Speaker 1: Hey everybody, welcome back to another episode of JavaScript Jabber.
Speaker 1: This week on our panel, we have Steve Edwards.
Speaker 2: Yo yo yo to imitate AJ coming at you from
Speaker 2: a cloudy bit warming up Portland area.
Speaker 1: I'm Charles max Wood from Top End Devs and this
Speaker 1: week we're talking to Matthew henij Now, Matthew, you lived
Speaker 1: near me as far as I remember. We were introduced
Speaker 1: by my neighbor and we had a good talk about
Speaker 1: AI and I thought, hey, let's let's have you on
Speaker 1: the show and dive into this stuff. So you want
Speaker 1: to let people know what else they ought to know
Speaker 1: about you and then we can roll from there. Sure.
Speaker 3: Yeah, it's great to be on the show. So my
Speaker 3: name is Matthew Henich. I'm full stack developer professional last
Speaker 3: twenty years, but doing answers.
Speaker 1: I was a kid so about thirty years ago.
Speaker 3: Always love jumping into new technologies and so it's working
Speaker 3: on a project that started working on called wows dot
Speaker 3: AI about three years ago, and and then chat JPT
Speaker 3: three point five came out about two years ago and
Speaker 3: it just blew my mind. Saw a lot of huge
Speaker 3: utility from it and got super involved with starting using programming,
Speaker 3: using AI. So, yeah, I'm also from I'm from Lehigh, Utah.
Speaker 3: So yeah, it's great to be on the show. Thanks
Speaker 3: for having me.
Speaker 1: Yeah, thanks for coming. Yeah. So when he says Lehigh, Utah,
Speaker 1: that's also where I'm at, I'm same town. So anyway, so, yeah,
Speaker 1: you were showing me when we talked before about your system.
Speaker 1: What is it w AOS? I don't even know what
Speaker 1: that stands for.
Speaker 3: Yeah, stands for a Web app operating system. It's a
Speaker 3: way if using low code tools to build web apps
Speaker 3: and then you can create AI workflows that basically control
Speaker 3: the web app in real time.
Speaker 1: Yeah. It kind of reminded me a little bit of
Speaker 1: what's it called zappier, except put prompts in instead of
Speaker 1: connecting to APIs for different products. That's a good way
Speaker 1: to go. Yeah. Yeah, So I guess just to dive
Speaker 1: in because at all the coding meetups that I go
Speaker 1: to anymore, everybody's talking about AI. They're excited about it,
Speaker 1: They're excited about what you can do with it. A
Speaker 1: lot of people are, you know, diving into different aspects,
Speaker 1: you know, whether it's generating text or images or videos
Speaker 1: or anything like that. I'm a little curious to just
Speaker 1: I guess kind of get the state of the art
Speaker 1: as far as you see it of AI and how
Speaker 1: people might or might not be using it.
Speaker 3: Sure, Yeah, Yeah. So it's kind of interesting because AI
Speaker 3: is concept has been around for a very long time,
Speaker 3: and kind of what a lot of people are now
Speaker 3: referring to with AI is more of like generative AI,
Speaker 3: so using different types of architectures like auto regression, which
Speaker 3: is kind of what l MS use, like CHATCHBT or claude,
Speaker 3: and then you have other kind of gener of AI
Speaker 3: like diffusion models, which you might see something like mid
Speaker 3: journey type kind of use there, and other kinds like
Speaker 3: gams and.
Speaker 1: Type so.
