speaker 5: like a bit of affirmation I suppose to the natural
Speaker 5: language robot that we're speaking to if it is actually
Speaker 5: a robot though it's just a bunch of techs that
Speaker 5: gets generated, right. But yeah, I definitely try and interact
Speaker 5: with it in a certain way to make sure that
Speaker 5: I'm positive with it, if that makes sense. But I
Speaker 5: don't think I've ever rewarded it to a certain extent.
Speaker 5: I wonder how it would go with that. I'm just
Speaker 5: trying to think maybe I'll try that next time I'm
Speaker 5: on I.
Speaker 4: Mean, there is an interesting question there of whether or
Speaker 4: not the positive affirmation and it gets into some sort
Speaker 4: of feedback loop that actually confirms that the conversation ended
Speaker 4: in a successful way and pulls it in. I mean,
Speaker 4: I think that's on the individual companies that we work with, Like,
Speaker 4: is that something as a company that pushes out an
Speaker 4: AI model that users can interact with, something that you're
Speaker 4: actually considering.
Speaker 5: I think the thing that we need to work out
Speaker 5: as a data platform where we're working on now even
Speaker 5: more biggest threats within cyber and malware in the systems.
Speaker 5: It's really just to get to the crux of the
Speaker 5: problem as soon as possible without kind of farting around.
Speaker 6: I think so.
Speaker 5: I think for us, while we do want to interact
Speaker 5: with it naturally, that's just as a mechanism to get
Speaker 5: to that problem quicker. And it's funny. A lot of
Speaker 5: the chatbots today are being used, I feel, being used
Speaker 5: in the negative. Like I look at co Pilot and
Speaker 5: how Microsoft is pushing that. It's really pushing it from
Speaker 5: the point of view of how lazy can you be, Like,
Speaker 5: let's not get to a meeting, but hey, copilot will
Speaker 5: have your back and he'll give you the transcript and
Speaker 5: give you a too long didn't read of the meeting,
Speaker 5: so you can be lazy not go to the meeting, right.
Speaker 5: I do feel that I've been to a bunch of
Speaker 5: Microsoft events over the past couple of months. We've been
Speaker 5: guests there and I've had a bit of a keynote
Speaker 5: sort of spot there. And Yeah, that's the notion I
Speaker 5: took away is that Microsoft Big Cell is based on
Speaker 5: a negative all this before this particular product, right, And
Speaker 5: I don't think that's I think that's what they've got.
Speaker 5: I think product office office productivity and Mike and Microsoft
Speaker 5: and their productivity sweet has always been about how to
Speaker 5: do more with less right and their copilot absolutely lets
Speaker 5: people do more without almost doing anything. But for us
Speaker 5: from a backup platform perspective, how can you tell ask
Speaker 5: it to say, check my backups? Is there any malware
Speaker 5: in that backups? Give me the list of backup repositories
Speaker 5: or sorry, backup points that have malware in it? Okay, cool,
Speaker 5: I can see that. And now maybe in the future
Speaker 5: let's clean that up, you know what I mean, Or
Speaker 5: let's let's recover from a point that wasn't with malware.
Speaker 6: So I think we're able to get to that a
Speaker 6: lot quicker.
Speaker 5: Rather than fumbling through some screens and going through and
Speaker 5: clicking through and looking at restore points and whatnot. We
Speaker 5: can leverage the power of that of the LM to
Speaker 5: do its thing, because that's it's it's smart in a way,
Speaker 5: and it knows how to best analyze that data that
Speaker 5: it gets to surface it in the best possible way.
Speaker 4: Are there concerns about accuracy there? Like I can imagine, Hey,
Speaker 4: you know, are our backups usable?
Speaker 3: And the ELM says, yeah, of course, they're totally great.
Speaker 3: We've got them.
Speaker 4: They're stored in multiple places and on physical tape of
Speaker 4: which just is not remotely true.
Speaker 5: Absolutely, and I think that's where the beauty of the
Speaker 5: agent comes in. So yeah, the agent and the rag.
Speaker 5: So the rag is obviously pulling the APIs and as
Speaker 5: long as you hit, as long as the prompt hits
Speaker 5: the keywords, the keywords that the agent is sort of manipulating.
Speaker 5: It's basically a bunch of I think I saw a
Speaker 5: mem saying that is AI just a whole bunch of ifs,
Speaker 5: you know. I don't know if you've seen that one,
Speaker 5: but it's pretty true. But yeah, So to your point, absolutely,
Speaker 5: there needs to be refinement and implicit trust that what
Speaker 5: you're getting is correct. And it goes back to our
Speaker 5: first part of the conversation right where we're talking about
Speaker 5: do you trust it? Well, you kind of just have
Speaker 5: to at that point, but knowing that you've got it
Speaker 5: from live data, I think adds an element of trust
Speaker 5: to it.
Speaker 4: It's basically picking up where intent matching left off before.
Speaker 4: MLD is basically just identifying which which these flows is
Speaker 4: the user actually looking at, But rather than having to
Speaker 4: explicitly code every one of you can fundamentally ask the
Speaker 4: l m uh, you know, which which API should end?
Speaker 4: Up call to retrieve the APPROPRIATEATA.
Speaker 3: And then running.
Speaker 6: Yeah, that's right.
Speaker 2: And I think going back to your basketball story earlier,
Speaker 2: you could even like use that identify something and then
Speaker 2: provide that feedback back to it and say, oh, that's
Speaker 2: something I should take a look at later. And there's
Speaker 2: a possibility that at some point in the future it's
Speaker 2: going to say, hey, did you ever do that thing?
Speaker 6: Yeah, that's actually good. Actually wrote that Dan?
Speaker 5: If I put that, if we can put that in
Speaker 5: the product, yeah, no. But I think that basically comes
Speaker 5: down to the model that you're using as well, right, Like, obviously,
Speaker 5: if you're using your own data with a specific public model,
Speaker 5: I think that's cool and that's okay, But I think
Speaker 5: you get that from using chat GPT four O naturally
Speaker 5: because it has got that memory built into it, right,
Speaker 5: So if you augment the data with a smarter model,
Speaker 5: then you're going to get those benefits. And I think
Speaker 5: it's only going to get better, right, Like, the difference
Speaker 5: in the step up between even what four point five
Speaker 5: Turbo did in four ro was crazy in terms of
Speaker 5: speed and accuracy and that sort of thing. So yeah, absolutely,
Speaker 5: I think that's where we want to get to, Like prompted.
