Andrew: 0:00

You know, one thing I want people to realize is that you don't have to stick with managed services. They're great, but I just want people to not fear being able to work with models directly, Because if you have and you're going to be hearing a lot more about them you go to your staples. You're going to see this everywhere AI PC, right, and you know you can have like a decent AI PC that's just on your network, that's not necessarily used as like a window station, but more just, people use it as inference, but you could work it into your business right, Like just not even online, but just in your office, because you can be making prompt documents and leveraging other things to improve just your, your workflow. You don't have to go use Microsoft Copilot or Google Workspaces, Gemini, and you're going to get a lot more flexibility and control, and that's something that I think that I'd like people to get out of it.

Tim: 0:57

Welcome to the Cables to Clouds podcast, your one-stop shop for all things hybrid and multi-cloud networking. Now here are your hosts Tim Chris and Alex. Hello and welcome back to another episode of the Cables to Clouds podcast. My name is Tim. I'm your host this week at Juan Golbez on Twitter. As always, with me is my co-host, Chris Miles, at BGP Main on Twitter. We have a very special guest with us tonight who is my cat?

Andrew: 1:31

I'm out, I'm gone.

Tim: 1:34

Oh, we also. We also have Andrew Brown with us rejoining the the podcast. He is here to talk about something really cool that we've all we've both been looking forward to, which is his upcoming Gen AI bootcamp. It's been all over Twitter, but if you haven't had a chance to see it, well, you're going to get a chance to see it today. So I guess, andrew short introduction in case people missed your last episode and then let's roll right into it, man.

Andrew: 1:59

Hey everybody, I'm Andrew Brown and I'm known as the Cloud Clown, bringing you, I don't know, all sorts of jokes and drama, but for those who don't know me, I'm a cloud educator. I create a variety of different cloud certification courses, I run boot camps, things like that, and all the stuff I usually do is for free. How do I make money? I don't know, but I keep opening the mail and there is like wads of cash and I don't ask any questions. But, uh, you know, it's just, it's just how it works. But, um, I recently just bought a, a church, an old church, and I'm turning into a technology center. Um, and if anybody has postcards, I'd love to receive some. Um, I have an address somewhere. I was supposed to have like a title card so I could show it up here, but uh, yeah, somewhere on my twitter we'll get in the show notes.

Tim: 2:45

We'll get in the show notes for it. Yeah, for sure. What's funny is I went upstairs so, uh, I went upstairs and looked through our stuff that we got at ghibli park ghibli park last year and we do actually have a good amount of uh postcards and I grabbed one and my wife was like what are you doing with that? I was like well, well, I'm going to, I'm going to send it somewhere as a postcard. And she was like no, no, you're not.

Andrew: 3:08

So I'm just getting. I'm just getting like a whatever kind of postcard. You you know, chris, chris came from Japan and he's like send me a postcard. I'm like is it from Japan? You're going to get a postcard.

Chris: 3:19

I think the idea, tim, is you're there, so you've already missed the boat on that I definitely missed the boat on that one.

Tim: 3:24

I'm hoping to go back next year and, uh, I will. I will buy a new postcard so that my wife doesn't yell at me, and then, uh, it's, you know what she'll be like.

Andrew: 3:32

Too precious to throw out, too too precious to throw.

Tim: 3:35

You don't send postcards to people, yeah but uh, yeah, we'll get that in the show notes and then, yeah, absolutely inundate this man with postcards. I want like a Miracle on 34th Street moment where they're walking in with bags and just dumping them on the ground Just postcards.

Andrew: 3:52

All hate mail, all right.

Tim: 3:57

No, no, no, no, no, no. This is good. The church thing is wild. I still. I know this isn't necessarily the topic, but I just think this is wild and you're turning the church like you're doing something with that church. You can just buy it for no reason, right?

Andrew: 4:10

What are you doing with it? Oh yeah, so we're turning into a technology center, so we're going to do STEM training for, for local schools. We're going to retrain people in, we say, hard industries, people that work in like the mill or the mine or the railroad, into tech, and somehow we're going to do most of it for free. But yeah, I just, you know, I don't know why I'm doing it, but I'm doing it.

Tim: 4:32

I mean, that's a pretty cool endeavor. Honestly, that's, I think a lot of us wish we could, you know, give back to that level. So no, that's really cool man. So let's talk a little bit about the Gen AI bootcamp, kind of what. If you don't mind me asking just kind of where did you? Is it just because, like hey, gen AI is a thing now, or did you actually have like a kind of a plan that you put together for this?