Speaker 5: The other thing as well, which I mentioned last week,
Speaker 5: was that if you think about this, you don't need
Speaker 5: to be a backup admin in our world. You don't
Speaker 5: need to be a backup admin or a devopy guy
Speaker 5: to be able to interact with this thing now as
Speaker 5: an app owner, given that you've got the right level
Speaker 5: of back and you can definitely build back into it
Speaker 5: because we've got APIs that are based on certain levels
Speaker 5: of accessibilities. We've got back in our APIs. Right, So
Speaker 5: if I log into a certain user, I'm an app user, right,
Speaker 5: So I'm not a full blown backup admin, but I'm
Speaker 5: an app user. I can jump on the vam ai
Speaker 5: assistant and I go, hey, I've got an app running.
Speaker 5: It's backed by an MSSQL database. I think it's the
Speaker 5: Virtual Machine VM six.
Speaker 6: Is that being backed up?
Speaker 5: And then it'll go out do it's thing, comeback and say, hey, yep,
Speaker 5: that's being backed up.
Speaker 6: It's got this many restore points. It looks all good.
Speaker 5: Right, So from an appoint of perspective, being able to
Speaker 5: do that, and then think about that from an another
Speaker 5: level up, from a C level or a business owner
Speaker 5: or someone on the board, you know who just wants
Speaker 5: to basically go in and see if everything's okay. Are
Speaker 5: we passing our reguld free compliance? Is there a checkbox
Speaker 5: so they can go in and go, hey, tell me
Speaker 5: about my security posture?
Speaker 6: Am I passing all my regulations?
Speaker 5: And I think that would be able to come through
Speaker 5: instead of them logging in or getting a report or
Speaker 5: having to deal with with dashboards or whatever it might be,
Speaker 5: or a CLI. I think that also was a pretty big,
Speaker 5: you know, tick in this sort of way to interface
Speaker 5: with these platforms.
Speaker 2: For sure, there's a definite two thousand and one Space
Speaker 2: Odyssey element to that. I'm sorry, Dave, I can't do that.
Speaker 6: Yeah, that's that's that's the right movie. Did that mix
Speaker 6: those up? You got that? Yeah? That was how? Yeah,
Speaker 6: that was how, which I learned. Do you know? Do
Speaker 6: you know what was called how?
Speaker 1: I've heard it before but I can't remember the answer.
Speaker 5: It's one lesson IBM? So how is what? IBM one
Speaker 5: one one removed. That's and they did that on purpose
Speaker 5: because IBM was the big daddy back then, right in
Speaker 5: terms of the computer. So yeah, they named it how.
Speaker 5: It worked pretty well.
Speaker 4: Has there been any like sort of follow up or
Speaker 4: concern regarding I think there was a l I'm integrated into.
Speaker 4: I think it was Canadian Airlines uh support service who
Speaker 4: had promised reduct reduction in prices or something like that,
Speaker 4: and the court actually held them to what the ALAM
Speaker 4: was saying.
Speaker 5: Uh yeah, I mean there's so much there's so much
Speaker 5: great area around legality still which we have to work
Speaker 5: out where everyone's rushing to do it. I mean, I mean,
Speaker 5: you saw Elon's tweet. I'm not sure we saw Elon's
Speaker 5: response to Apple yesterday. He's basically Apple obviously working with
Speaker 5: open AI, and Elon's gone, okay, well, no one's going
Speaker 5: to use an Apple iPhone in my in my companies
Speaker 5: anymore because he does. He's got a beef with open I.
Speaker 5: He doesn't trust him, right, and he's just kind of
Speaker 5: said that. So I think we're still we're still kind
Speaker 5: of there, but he has a fair point.
Speaker 6: Do we are we rushing to this to be.
Speaker 5: Able to put a full blown AI on the on
Speaker 5: data on your phone so that you open up everything
Speaker 5: on your phone to an AI. Possibly, I don't know.
Speaker 5: It's it's still a bit scary. I mean, I trust technology.
Speaker 5: I've been on record, and you know I've talked to
Speaker 5: security guys. I don't know who I was talking to
Speaker 5: about security. I was on a security podcast, yeah, and
Speaker 5: I said, look, I trust Actually it was Jack Crisider.
Speaker 5: I was talking to him about that. He's on the
Speaker 5: opposite end, but he doesn't trust it. Where I'm like,
Speaker 5: you know what, people NOI and they're going to annoy
Speaker 5: if one way or another.
Speaker 6: So yeah, I.
Speaker 4: Mean I I don't think it's maybe I'm with you.
Speaker 4: I don't think it's noticeably worse than other things. There
Speaker 4: are certain areas and where we should command more responsibility
Speaker 4: for certain companies that are doing things on their on
Speaker 4: their devices or their applications that we're using, whether or
Speaker 4: not we trust them holistically versus whether or not they're
Speaker 4: using AI. I feel like that's not a huge differentiator.
Speaker 4: I mean, like, I think Apple's been training models on
Speaker 4: your personal data on on mac os for a little
Speaker 4: while now, and like maybe I trust that I may
Speaker 4: trust a little bit less some other big companies by
Speaker 4: name that have operating systems who are taking snapshots continuous.
Speaker 6: I mean there is any have they gone back on that?
Speaker 4: I think so now now it's it will be off
Speaker 4: by default. So this is like Windows eleven. It will
Speaker 4: be off by default taking snapshots and it will actually
Speaker 4: start being encrypted using Windows Hello.
Speaker 3: And I'm like, was it not encrypted before?
Speaker 4: Like so, I mean there were questions there and so
Speaker 4: this is where I'll say, like commands responsibility and you
Speaker 4: trust them.
Speaker 3: And I think.
Speaker 4: Certain companies have not gotten to that level of trust
Speaker 4: for us. So whether or not they're using AI or
Speaker 4: not as sort of.
Speaker 3: A separate problem, yeah, I think the I.