Andrew: 4:55

I mean, I always have a reason why I do things Like the reason we did the AWS Cloud Project Bootcamp was that I had so many students that I had seen at like reinvent and other places that had gotten certified didn't get jobs or roles and they were. They wanted to build a project, they wanted to know what, like what to do, and I would give them ideas, but they they couldn't get started and I didn't realize there was such a gap there, and so what they really needed was somebody to take them through to a degree and then give them flexibility to to discover on their own what they could do. But the reason I'm doing the Gen AI Bootcamp is totally for selfish reasons, totally different, which is just like I've been taking Japanese lessons and I keep building out little Gen AI learning assistants and I want to just squeeze more time in there and I'm like, how can I do that on company time? And so if I do a bootcamp and Gen AI is hot, hot, hot, then I can go ahead and do that on company time. And so if I do a boot camp and it's still and like jenny, is it's hot, hot, hot that I can go, I can go ahead and do that.

Andrew: 5:48

But you know what language learning and uh works really well because, like natural language processing and lp uh I gotta use some initialisms here to sound super fancy but it like it's just a powerhouse for lms, ims. I don't know if it's novel, but it's a good use case and I think that with everybody really interested. I'm not sure if people are interested, but they feel obligated to make sure they don't miss out on this and learning about Gen AI and their tool belt, because we don't know if it's going to take people's jobs or not. It's not, but people are worried about it.

Tim: 6:29

So I think that it's just the most accessible thing to teach people right now. Yeah, that's really a good point. Uh, and and and the whole boot camp thing is is, um, and we've said this before right, like the best way to learn something obviously is to do something, and you really like the use case thing is so, so important, right, like just applying what you're learning and doing something with it, it's probably like the best way to learn something. So language learning is definitely definitely the way. I think you know we're both learning, we're both learning Japanese. So I'm, yeah, we're, we're, you're building some really cool apps with it and I'm asking chat GP dumb questions.

Andrew: 7:01

So we're doing our part.

Andrew: 7:03

But you know, but even with all like the stuff, we can use it. I still need a real japanese teacher to progress, right? So I? I think that a lot of people think that it's like 100 replacement, like it's coming for jobs and I I literally can't progress in japanese. I'm sure there's some people that self-study, but like having a, a real human that knows the subject matter and can guide you through it, is like super useful. But you know, it just alleviates a lot of the the busy work. It's like imagine having our teacher every day, you know, having to do drills with me. That's not, that's not a good use of their time, right. So right, you know, I think. I think it kind of meets in the middle. It's not perfect but it's definitely working for my needs anyway.

Chris: 7:46

Yeah, so quick question about that. So, since you're doing a Gen AI bootcamp, gen AI seems like this kind of realm where there's a lot of people doing the hard work that are considered experts, but even the experts are still learning on a day-by-day basis about kind of what this thing is and then how to really grapple with it. So my question for you is how did you go into preparing you know yourself for this Gen AI boot camp and how did the content kind of come out of that?

Andrew: 8:18

Sure We'll talk about. I'll break those into two parts, the latter being how did we prepare and learn for all this stuff? But the first one about Gen AI experts not being able to grasp stuff, because when you think of Gen AI, gen AI is actually just a subsection of AI and machine learning. So the experts that are in academia and know how to build machine learning models and stuff like that, to them they're just like yeah, this is a thing and it's very novel. They know how to apply it, but a lot of people in the Gen AI space don't have I shouldn't say they all, but a lot of them don't have that AI ML foundational background Like the capital models.

Andrew: 8:55

Yeah, they're more like practitioners of like oh, I have a model, I play with it and for them it's very hard for them to find a fit because they might not understand how it fits into the landscape, right. Find a fit because they might not understand, like it, how it fits into the landscape, right. Or they're utilizing gen ai when it's very expensive and ineffective at specific ml tasks where traditional machine learning is more effective, or, uh, you know, building a simple neural net or using something like xg boost, um. So I think there's that kind of muddying going on. Today. I think it was um eight of us andying going on. Today I think it was AWS and I use all the clouds. I just happen to be wearing this because it's warm, but today Matt Wood, who was the VP of AI over at AWS, has departed. He's been there for 15 years, which is really interesting. So in the internals we were like, oh, does that mean that AWS is deprecating AI? Now, all there is is Gen AI, which I don't think that's the case. I think it's just coincidental. But Gen AI is definitely, again, it's just a subset of very specific machine learning models, right, or like put under that umbrella.

Andrew: 9:59

But let's talk about how I prepped for getting up to speed. So I did already have some machine learning skills behind me Before I even had this company. I had a couple of startups and I was doing machine learning pipelines. I didn't realize I was really doing machine learning it was more like classical machine learning and things like that but I already had some working skills at building more efficient machine learning pipelines on AWS and other tools like that. So I already had a bit of that foundation. But for this it just felt like Web3, but there's like all these fricking new terms and you dig, dig, dig and you find out that the term was just like overblown as the description of what it was right.