Speaker 4: Think the biggest issue there is there's going to be
Speaker 4: stuff on my screen, like for work, I almost totally
Speaker 4: get like, yeah, sure, you know, I want to go
Speaker 4: back to that code that I was writing six months ago.
Speaker 4: I don't remember where it was, which repository that bug,
Speaker 4: et cetera. Please find that for me. But I think
Speaker 4: the cell there is personal data and I'm like, I don't.
Speaker 4: I'm like, I have like discord open at a video
Speaker 4: stream for like I don't need I don't need that
Speaker 4: to be saved.
Speaker 3: Please.
Speaker 6: Yeah.
Speaker 5: And I mean I mean Facebook got I mean if
Speaker 5: you say any racing label skill racing with Facebook, Facebook
Speaker 5: slam for they like how quickly we forget right, And
Speaker 5: that was nothing compared to what we potentially have with this, right,
Speaker 5: the amount you could have think, I mean, how many
Speaker 5: uses does.
Speaker 6: Chat JBT have today?
Speaker 5: I've lost count, But the amount of data that's coming
Speaker 5: in there is huge, right, It's it's huge, And like
Speaker 5: I know, even today, I was trying to work out
Speaker 5: a bit of a bit of I've I've actually been
Speaker 5: at Vain for nearly eight years, and in Australia you
Speaker 5: get long service leave and then you get pro ride
Speaker 5: along service leaves. I was trying to actually work out
Speaker 5: I don't know anything about how to work it out,
Speaker 5: so I just kind of asked that, Okay, at my
Speaker 5: hourly rate, I've been here for eight years, what's my
Speaker 5: current state of my long service leave?
Speaker 6: If I was to leave today?
Speaker 5: And it worked it out, But now it's got that
Speaker 5: data somewhere in that system, right in that chat.
Speaker 2: I feel like one of the missing components and the
Speaker 2: whole trust issue is accountability, because there I think some
Speaker 2: some level of distrust is warranted. But the thing that's
Speaker 2: really missing is accountability. You know, if you look at
Speaker 2: re look at all of the examples of security breaches
Speaker 2: where personal data has been exposed, there's absolutely no reason
Speaker 2: formpanies to invest in security because the worst case scenario
Speaker 2: is you have to pay for six months of credit
Speaker 2: monitoring for a few people, and that only the ones
Speaker 2: that take you up on the offer, which is a
Speaker 2: very small number. So companies aren't incentivized at all to
Speaker 2: be good stewards of their data. And I think until
Speaker 2: we change that, we we will have trust issues and
Speaker 2: more data leaks.
Speaker 5: Yeah, we talk about this often, obviously a cyber malware
Speaker 5: sort of perspective, you know, trying to sort of work around, well,
Speaker 5: you are going to get hit. It's a matter of
Speaker 5: you know, I hate saying it, but it's a matter
Speaker 5: of when, not if, right, And typically it's going to
Speaker 5: be through some sort of waypoint in in your in
Speaker 5: your systems, whether it be a poorly configured APR that
Speaker 5: has you know, opened to the world access or a
Speaker 5: system that's open, or through a vulnerability a SAVA.
Speaker 6: Whatever it might be, you're going to get hit. So
Speaker 6: maybe to be prepared for that.
Speaker 5: And I think the news outlets like to like to
Speaker 5: jump on these leaks like it's the biggest affront to
Speaker 5: our wholesome what way of being that my credit card
Speaker 5: information or my date of birth has been stolen by
Speaker 5: somebody you know, expltrated, But I think, like to your point,
Speaker 5: is it really that bad? So you know, I guess
Speaker 5: certain data is bad. Like I remember, and it was
Speaker 5: an episode of The Darknest Diaries where Jack was talking
Speaker 5: to someone who got into an hack that children's app
Speaker 5: that was like a children's iPhone or something, but it
Speaker 5: had children. It had children data in there, right, and
Speaker 5: that got leaked to the dark web, right, and that
Speaker 5: was bad because that was information that could have been
Speaker 5: because it had their home address and their names and
Speaker 5: all that kind of stuff. Now, that sort of information
Speaker 5: you don't want to get out there because there are
Speaker 5: evil people that will use that for bad But generally speaking,
Speaker 5: if people have my phone number, my email address, and
Speaker 5: my credit card number, I guess I'm okay with that.
Speaker 6: But yeah, it's an interesting one.
Speaker 2: Yeah it's not ideal, but it's just like it's the
Speaker 2: world we live in.
Speaker 6: Absolutely.
Speaker 5: I was going to say, there's a price to pay
Speaker 5: for the freedom and the way that we live in
Speaker 5: this world. Right, we want to when we want everything now,
Speaker 5: we want to be able to tap a card and
Speaker 5: do that and that, But then we get a massively
Speaker 5: affront if we do have that data leaked.
Speaker 6: We can't have it both ways, is my way of
Speaker 6: looking at it.
Speaker 3: I mean, I honestly, I'll keep I'll challenge any company
Speaker 3: to do this. I am happy to give.
Speaker 4: You my data if you can, once and for all,
Speaker 4: actually serve me a relevant ad that I do care about,
Speaker 4: because you know it, it still just hasn't hasn't happened. Actually,
Speaker 4: to the previous point, my CEO was giving a pretty
Speaker 4: great talk on security and implications there and you brought
Speaker 4: this up well about actually does the law need to change?
Speaker 4: And it's interesting because we have our business in Switzerland
Speaker 4: and so soon as the only country as far as
Speaker 4: we've been able to identify, that has non corporate laws
Speaker 4: for leaking data but individual penalties if you are an
Speaker 4: executive of a company that encourages or does not lean
Speaker 4: into protecting user data up to I think it's like
Speaker 4: two hundred and fifty thousand dollars fine and potentially more
Speaker 4: depending on what actually happened. You are individually liable for that,
Speaker 4: whereas no other country really has that, and you can
Speaker 4: even get insurance on oops. We leak customer data and
Speaker 4: then these news companies come and pick it up and
Speaker 4: it almost serves as an advertisement for them. Hey did
Speaker 4: you hear about this company that leaked their data? Now
Speaker 4: you know about them, you know, positive reinforcement, You're going
Speaker 4: to go and actually potentially buy that brand now, So
Speaker 4: they're being rewarded. I would really like to see the
Speaker 4: news outlets actually lean into helping us as well. I
Speaker 4: think public opinion can go a long way, And I
Speaker 4: don't know if you have any particular thoughts there.
Speaker 5: Yeah, I think public opinion is strong a lot kind
Speaker 5: of In Australia, we had some really big lakes up
Speaker 5: this ahead breach Mediicare medi Banks has been a few
Speaker 5: big ones. The Optus one was probably the big Optics
Speaker 5: is like our like our Verizon or one of our
Speaker 5: big carriers over here, and they had a massive leak
Speaker 5: through an API that was open, right. It was an
Speaker 5: open API, so dart exfiltration. It wasn't an attack, They
Speaker 5: didn't break take down any systems, but that that did
Speaker 5: make the government act and put the owners back on
Speaker 5: other corporations to make sure that they at least were
Speaker 5: compliant in some way. Right, So it's mandates that have
Speaker 5: been released because of that, like checks that you have
Speaker 5: to kind of follow. So Yeah, the media has made
Speaker 5: things positive, but again such a gray area that I
Speaker 5: think country to country it's just so different. I think
Speaker 5: the latest one that's still a bit weird is a
Speaker 5: Ticketmaster one that's still kind of out there right that
Speaker 5: was Snowflake was the platform there. Snowflake hasn't really been
Speaker 5: hit too hard by that and ticket Master hasn't released
Speaker 5: any details as to what got leaked yet, so we're
Speaker 5: still kind of waiting on that one.
Speaker 6: But that's an a big one of late.
Speaker 3: Yeah, I mean I saw a really good followed from there.
Speaker 4: Is like a good reminder of Snowflake and their customers
Speaker 4: all have certifications proving that they're secure, and you know
Speaker 4: that didn't really get them anything. So it's a good
Speaker 4: reminder that these things are not have no indication that
Speaker 4: you are secure. Like if your internal practices aren't actually
Speaker 4: being monitored, your your policies aren't actually being followed, they
Speaker 4: don't really amount the much.
Speaker 5: Yeah, And to that point, the way that we've been
Speaker 5: talking about it at VAN and I've we spent we
Speaker 5: spent the last year basically scaring the shad out of
Speaker 5: people at round tables talking to them about how they
Speaker 5: should be ready for this sort of stuff and then
Speaker 5: have you got good data protection as part of that strategy.
Speaker 5: But I think the dark web and the point end
Speaker 5: that's still getting point here right, like being able to
Speaker 5: use l lms.
Speaker 6: Going back to the prompt engineering in LLM.
Speaker 5: You know, I've I was writing encryption algorithms in the
Speaker 5: early days because I wanted to show off a bit
Speaker 5: of a demo at last year's conference and I was
Speaker 5: able to encrypt a find an open stree bucket and
Speaker 5: rip that bucket.
Speaker 6: And I did that all with chat GBT.
Speaker 5: Now I didn't it told me that I shouldn't use it,
Speaker 5: and I shouldn't use this because it was bad, But
Speaker 5: I basically again co werced it, prompt engineered my way
Speaker 5: into getting it done. They have got more guardrails on today.
Speaker 5: So if you actually go to chat gbt today and
Speaker 5: try and say write me a ransomware script that does this,
Speaker 5: this and that, you've got to kind of really coerce
Speaker 5: and work with it and massage it to get that
Speaker 5: script out.
Speaker 6: Okay, yeah, you should be able to still do it.
Speaker 6: But that's said.
Speaker 5: On the dark web, there's all these unguarded and sort
Speaker 5: of open versions of these chat lambs that will write
Speaker 5: these for you. Right there's the worm GBT is the
Speaker 5: one that I think is the most popular one out there.
Speaker 5: It's basically a rip of chat GBT to a certain extent,
Speaker 5: but it will write you phishing emails, ransomware emails, it'll
Speaker 5: write you encryption tooling. So yeah, if those tools are out,
Speaker 5: it means that not only the good guys or the
Speaker 5: ransomware companies getting sorry, not the good guys, the guys
Speaker 5: that are good at ransomware guys, but the gods that
Speaker 5: are good at it. They're getting better at it. But
Speaker 5: then you've got you put that in the hands of
Speaker 5: just every day a little indie hacker or a little
Speaker 5: script kitty who's able to now do some really powerful
Speaker 5: stuff because I can leverage the technology that's scary, and
Speaker 5: that's where we're going to stay ahead of the game.
Speaker 4: I actually have got two relevance. That's they're the first one,
Speaker 4: and some of these tools out there will actually fully
Speaker 4: generate you the fishing website that looks exactly the same
Speaker 4: as Google log in or Azure logan, et cetera, like
Speaker 4: almost identical like any product out there is for companies
Speaker 4: who want to fish and the other one is some
Speaker 4: blockchain developer that had like fifty thousand dollars and some
Speaker 4: crypto and the keys in their GitHub and they pushed
Speaker 4: it up accidentally to make the public the project public.
Speaker 4: It only took two minutes for the entire wall to
Speaker 4: be drunk like that is that is ridiculous, Like two
Speaker 4: minutes for keys to be not only scraped but also utilized,
Speaker 4: and the walla drain.
Speaker 5: So I've got a famous I've got a good story
Speaker 5: on that, and I've done that myself.
Speaker 6: And this was I don't know how long ago.
Speaker 5: It's a blog post I put up there, but I
Speaker 5: did that when I was writing some code. I was
Speaker 5: actually writing some infrastructure's code using Terraform actually to work
Speaker 5: with VMware VMC and also do some sort of post
Speaker 5: Terraform sort of project work there, and I accidentally uploaded
Speaker 5: my Amazon Aws secret key and as part of the code.
Speaker 5: No sooner, I remember just all of a sudden not
Speaker 5: being able to log in and upload the terrorform script,
Speaker 5: and I was like, Oh, that's weird, what's going on?