Andrew: 10:41

Like a marketing term or something I don't know, but it's like obscurifying the utility of what it was right, like a marketing term or something I don't know. It was like obscurifying the utility of it. Like RAG is Retrieval, augmented Generation why do we have to call it that? All it is is a no, I know what it is. It's just like a way of going and retrieving data and bringing it back and putting it into the prompt before the, the, the agent or LM response applies, and so at anything that it goes to, if it goes to the internet, if it goes to a database or whatever, that's all it is Right. But like, when you start reading about it, people don't describe it very clearly. And then it's like it seems like it has to be with a vector store, and then it's like it seems like it has to be with a vector store.

Tim: 11:27

What's a vector store? Do you know what I mean? And yeah, no, that's the magic of it. Right, the, the and. And the only reason I know this part of it is because I've been extremely deep on it. Uh, because this is what I'm doing, my talk at reinvent on is rag, um, but yeah, I mean. So the vector store part of it is is literally just like a very fancy mathematical matching algorithm, right, like the whole vector store thing, where you take documents, plug them into vectors, turn them into math and then, when you build your prompt, you turn the prompt into a vector and then you compare them and try to find the closest match for the docs you have, I guess, right?

Andrew: 12:00

I think.

Andrew: 12:00

So I have my simple speech, and it's like what a vector store is is it takes your chunks of your words, or the words of themselves, turns them into numbers and puts them into like, think of, like a plot graph, like dots on a graph, and if the words are similar, they're going to be near each other, right.

Andrew: 12:26

But the part where it gets kind of more complicated is that how do you know like these two words are related, or these four words are related, and that's where embeddings come into play, because the embedding is the algorithm, the way to describe that relationship. And so when you store them in a vector store, what's the relationship? Is it because the length of the word? Is it because they look similar, like similar letters and spelling? Is it because they're in the same topic? And then, on top of that, an embedding could be optimizing on multiple things, so it could be like a combination of those things and you have a bunch of numbers. So that's my, my simple explanation, that I understand it, and somebody that knows vector storage would be like I don't, like you're. You're missing out all this information, right?

Tim: 13:19

it's all about the math for them, right for the people, that that that's what they do. But, yeah, you're right about this. Uh, you know, gen ai just being a almost like a, okay, a natural language way to engage with aiml, which is not new, right like so, uh, and the embedding model thing is is actually really really well. Well said because people don't realize there's like lots of different kind of embedding models out there too that'll based on sentences, based on topic, based on there's there's uh, video ones and image ones and stuff, and it's all chunking that same.

Andrew: 13:50

It's crazy actually, how it all works moon patterns, you know, like, whatever, whatever it is uh and um. Another thing that and this is more like uh, folks in academia that they always want me to say about, um, gen ai, which is it's not all about the llmsMs. There's multiple modalities, so, right, you have vision, you have audio, you have text, you have molecular. You know, there's other other ones there that I like. I'm sure there's other, like video, which is technically just images, moving images, and then when you look at those other ones besides LLMs, those are just like models that already existed before Gen AI, right, like we already had vision models.

Tim: 14:28

Um, it's just that, uh, when you put them under Jenny I, they get a bit more attention, um, because you know, the marketing helps it, right, Is is Jenny, and this is the part I've never been able to quite figure out, because I agree that, like, ultimately it's really just an expression of AI, ml, it's like a natural language expression. Is that the marketing? Like the, the, the, the fact that you can use common language to engage with all this data? Like before it was just machine learning, it was just like pattern recognition and everything.

Andrew: 14:55

Gen AI just means that like it's generative, so like if you input something it's going to output to generate something, because like when you think of machine learning like general AI machine learning it's.

Andrew: 15:08

It's making a prediction, right? So you say what's the weather going to? Let's forecast the weather. What's what? What would it be 10 days from now, based on the data that you have, and it'll spit out a number. Or it's like tell me, you know, based on this, like classification, what's like? If I give you this word of an animal, can you classify it and tell me, is it a, is it a mammal, is it a reptile? And so that's the classical stuff.

Andrew: 15:35

Now the interesting thing is that llms can do these simpler types of classical machine learning tasks. Um, but it's very expensive compared to like. Even if it's not that expensive, it's just like it's pennies. It's dirt cheap to learn classical machine learning and utilize it for those things. But with llms you don't have to understand how a machine like a machine learning pipeline works. You don't have to know how to build an ML model or work with XGBoost, and so it makes these other simpler ones more accessible at a higher cost. But once you understand the costs and stuff, you learn a little bit more and you can utilize those other simpler models, which is what people need to do when they're learning Gen AIs. To go into those simpler models.