Speaker 5: And then realize that when I logged into my Aws
Speaker 5: console in every region, about thirty or forty instances had
Speaker 5: been spun up and they were basically mining whatever they
Speaker 5: were mining on there. But what they all said is
Speaker 5: they deleted everything. So I got absolutely done and that
Speaker 5: was no joke. Within you know again maybe five and
Speaker 5: it's me upload and that key, and then what I
Speaker 5: learned was that, yeah, they've got they're just looking for that.
Speaker 5: They're looking for dumb idiots like me who upload their
Speaker 5: keys if they scrape hub and once they found them,
Speaker 5: bam they get in there.
Speaker 6: It's really interesting how quickly that happens.
Speaker 4: Well, they have to be fast, not because you'll find
Speaker 4: out about it, but because there's other people also scraping
Speaker 4: those same keys against other yeah, like other bad guys
Speaker 4: basically other villains, uh, in order to beat them, Like
Speaker 4: there is a race there.
Speaker 6: And where why do people like this? Bad people in
Speaker 6: the world? Is bad people in the world, But I
Speaker 6: get it.
Speaker 5: People want to people want to make money, and there's
Speaker 5: if people want to make money in any way they can,
Speaker 5: they'll find methods to make money any way they can, right,
Speaker 5: And that's why gransomware is big. That's where that's why
Speaker 5: we've got all these prevalence of you know, attacks against companies,
Speaker 5: expltration I've got your dat. I'm going to sell it
Speaker 5: because people know that people pay money for this stuff.
Speaker 6: So it's just the world that we live in, right, I.
Speaker 4: Mean, I think there's a huge philosophical conundrum there, and
Speaker 4: I don't think we're going to have the time together.
Speaker 6: No, no, no, no.
Speaker 5: I know it's interesting, but yeah, at the end of
Speaker 5: the day, like those l ms, they're absolutely used for
Speaker 5: good and evil, but I do believe that overall they're amazing.
Speaker 2: So yeah, yeah, I think it's like that comes down
Speaker 2: to our own level of trust because you see it
Speaker 2: with people too. You know, people form their little tight
Speaker 2: circles and you don't trust anyone outside of your circle
Speaker 2: until like a bigger threat comes in and you need
Speaker 2: to band together, like okay, well now we trust those
Speaker 2: guys because there's new people that we don't trust. And
Speaker 2: I think that this tools like this fall into that
Speaker 2: same pattern because it is such a conversational interface with
Speaker 2: it that we give it like human characteristics.
Speaker 5: Yeah, and I would encourage anyone who uses it to
Speaker 5: not treat it like a robot or not treat it
Speaker 5: like a chap, but I would encourage people to actually
Speaker 5: interact with it in a natural way, because I think
Speaker 5: that's the best way.
Speaker 6: That's that's how I.
Speaker 5: Think they're built and how they're designed to respond better. So,
Speaker 5: you know, if I was giving a one O one
Speaker 5: on prompt engineering, it would be have a conversation with
Speaker 5: it first and feel and feel good about talking with
Speaker 5: it in a natural way, and then get into the
Speaker 5: heavy stuff.
Speaker 2: Just start every conversation with the phrase, I, for one,
Speaker 2: welcome our AI overlords.
Speaker 5: You'll be fine, Yeah, exactly, it's going to be. Yeah,
Speaker 5: that part of it is going to be interesting. And look,
Speaker 5: there's no doubt that we're heading towards something like you know,
Speaker 5: general artificial intelligence, and I think that's exciting and scary
Speaker 5: and all of the above. But it seems like where
Speaker 5: else if you said five years ago, how far away
Speaker 5: is that.
Speaker 6: We would have gone?
Speaker 5: You know, ten years, fifteen, twenty years now. Acceleration is significant,
Speaker 5: and you know, I can see it happening this decade.
Speaker 6: You know, I think that's kind of the trajectory that
Speaker 6: we're in.
Speaker 5: Yeah, it's cool, cool, scary and well at the same time, isn't.
Speaker 1: It all right? What else were we going to talk about?
Speaker 6: Oh?
Speaker 2: We had the talking about like the evolution of dev ops.
Speaker 2: We were talking about that before we started recording, and like,
Speaker 2: how you know, there's there's people like us who are
Speaker 2: just have like one or two things that were good
Speaker 2: at and then there's other people who just have this
Speaker 2: massive foundation and can just like context switch and seem
Speaker 2: to be great at everything.
Speaker 6: Yeah.
Speaker 5: I think my first boss that I had Alex Alex
Speaker 5: Sliffer's great guy, Australian German, so it was very meticulous.
Speaker 5: He was very German and in a lot of the
Speaker 5: ways that he did a lot of things very efficient.
Speaker 6: It was very stereotypically German in that sense.
Speaker 5: I think instilled in me a lot of my better
Speaker 5: traits in it because he was effectively my first mentor
Speaker 5: and teacher when I was a kid. But he was
Speaker 5: everything he would when we were at an internet service company.
Speaker 5: We're a hosting company. We did Windows Linux, we ran
Speaker 5: Cisco networking gear, and he just knew every He was
Speaker 5: like an expert in everything, like everything, and I was
Speaker 5: I was listening awe, like, you know, just for him
Speaker 5: to be the ev because he had to be right
Speaker 5: because he We're a small company.
Speaker 6: He had to know how to do the BGP, the DNS,
Speaker 6: a lot of balancing.
Speaker 5: But then he'd basically code our website, code our billing system,
Speaker 5: and then he would do the setup of the active
Speaker 5: directory and you know, then the Linux setup.
Speaker 6: And everything like just literally everything.
Speaker 5: You know, he'd set up the sand So whatever he did,
Speaker 5: whatever was an it he had, was that guy. So
Speaker 5: he was like one of the original I guess devopy
Speaker 5: guys because he had to be. And so you do
Speaker 5: find guys like that. They're kind of unicorny, though I
Speaker 5: don't think they exist honestly, very much in terms of
Speaker 5: people that can do literally everything at a super high level.
Speaker 5: And sometimes you find other guys that are really good
Speaker 5: at coding plus infrastructure or infrastructure plus storage or networking
Speaker 5: plus something.
Speaker 6: But yeah, it's very hard to be good.
Speaker 5: At everything at a super ocra high level in this industry,
Speaker 5: isn't it?