Tim: 16:16

Sorry, we were messing around with this a little bit. Right, you and I were messing around with this idea of downloading a model to do your own training and stuff. Let's talk a little bit more about the bootcamp. I don't know how much you're willing to share about the structure and whatnot at this point, but I'd love, I'd love to understand kind of what your, what your thinking is or planning is about, like who's coming into it, who's the ideal student and like where you want them to leave. Maybe they just start there. We can work our way through that.

Andrew: 16:47

You know, I would like to teach all levels, and so I'm in the middle of developing the curriculum and I might do multiple tracks. So, like the thing is is, if you're learning about prompt engineering in the bootcamp six weeks, it'd be nice to if people are at. Let me step back one more. So I've built out a Gen AI roadmap, and it uses what I call a maturity model. The idea is that you talk about where somebody is in the maturity of the knowledge that they have in Gen AI, and so the first place that people usually end up are AI-powered assistants, gotgpt, anthropic Cloud, lama, mitstroll, you know, et cetera, et cetera, and so just in that little that area, there's a lot to do. Just with prompt engineering, right, you could do a lot and never ever learn how to programmatically work with LLMs, and so that's like level 100. Even my mom would want to use that, right, and so I almost feel like I could do six weeks just on that.

Andrew: 17:45

Then there is the next step. There's managed services. You know how to program, but you don't know how to work with, let's say, hugging Face or Python or PyTorch. If you get conflicts, you just give up, right, you don't want to download a model. You just want to be able to work with models. So one step there, and so that would be something like Amazon, bedrock, gemini or working with Cohere's API. So I feel like that's a level in its own and that could be over six.

Andrew: 18:17

And then there's the more top level, which is like next level, which is like okay, I'm comfortable with code, right, I can work with Python, I can work with Hugging Face, I can download a model, I have CPUs and GPUs in my house that I want to utilize and I want to have more flexibility in my models. I don't just want to be using these stock built ones and I'm worried about security and I'm worried about these other things that come with. Like if you use a proprietary service. Like if I use ChatGPT and I get, I'm used to it in my workflow and I forget how to program priority service. If I use ChatGPT and I'm used to it in my workflow and I forget how to program, now I'm stuck If they jack up the prices. I'd rather have something I have more flexibility with. So that's another level.

Andrew: 18:59

And then there's a level beyond that, which is like I got to deploy this for enterprise. I got to know how do I right-size my workload and what would it look like at scale? Do I need AI inference for that, like an AI accelerator, and what's the concurrency and what's the deployment model look like, and et cetera, et cetera. And what's my technical path from startup to full size? And so it'd be nice to teach all those four levels and you could do that over six weeks, but the thing is is that it'll be challenging, because I also just want to build apps and have this little ecosystem, so I'm going to have to figure out a way to do it, and it's probably gonna be messy, but you know, if it's all there, then I think that's that's what matters, and we have a lot of people on on the same thing. It's going to make it like the. The road will be very well traveled for everyone else to follow.

Chris: 19:51

Awesome. Yeah, that was. Sounds like it'll be a very, very fun boot camp, so I might have to try to join myself. That does kind of lead me to another question I had. So based on, based on what you've learned so far and what you've, you know, gone to putting in to this boot camp, what do you think? Oh, I'm curious to get your input on what you think will actually be the outcome of this from a from a business perspective. Like, how are people actually going to use these LLMs? Like, do you feel like there will be a lot of businesses just using the off the shelf ones, or do you think they'll actually put in the effort to build their own? And is there a specific or a specific reason why you think they would do one or the other?

Andrew: 20:33

You know, one thing I want people to realize is that you don't have to stick with managed services.

Andrew: 20:38

They're great, but I just want people to not fear being able to work with models directly, because if you have and you're going to be hearing a lot more about them you go to your staples.

Andrew: 20:48

You're going to see this everywhere aipc, right and uh, you know, you can have like a decent aipc that's just on your network, um, that's not necessarily used as a like a window station, but more just, people uh, use it as inference, um, but you could work it into your business right, like just not even online, but just in your office, because you could be making prompt documents, uh, and leveraging other things to improve just your workflow. You don't have to go use Microsoft Copilot or Google Workspaces, gemini, and you're going to get a lot more flexibility and control, and that's something that I think that I'd like people to get out of it. So that's probably one of the bigger things. It's just not feeling that you have to use the cloud for it even though I'm the cloud person, that there's other options there, and I think edge is going to be a big deal. So I just want people to get more comfortable with the edge.