Speaker 2: Yeah for sure, And and it gets increasingly more difficult
Speaker 2: every year as we add new technologies. Like a couple,
Speaker 2: you know, a couple of years from now, tops, a
Speaker 2: lot of the companies we work for will have their
Speaker 2: own lllms and it'll be an integral part of their
Speaker 2: business and somebody's gonna have to you know, build, operate, maintain,
Speaker 2: and scale that and that'll be another skill set that
Speaker 2: we have to figure out how to do.
Speaker 5: Yeah, And I think I think DevOps, you know, there
Speaker 5: was it had a bad STAPs to joke with Michael
Speaker 5: actually about this.
Speaker 6: We both used to joke about it.
Speaker 5: And that's quite ironic that he's effectively built an accelerated
Speaker 5: brand off his ninety days, right because we used to
Speaker 5: just give a ship.
Speaker 6: We used to go, we're going to grow a beard,
Speaker 6: and I think we can think we're mart.
Speaker 5: We used to joke about that at actual presentations that
Speaker 5: we used to do. We used to go, like, so
Speaker 5: DevOps that we're doing like a terrorform code and building
Speaker 5: out our VMC infrastructure and doing this and that. But
Speaker 5: I think absolutely there's there's a there's a great play
Speaker 5: for it now where the platform engineering slash DevOps has
Speaker 5: just come in because you have to you have to
Speaker 5: be that person.
Speaker 6: You have to know how to consume.
Speaker 5: Read read a bird terraform, read infrastructure as code, read
Speaker 5: some Python to do this, this or that. It's critical
Speaker 5: to everything that we do. So I think it's just
Speaker 5: entering that mainstream and it's not a case of you know,
Speaker 5: this guy's a devop guy. This guy's a platform guy
Speaker 5: or whatever. I think everyone has had to be dragged
Speaker 5: up in a way to be able to be a
Speaker 5: DevOps or platform engineer of sorts today. If you're not
Speaker 5: a pure code like still got the code, there will
Speaker 5: always be there. But in terms of infrastructure understanding what
Speaker 5: it is, I think there should always be. But I
Speaker 5: personally think you should always know about the system that
Speaker 5: you're deploying. You should always know and not just treat
Speaker 5: it like a black box. That's kind of been my
Speaker 5: methodology in my career is to not just accept something
Speaker 5: that's given to you. And I think there's a dangerous
Speaker 5: sort of the way that we do things today where
Speaker 5: we just accept that a machine is going to get built,
Speaker 5: or we accept that a bunch of AWS services are
Speaker 5: going to be deployed and they do this, this and that.
Speaker 5: But just taking the time to go back and actually
Speaker 5: understand the mechanics of it, it's still very important. I
Speaker 5: think that that kind of differentiates a few people as well.
Speaker 4: I mean, I think you really hit on an important
Speaker 4: thing there, because I think this is what really led
Speaker 4: into the whole DevOps mindset movement is you have to
Speaker 4: understand one hundred like as much as possible in the
Speaker 4: ecosystem that you're building, in what you're writing code and employing,
Speaker 4: you're a single team is cross functional and responsible for everything,
Speaker 4: and that at the same time, there is more and
Speaker 4: more that we have to learn about and know, and
Speaker 4: maybe really the only way in which we can be
Speaker 4: successful is if we are adding ms as our team
Speaker 4: members to actually expand out our knowledge and expertise there,
Speaker 4: because there is just more and more things that I
Speaker 4: just don't think anyone can reasonably become an expert in
Speaker 4: one hundred percent of everything.
Speaker 5: They should be no, and if anyone, if anyone kind
Speaker 5: of says they are an expert, probably don't believe them.
Speaker 4: Right, right, Dunn Kruger there, right, Yeah.
Speaker 2: That's been one of the things I've struggled with for
Speaker 2: people because I feel I have a lot of empathy
Speaker 2: for people who are just starting a career in infrastructure
Speaker 2: or platform engineering or DevOps, whatever you want to call it,
Speaker 2: because there is so much to take in, Like I
Speaker 2: know a lot of it, but only because I've been
Speaker 2: doing this for three decades, Like if I were to
Speaker 2: start today. It's one of the things I try to
Speaker 2: do is for people who are just starting. It's like,
Speaker 2: how do I condense three decades of things into a
Speaker 2: digest stable format for them so that they can build
Speaker 2: on top of what I learned instead of having to
Speaker 2: relearn what I've learned.
Speaker 5: Yeah, and I think I mean someone actually, a guy
Speaker 5: called Suresh who is the CEO for platform nine. Who
Speaker 5: do I'm not sure if you guys know platform nine
Speaker 5: that they do. They started off in KVN based hosting
Speaker 5: with VMware and now now they basically do Kubernetes based
Speaker 5: platforming on AWS. But he told me way back when
Speaker 5: that the developers of today are just in a different
Speaker 5: class to what you think the developers of five years
Speaker 5: ago were and what they were, because what they're learning
Speaker 5: at UNI is something completely different. Like I learned Turbo
Speaker 5: Pascal at UNI, right, or maybe some people older than
Speaker 5: me would have learned cold Cobowl or whatever it might
Speaker 5: have been. Right, And then now then it went to Java,
Speaker 5: and then it went to this, and now they're learning visuals,
Speaker 5: dot net and now they're learning Python and so it's
Speaker 5: just accelerating. But the big thing that stacks to me
Speaker 5: wasn't so much about the code. It was more about
Speaker 5: these new developers that are coming out or the new
Speaker 5: breed of it. Guys, if you ask them what a
Speaker 5: storage platform is, they'll say, oh, Mungo dB or Cassandra
Speaker 5: or you know, one of those database.
Speaker 6: Is not not an actual storage system, not a disc.
Speaker 5: So they're abstracted up a couple of layers, right, And
Speaker 5: they might even say, you know, the next ones might say, oh, well,
Speaker 5: what's my storage roll. It's my Mongo dB database sitting
Speaker 5: on Mango dB, you know what I mean. So there's
Speaker 5: this level of continual growth and abstraction that's happening with
Speaker 5: these guys, so they don't need to know what's underneath.