Tim: 21:42

When you say edge, you mean like edge computing, like this AI, pc idea of being offload.

Andrew: 21:46

Yeah, it's weird because I'm saying edge but it just means on-premise. I'm basically saying on-premise, but when you look from the cloud perspective it's like yeah, Because I guess if you say edge, then it can be a managed service that is in-house as well, and I haven't seen a lot of solutions like that. But sometimes I just use the word edges, on-premise. I don't know why.

Tim: 22:14

I guess, you're not supposed to, but I do. I think that's an accurate way to use the term. That's how I typically use it as well. So do you think actually? So there's technical challenges with you know, and we've run into them ourselves when we were messing around with it with, like you know, essentially you know, downloading your own model, for example. Even that part of it can be difficult just to do technically, and also, of course, making sure that you have, you know, a graphics card or something that's able to do it as part of the boot camp. I mean, is that something that you're going to want to try to solve, you know, for the students, or is that something that is that is bridged too far, that it's just not gonna be part of it?

Andrew: 22:45

No, it's gonna be part of it, but I have to make sure that we make make things. You know, people that can't have any spend, like how can they do it for free? That's, that's one layer. There's the other layer which is, like you know, I have, I, I have some spend, or I have, you know, cloud. How can I do cloud? But I just don't have the hardware Right. And then the other one is like I had the hardware, but it's not the best Like it's like you know, it can do it, but maybe just on CPUs. And then there's the person like I have my gaming computer or I was trying to do mine like crypto mining, but now I guess I can utilize this for work for for Gen AI, we can do that. So we kind of like those four levels and you know I want to try to satisfy all four as best I can. But that's going to be a hard thing because it just going to be things that people can't participate in because there's that hard restriction. But you know, but at least we get the exposure to it. But yeah, I mean, it's just, it is what it is Right Like I can only do my best to try to support all levels.

Andrew: 23:43

But that's another challenge, like I need to make badges. And so when we did the AWS Cloud Project Bootcamp, the Terraform one was just a single badge because it was smaller in scope, but the AWS Cloud Project Bootcamp had four badge levels and the idea was that the farther and harder you pushed and the effort you showed, the more likely you could get that Red Squad badge which was subject to me to issue. The other ones are extremely well-defined, but the other ones were the last one. There was like push, push, push, push. And so here I have to kind of figure out how the grading would work, the badge would work, when you know, not everyone can work across all those four things. So we have like four different levels of learning, four different levels of maybe you could say deployment or platform.

Andrew: 24:26

These terms aren't defined right. Like I'm making my Gen AI roadmap and I'm trying to like okay, how do I, because there's no generic making my Gen AI roadmap and I'm trying to like okay, how do I? Because there's no, there's no generic cert on Gen AI, there's not. Like AWS, has I made AWS's one, I made the Azure one, I'm doing the Intel, the NVIDIA one. Right now Google doesn't have one for some crazy reason, and so you know they don't define the stuff very well, right?

Andrew: 24:49

And even you think AWS would like leverage what they the terms and all cloud practitioner to bridge it over. They don't, and so you know I think that we really do need like a generic cert and um as a byproduct. Before I even start this boot camp, I'm going to finish that production and that's the prereq for the course, so that people you know will have a grasp of what they're looking at before we start building the project. Because, like you can't, you can't learn any of these certs that exist now and it's like it'll you'll be so deficit in so many areas, more so than previous cloud certifications yeah, I actually just started yesterday your um cloud certifications.

Chris: 25:29

Yeah, I actually just started yesterday your um aws certified ai ai practitioner, uh boot camp on youtube yesterday and um, so far so good. I'm still very early in there, but, um, good job so far. Um, but I I do notice that at the beginning you kind of start with this sliding scale of how long it's probably going to take someone to study. You know, if you're very skilled in in the technology realm, it's probably going to take someone to study. You know, if you're very skilled in the technology realm, it's probably going to take you a little bit less time to grasp some of the concepts and things like that. Do you feel like this is kind of in the same category, like if you're strong with technology, you're probably going to do better in certain capacity, or are you really trying to? I know you're trying to teach all you know, kind of grade levels kind of thing but do you think that disparity is still going to exist?