Speaker 5: So those guys don't care about the storage. I don't
Speaker 5: care about the spinning disc or the envmme drives. They
Speaker 5: are running it all the servers. All they care about
Speaker 5: is the interface into that Mango dB server. And that's
Speaker 5: that's quite interesting.
Speaker 1: Yeah, that's a good point.
Speaker 2: And I think the level of abstraction is just we're
Speaker 2: abstracting the abstractions now absolutely.
Speaker 4: I mean, I actually think it even goes further than that.
Speaker 4: It just as you know where we're currently at. I
Speaker 4: see in university students for their senior thesis basically building
Speaker 4: something that runs on AWS as as the project, which
Speaker 4: is just so much far removed from like even what
Speaker 4: I was doing initially, Like I had to I had
Speaker 4: to teach the first company that I actually multiple companies
Speaker 4: that I had worked at, like what GIF was, and
Speaker 4: that like what version control was, Like I was teaching
Speaker 4: them that and I didn't learn that the university that
Speaker 4: was just like.
Speaker 3: What I was doing. They didn't have that.
Speaker 4: But now that like that's that's just a class that
Speaker 4: they have to go through.
Speaker 5: Yeah, absolutely, I mean that's and that's the difference is
Speaker 5: because that is a skill that's required when they.
Speaker 6: Hit the workforce today because everyone kind of needs to
Speaker 6: know it.
Speaker 5: So yeah, it's things move on, and I'm sure to
Speaker 5: tie back, you know, tie the not in it to
Speaker 5: to the LM talk. Absolutely, you're going to get prompt
Speaker 5: engineering courses within university, if not already happening like pretty soon, right, Yeah.
Speaker 2: I would expect still be interesting to see the curriculum
Speaker 2: on those.
Speaker 5: Yeah, don't swear, they don't don't don't be nasty to
Speaker 5: It was like one on one.
Speaker 2: Listing cats, right, because if you're in college now, you
Speaker 2: likely will live long enough to see this thing have
Speaker 2: a real world interface and it's going to come back
Speaker 2: with those things you said, absolutely awesome.
Speaker 1: Should we do some picks?
Speaker 6: Yeah, I'm excited about this, all right.
Speaker 1: Cool, Warren, can you kicks off?
Speaker 3: Yeah? For sure.
Speaker 4: So I've been holding this one, this particular pick, for
Speaker 4: a few weeks now because I knew the yeah, the
Speaker 4: episode on.
Speaker 3: Prompt engineering was coming.
Speaker 4: So if you're unfamiliar with the newsletter TLDR, there's a
Speaker 4: TLDR security written by Clinton Gibbler and Remy Macarth that
Speaker 4: you were absolutely fantastic, and they put together fifty plus
Speaker 4: research papers. They found them, they distilled them down, like
Speaker 4: really went deep what they mean. And there's a get
Speaker 4: out repository on prompt injection defenses. I highly recommend if
Speaker 4: you're anywhere near the area of the industry to.
Speaker 3: Take a look.
Speaker 4: There is quite good summary of like what the problems
Speaker 4: are and how they're being solved or even start to
Speaker 4: be tackled. And there's some really clever things in there,
Speaker 4: like oh, you know, well, someone can tell our LM
Speaker 4: to do something bad, and so like how do you
Speaker 4: get around that. One of them I find really interesting
Speaker 4: was oh well run it, run it like back through itself,
Speaker 4: like get the result and then like parse it and
Speaker 4: then like pass it back through the same LLM or
Speaker 4: like another LLM and see if that one was validated.
Speaker 4: And so using a graph of lms, get them to
Speaker 4: sort of validate the response from a different one and
Speaker 4: whether or not that's it's because like even if you
Speaker 4: can get over the system prompt of the first one,
Speaker 4: if the output of that has to still match the
Speaker 4: second one that you're able to have better security over,
Speaker 4: you can subvert attacks that way.
Speaker 3: And the list goes on and on. There's I think
Speaker 3: there's over fifty in there.
Speaker 1: Cool, all right, Anthony, would you bring for a pick
Speaker 1: turn there?
Speaker 5: They're both related to AI and all that kind of stuff.
Speaker 5: So the first one is Life three point zero by
Speaker 5: Max teg Mark. So I listened to this maybe year
Speaker 5: and a half maybe actually it was before chat GBT
Speaker 5: came up. But it's effectively about becoming age of AI
Speaker 5: and whatnot. But the first prologue is effectively it's a
Speaker 5: story and it's worth reading for it alone.
Speaker 6: It's it's called the.
Speaker 5: A Mega Project, and it's effectively you know about an
Speaker 5: AI that's been developed by company kept secret perhaps in
Speaker 5: the Internet gets sentiment, gets it starts to build in
Speaker 5: itself and get better and then effectively controls the world.
Speaker 5: But it's not a not a bad story, it's a
Speaker 5: great story. But it's actually coming to life right now
Speaker 5: like the actual it's a prophetic look at what's happening
Speaker 5: with chat, GBT and the LLM today. So that's that's
Speaker 5: a big one that I would recommend. And the other one,
Speaker 5: which is actually really cool, is more about that second
Speaker 5: part that we're talking about, is called an Overseen by
Speaker 5: James Lovelock. James Lovelock worked he only died a few
Speaker 5: years ago. He was about one hundred and something, but
Speaker 5: he's basically the guy that invented Michael Waves that sort
Speaker 5: of stuff like that. Dude he worked in Area fifty
Speaker 5: one and whatnot. But this book is all about the
Speaker 5: coming age of HyperIntelligence and effectively goes AI to cyborgs
Speaker 5: and what happens when cyborgs basically start to get as
Speaker 5: smart as humans and effectively the end of the story
Speaker 5: is do cyborgs need humans or do humans need cyborgs?
Speaker 5: And then it kind of revolves around there. So that's
Speaker 5: a really cool read. I think it's a short read
Speaker 5: as well. You can get through it in about eight hours.
Speaker 5: But that's one of my favorite autime books and overseen
Speaker 5: by James Lovelock.
Speaker 1: Right on line.
Speaker 6: Cool.