Andrew: 26:13

Yeah, because the thing is is that unless you have a machine learning background and a data background, that stuff is still key, right? And then you still have DevOps thrown in there, like, if you already have DevOps and you've done MLOps, you're going to have an easy time over there. And Like, if you already have DevOps and you've done MLOps, you know you're going to have an easy time over there and you have the cloud foundation is going to help a bit. But really it's just like you have a very specialized version of machine learning and working with data for these particular type of models. So unfortunately, that disparity is there. And then it's like then you have this hardware layer and so, like some people don't come from a hardware world, especially in cloud, you know, people have never, you know they've, they've never worked with cuda. They've never. You know they don't even know what cpus they're utilizing, unless they've worked for a larger enterprise. And even then they just know it as as the skew right.

Andrew: 26:58

they're like oh, the skew has this and you know, and so, um, you know there's going to be, for, depending on where you are, there's going to there's going to be challenges, right so? Or even your developer, it's like. It's like even I'm using, uh, like python and pandas and all these tools and they're just in pytorch and they're throwing all these errors constantly. If you don't get the versions exactly right, it's even more more so difficult than than regular program. You can get through it. It's, I guess, like learning japanese, like three different language, like character systems. You know what I mean. And and the, the verb comes at the end, you know. So it's a good pairing, yeah that's a good point but you can't.

Andrew: 27:35

You can do it right yep, that's.

Tim: 27:38

It depends on how bad you want it right. And actually that brings me to what? Do you think that and this has nothing to really do with the boot camp? Uh, it, I guess it kind of does, but it doesn't really, because it's not like, yeah, the presence or absence of the boot camp has anything really to do with this. Do you think that we're gonna see, or maybe we're already seeing, um, like a gold rush for you know, ai certification, ai learning, ai careers, like we saw for cyber security? I hope so, because I'm making certs.

Andrew: 28:07

But you know what, like, I put out the Azure AI one and like it did okay, but it wasn't like people were running at it. And I put out the AWS AI one and people are picking it up, more so than the Azure one. And you know what, like, I'm one of the few persons that have NVIDIA and Intel practice exams and making courses for those, and we're not seeing them fly off the shelf. But certifications are really driven by not just the demand, but also how it's packaged and the authority that's pushing it out, and the way people are using AI is not the people like AWS and Azure want you to use it Right. They're like how do I download, how do I all these other things that I'm talking about? So, you know, I'm thinking maybe if I pull it off and I make the right kind of content, it could work, you know, and so then maybe I'd see the demand. But at this point, right now, the certs that I've been covering.

Tim: 29:12

It's kind of like I thought it would be more. You know like, yeah, that's kind of what I mean, though, right, like because with cyber security I mean it was and still is pretty fast tech growing field you know certs or no certs, just the field itself, if you will has been extremely popular. It's been growing very fast for a long time. I don't know, it might have reached its peak already. It's hard to tell with these things right? Do you think that Gen AI will, for one, stick around long enough but, for second, just be diverse enough as a tech to invite that same kind of gold rush to like get people involved? You know running for it as a career?

Andrew: 29:40

When we talk to technologists and you know people in tech you know why do they want to learn Gen AI? Do you think there's because, like, when, when, the when, everything was data driven right. Everyone's like oh, you can make good money being a data scientist, I gotta become a data scientist, that's a lot of good money. Oh, you can make good money being a comp sci person, or not comp sci. Uh, a security, a cyber security person, I gotta go make good money. That no one's saying that by jni they're going oh, I hope I don't lose my job to jni. I better learn and figure out what's going on with this thing, because I have no idea I'm going to lose my job or not. Right, like, that's, that's how they're thinking and that's like the number one tag that I'm going to have in the gen I boot camp. It's like, uh, you know, I'm going to teach you everything you need to know so you don't lose your job. Like, because that's whatever. That's people's biggest fear.

Andrew: 30:19

Now. It's not to say that the, the, the tools are not useful, like they're definitely are. They're great in your tool belt, but the mindset is different. People aren't running into it like Web3. Web3 is like oh, I can make a bunch of money because it's all about money, right, but the conversation is different is such a broad topic that can fit like cybersecurity.

Chris: 30:49

I feel like it at least has a certain bucket that it fits into right. You know what it's addressing. You know it can kind of expand into other facets a little bit here and there, but it's relatively well-defined, whereas Gen AI it's like there's a bit of Gen AI that can put in every single vertical. It can address so many different use cases. It's not just technology specific right, so it's kind of you know. I think it's harder to define. You know what the boundaries are and how malleable this thing needs to be. I guess I'd be curious from your perspective, like when someone wants to learn it do you think it's going to be best to just like figure out a use case, try to solve a problem and use Gen A how to do it, or do you really want to go back and learn the bits and pieces that go into it beforehand? What do you think is the better method?

Andrew: 31:33

Well, I mean, the method I've been using is I've been doing both right, so, like I've been building things, that was my motivator to do it. And as I was building things, I would collect each thing oh what's this? And I would collect each thing, go oh what's this? And I would, I would unfurl it Right and I would go back and make that Gen I roadmap which will turn the Gen I essentials course.

Andrew: 31:50

So the point is is that if, if I make the Gen I essentials course and people do that upfront, cause that's what that's kind of more like what people are used to doing, and I'm saying it's the best way to do to how, how people want to learn, but that's not the way that I learned it's it's it's building things and then pulling each part out and then expanding on them, right? So you know, I think that that's what's going to happen in terms of boundaries, like now that I'm in it, like when I started I was, I was like we're like where are the boundaries? I can't feel them Right. And I think it's because every other day you see something on Twitter like Gen AI look at what it can do now and it's so fuzzy about what it's describing.

Andrew: 32:38

But now that I'm in it, I can draw the lines of where it starts and where it ends and I just see again, it's the Gen AI stuff that we're seeing online is. You know, it's that pump up the VC investor money and get everyone excited, and I'm not sure if it's intentionally being obscure, but it's just like you know, people don't know what they don't know, right, and you know, we just keep seeing more of it.

Chris: 32:58

You're like, you're like Neo, like you can see the matrix now, so to say.

Andrew: 33:03

Yeah, unfortunately I can see gen AI. I didn't know that was gonna be the thing that I was gonna be really like, I don't know. Like to me, it's just like this is a gen like I I teach everything, right. So to me, again, it's just a tool. It's like if I was teaching devops or serverless or purity or whatever. But you know, people feel that it's very like it's over, like it's overreaching, like cloud, right, but I. But now that I mean I just again it just feels like another tool in my tool belt and I think the more we get people through it, that's how they're going to feel about it too.

Tim: 33:31

Yeah, that's a really good point. Maybe, actually, maybe one of the biggest services that learning about Gen AI can do for anybody is helping them find those lines so they can understand that, like, not only can Gen AI not take my job, but actually that you know it has a very rigid structure and it has a very specific application, if you will like.

Andrew: 33:53

Just kind of you know, once you see something and wrap your hands around it, you kind of understand its limitations better, like you, like you were saying so and I think there's just like a lot of like, like, like mind-blowing experiences when you first use it because you go, oh my goodness, it can do this, and now everything's different. But then when you start using but you go, oh, here's all the cracks. Do you know what I mean? Like I was using vercel v0 to it actually worked really well, like to build up the um, that marketing site, for apparently, before I do the boot camp, I'm doing multi-city all-day training and, jenny, I don't know why I'm doing this and anyway.

Andrew: 34:25

So I built the marketing website in v0 and I built it in an hour and I was like, oh, this would normally take me, uh, two days to do. And I was like, I guess I don't need to write HTML CSS by hand because, like, I was using things I don't normally, like I don't like Tailwind, I don't like React, but I know how to use them, but it and I use something new called ShadCN and it worked. And it was like, oh, I just need to generally know, based on, like, having deep skills being able to work with and having confident that I liked what it was generating. And so for that first one, I was like wow, that was great.

Andrew: 34:56

When I started building the second one, I started writing by hand because I was like I'm not doing exactly what I want and and and basically I filled the gaps on on the stuff I didn't like about react like that. I forgot about React and ShadCN and Tailwind CSS. So you always get that aha moment, but then eventually you're back to tweaking things by hand and then you forget like, oh right, I was using Gen AI and I'm like, why am I not using it? I'm writing everything behind again.

Tim: 35:19

But you get to a point of efficiency where you're just like, oh, I guess I's kind of how I feel like it, but it's not the tool I'm using, like end to end no, I mean, that makes a lot of sense actually, and I think that ultimately, when the whole thing, when the smoke clears and all the vc money is, is spent one way or the other, for good or for bad, I'm trying to get as much as I can right now.

Andrew: 35:43

Soak it up, man.

Tim: 35:44

Soak it up uh, we'll, uh, I think that's what we'll end up with something more reasonable as something, probably like that, like you're what you're talking about right tooling, better tooling, more tooling to help people do their job, not replace it. Um, we need to start wrapping up, but I would love for you to first of all, uh, give us a little bit more details about the in-person thing you're doing and then plug any of your AI courses as well, so we can make sure we get them in the show notes and just tell us about it, and then we'll wrap up here.

Andrew: 36:12

Okay, yeah, so apparently I'm going to be, I'm at least going to Toronto to do a full day training event and I'm looking to expand it to multiple cities. So next cities is Montreal, waterloo, ottawa, and again, it depends on the support from AWS and another very nice sponsor. But you know, I might be going to the states, I might be going all over the place San Francisco, new York, jacksonville, can't say the name of it, but it starts with an R, oh.