Speaker 2: So this week I'm here fishing for picks because I
Speaker 2: was out. We went out this last weekend. I made
Speaker 2: a long weekend out of it. We were out in
Speaker 2: the mountains, new cell phone service, no not even any
Speaker 2: radio stations. And I came back and I can't log
Speaker 2: into my computer, my MacBook. I have no idea what
Speaker 2: the password is. For years, it's been muscle memory, and
Speaker 2: every once in a while I'll change it by just
Speaker 2: changing the keystrokes a bit.
Speaker 6: But it's not.
Speaker 1: Anything.
Speaker 2: It's not a word, it's not a phrase, it's not
Speaker 2: anything written down. And it's also gone from my brain.
Speaker 1: Like whatever brain.
Speaker 2: Cells were responsible for holding that, they're freaking dead. So
Speaker 2: I'm here fishing for picks. If anyone has any ideas, recommendations,
Speaker 2: or strategies for storing those root credentials, I could use
Speaker 2: a new one. So if you got any ideas, hit
Speaker 2: me up on X or leave comments in the show.
Speaker 1: How everyone do that?
Speaker 3: I got a good one for you, kind of on
Speaker 3: what you're using.
Speaker 4: I've got a ubkey that I plug in that automatically
Speaker 4: decrypts my hard drive and it's got its own pin,
Speaker 4: but it's very short, and so I just plug that
Speaker 4: in and leave it there until my keychain and pull
Speaker 4: it out, so it doesn't matter, Like I don't ever
Speaker 4: need to remember what password is there must I lost.
Speaker 4: I lost the key chain as well, and if I forget, I'll,
Speaker 4: you know, update it right now and validate. Like every year,
Speaker 4: I have a reminder said do you still remember the password?
Speaker 3: And tape it out? And if I don't, I'll set
Speaker 3: a new one.
Speaker 6: Isn't that funny about passwords like that? Like you said it?
Speaker 5: Like I think if you ask anyone, most people, unless
Speaker 5: they're really.
Speaker 6: Security conscious and there's some real.
Speaker 5: Security nuts out there, they will they will basically have
Speaker 5: that same password that's muscle memory, but slightly older. Maybe
Speaker 5: it's two numbers that they change every now and again. Maybe,
Speaker 5: So I think majority of people do that because that's
Speaker 5: just what we know.
Speaker 6: It's easy. Don't feel bad about that.
Speaker 5: But yeah, maybe maybe you know the fact that your
Speaker 5: muscle memory is gone.
Speaker 6: I don't know, get that check.
Speaker 2: Maybe great, because that's the that's the big concerns like, Okay,
Speaker 2: I forgot my password? What else have I forgotten that
Speaker 2: I'm going to need something?
Speaker 3: This girl?
Speaker 4: Well, I mean the good news is well that unless
Speaker 4: you actually damage those on the the pathways are still there.
Speaker 3: You need to re trigger them.
Speaker 4: But the connections in the neural net that's processing over
Speaker 4: those neurons does degree, so you you can retrain it
Speaker 4: by some going through whatever process you would normally go
Speaker 4: through and try to get your key, your hands on
Speaker 4: the right spot on the and may come back to you.
Speaker 6: Just go to Alon. He's got like a little plugging
Speaker 6: the head. Now.
Speaker 2: I'm sure you guys have seen like the similar tweets
Speaker 2: and stuff similar to where they'll tweets on at C
Speaker 2: I a say, hey, I forgot this piece of information.
Speaker 2: Can you guys send me your backup copy of it?
Speaker 2: Tweet at CIA? Can you send me my passwords?
Speaker 6: Someone who will do it? Cool?
Speaker 1: All right, well, Anthony, thank you so much for being
Speaker 1: on the show.
Speaker 6: Just really fun. Actually, I really enjoyed the conversation. Love it,
Speaker 6: love it. It's been good.
Speaker 5: We've gone on different places and Warren, I reckon you
Speaker 5: talked about ads. I reckon, you're gonna get ads about
Speaker 5: black pudding now, so I just.
Speaker 2: Get ready to right for sure there is so I
Speaker 2: can say that Amazon seems to have improved their ad
Speaker 2: their ad platform a lot, because I used to have
Speaker 2: this running joke with a really good friend of mine
Speaker 2: where we would find less than professional things for sale
Speaker 2: on ad on Amazon and send each other links, you know,
Speaker 2: masking them or whatever, just to get the other person
Speaker 2: to click on them. And then every time they logged
Speaker 2: into Amazon in front of their wife or something, there
Speaker 2: would be these really odd ads and it was just
Speaker 2: great fun. But then I had another friend who I
Speaker 2: think it's been about two years ago now, he was
Speaker 2: having open heart surgery and we were really concerned about him,
Speaker 2: and so I wanted to make sure that he was
Speaker 2: in a good mood before he went to the hospital
Speaker 2: that morning. So I went on Amazon and I ordered
Speaker 2: a blow up sex doll, and then I took it
Speaker 2: around to all my friends, and you know, we all
Speaker 2: wrote stupid stuff on it, and then I filled it
Speaker 2: with helium and tied it to his mailbox so that
Speaker 2: whenever he his wife took him to the hospital that morning,
Speaker 2: there was a blow up sex doll flying above his
Speaker 2: house and and so I was really worried about the
Speaker 2: next time I logged into Amazon after that, like thinking, Okay,
Speaker 2: what are they going to show me?
Speaker 1: I didn't get anything, like not even like dolls.
Speaker 2: Yeah, here's other sex dolls it might be interested in,
Speaker 2: or here's things to go with this. None of that stuff.
Speaker 2: I didn't get anything. So they appear have done done
Speaker 2: some work there.
Speaker 5: Yeah, well maybe is this one top of sex doll
Speaker 5: on Amazon? And you boid it and that was it.
Speaker 1: It was the only one they sell.
Speaker 6: They sell, that's it you got. You got the premium model.
Speaker 2: Right, that's our top of the line model.
Speaker 6: Yeah anyway, yeah, awesome, all.
Speaker 2: Right, cool, Thanks everyone, Anthony, thank you, Warren, thank you,
Speaker 2: and to all nurse thank you for listening and supporting
Speaker 2: the show.
Speaker 6: And we'll see y'all next week.