Tim: 36:42

Raleigh, you're talking about Raleigh. I'm in Raleigh, so let me know if you show up, raleigh, I'm in.

Andrew: 36:46

Raleigh. So You're talking about Raleigh. I'm in Raleigh, so let me know if you show up Raleigh.

Tim: 36:49

I'm in Raleigh, so if you make it out here, let me know.

Andrew: 36:55

I just got to put it on the list. And so you know like I'm trying to go do these events here and bring the training just because I wanted to do I've always wanted to do in-person training, multi-city, but I had to do the right topic to get the sponsors, uh, or or the support to do it. But I don't care, I still want. I still want to just go and teach and help the community. So you know, I might be coming to a city near you at least it and so I want to get like um, you know, it's like you get like a map and then it's like you got the big bobblehead and they're like going around to the next town. I want to get that, that, uh, that graphic going there, but right now just a couple cities, and if it does really well, then it's definitely going to expand and that might go on for the next year. So let's see if that roadshow happens. Awesome.

Tim: 37:38

Awesome. And then, of course, we'll get all this in the show notes, but your site is examproco right, that's right.

Andrew: 37:46

Well, you know what ca works too, but we never tell anyone that. It would probably make more sense to say ca being Canadian examproca, but examproco sounds better. Just don't go to examprocom.

Chris: 37:58

I don't know if anyone's gone there Made that mistake yesterday.

Andrew: 38:02

Chris, oh yeah, you thought I was into something else. Eh no, seriously, if you go to com, it's really interesting.

Tim: 38:09

It's really interesting. I'm not going. I'll take your word for it.

Andrew: 38:13

It's not that it's not safe for work, but it's just like, oh, okay. And then there's examprocouk, which is not us. We get their emails all the time.

Tim: 38:22

Nice.

Andrew: 38:23

We're co and we have ca, but we don't tell people that.

Tim: 38:26

Awesome, okay, well, definitely let us know more about the bootcamp. Oh, timing, sorry timing. I meant to ask you. You've announced the time for the Gen AI bootcamp, right?

Andrew: 38:38

Oh, you know what? I'm going to announce it here, right here.

Tim: 38:41

Oh, I thought you already had.

Andrew: 38:42

You don't have to, no no, no, this is an exclusive. You're getting exclusive of when it's going to drop. It's dropping. It's starting the third week of january. What is that? Uh, january, because I said middle of january, um 2025. Is that the next year? Uh, so if it's that, then I would imagine that it's dropping january 17th. Could have done it on the 10th, but I'm just like I'll give myself an extra week just in case. So january 17th is is the start date for the boot camp, so hopefully everybody is excited, uh, for that awesome, awesome also, apparently I'm doing a boot camp right now.

Andrew: 39:15

I don't know if anyone knows. I'm doing like a small, mini, mini camp I didn't know and yeah, for get ops or something. But yeah, very cool, very cool.

Tim: 39:22

all right, we'll make sure that gets in the show notes as well and people can follow you on Twitter. Actually, what is your favorite way for people to get in touch with you?

Andrew: 39:31

A postcard. I want people to send me postcards. I'm going to plug it right now Give me a second. I got the address. No-transcript scriber. Ontario, canada. P 0 T 2 S 0. That's P 0 T 2 S 0. Looking forward to getting your postcards and maybe if I, if I, get a postcard, someone might get something special back.

Tim: 40:01

All right.

Chris: 40:02

So, here we're with this. The first time we've we've endorsed snail mail on the pod, so I'm glad to see it can come back.

Andrew: 40:08

This is a new one for us. I call it edge mail.

Tim: 40:12

Edge mail. I love it.

Andrew: 40:13

I'll get some edge mail here.

Chris: 40:15

New term Nice.

Tim: 40:16

I love it. Edge mail. Okay, all right, so we're going to wrap up for tonight. Once again, thanks for coming on the show, andrew, it was great to talk to you and, um, if you liked what you heard and or saw this evening, uh, make sure you follow everybody involved on all socials. Make sure you send this guy a postcard several, if you could afford it. And uh, uh, you know, buy our cereal. Um, do all that good stuff and uh, and we'll see you next time.

Chris: 40:46

Hi everyone. It's Chris and this has been the Cables to Clouds podcast. Thanks for tuning in today. If you enjoyed our show, please subscribe to us in your favorite podcatcher, as well as subscribe and turn on notifications for our YouTube channel to be notified of all our new episodes. Follow us on socials at Cables to Clouds. You can also visit our website for all of the show notes at CablesToCloudscom. Thanks again for listening and see you next time.