Speaker 3: Kind of those are kind of some of the more
Speaker 3: popular ones. And so it's kind of interesting because when
Speaker 3: I first started getting into AI with llms, with like
Speaker 3: CHATBT three point five, it's one of the things I
Speaker 3: kind of thought about using it for is kind of
Speaker 3: like a a universal API where you can ask it
Speaker 3: to do anything and then have it basically come up
Speaker 3: with the answer. Kind of find out pretty quickly that
Speaker 3: that's not really a general little use case. It can't
Speaker 3: do everything. It has advantages and disadvantages. It's kind of
Speaker 3: interesting A lot of people. There are a lot of
Speaker 3: businesses that are kind of just a wrapper over over
Speaker 3: top of something open AI and so which a lot
Speaker 3: of times you can just use a chat like app
Speaker 3: and really get a lot of the use out of
Speaker 3: it that way. Some of the advantages of some of
Speaker 3: those kind of applications are they put a lot of
Speaker 3: thought and effort into the prompt engineering that's basically a
Speaker 3: way to explain to an AI like how it should
Speaker 3: behave and what rules it should follow, and so that
Speaker 3: there's a lot of utility out of that. But I
Speaker 3: think one of the big things that we're seeing a
Speaker 3: lot of move towards is AI agentic workflows. And there's
Speaker 3: different kind of names with that. You might hear like
Speaker 3: AI swarms, like agent swarms or agent teams, and where
Speaker 3: the huge benefit from that is instead of using like
Speaker 3: one agent, like if you have an AI agent. There's
Speaker 3: a lot of different definitions for that, but one way I
Speaker 3: like to look at it is is an agent is
Speaker 3: something that you can give a prompt to and you
Speaker 3: can get some kind of expected response from. And so
Speaker 3: if you're using something like CHATBT, you can give it
Speaker 3: like a task or asking a question and it will
Speaker 3: give you a response that's like one agent and it's
Speaker 3: more could be more general general. An agent swarm or
Speaker 3: team or like a workflow is a way to be
Speaker 3: able to have multiple different agents work together to solve
Speaker 3: a problem or perform some kind of task. And so
Speaker 3: there's a lot of advantage of that, just like having
Speaker 3: maybe one person in it for company that has to
Speaker 3: wear all the different hats, versus having a team of
Speaker 3: people working in a company to perform some kind of task.
Speaker 3: So you have different people that are specialized at different
Speaker 3: kind of roles, and each one of them does really
Speaker 3: well and what they do, and it comes together as
Speaker 3: a collective to provide the most value. You can do
Speaker 3: the same thing with a AI agent swarms or teams
Speaker 3: or workflows, whatever you want to call that. An example
Speaker 3: of that would be a project I used with WOWS
Speaker 3: created completely within WOWS is something called speak Magic, and
Speaker 3: you can check out the examples of that Speakmagic dot AI.
Speaker 3: And the idea is you can give one prompt and
Speaker 3: then have an agent swarm of like forty two different
Speaker 3: agents that basically take a story input, like you just
Speaker 3: give an idea. Maybe you have ideas for a character
Speaker 3: or something like that, and it'll create up to a
Speaker 3: two minute video of these different agents working together to
Speaker 3: create like a scene. And so it'll take the story prompt,
Speaker 3: it turns that story prompt into scenes. It's turns of
Speaker 3: scenes into scripts. It turns those scripts into shots, and
Speaker 3: then you have like different characters that are basically acting
Speaker 3: out the scene, and animates the characters like speaking to
Speaker 3: each other, and they could add like sound effects and
Speaker 3: music and different things like that to create the create it.
Speaker 3: So there's a lot of advantages of that. And so
Speaker 3: it's kind of like where I see things are going
Speaker 3: kind of the state of.
Speaker 1: Yeah, yeah, it's it's interesting because I talk to different
Speaker 1: people and it seems like everybody doing something different with AI.
Speaker 1: So you've got kind of like you were saying, where
Speaker 1: you've got people who are you know, they're trying to
Speaker 1: make a story and then they're maybe they're making it
Speaker 1: into a story with the video. Right, most of the
Speaker 1: people that I've been talking to and working with, they're
Speaker 1: using it kind of you were talking about agentic AI
Speaker 1: and the you know, kind of building out your team
Speaker 1: to do different things, and that's kind of where my
Speaker 1: interest has been, except it's more of a chat agent
Speaker 1: and less of a voice or a video agent. And
Speaker 1: so yeah, you give it a prompt and then you
Speaker 1: also give it a set of functions that allow it
Speaker 1: to do things, and then it can go off and
Speaker 1: it can do the things. But what I'm finding most
Speaker 1: people do with that is, like you said, they have
Speaker 1: a team of agents. And so you may have an
Speaker 1: agent that is kind of the coordinator or support agent,
Speaker 1: and then you know, it can go and talk to
Speaker 1: the scheduler agent to get stuff on the calendar, and
Speaker 1: go talk to the technical agent to you know, get
Speaker 1: more specialized technical feedback, or it can go talk to
Speaker 1: you know, another agent that has access to different other
Speaker 1: APIs to do other things, and and so that that's
Speaker 1: kind of the deal. And so then you get into
Speaker 1: using something like model fusion for JavaScript or I've been
Speaker 1: doing a lot with ray r ai X and Ruby
Speaker 1: to do a lot of this stuff. And you just
Speaker 1: you write a tool, and a tool is essentially that
Speaker 1: set of functions and and you make stuff run. And
Speaker 1: so yeah, one of the tools might be here's the
Speaker 1: video generation API, and you know it also uses a
Speaker 1: AI to do its work. And so anyway, it's it's
Speaker 1: really fascinating kind of see where it's all going. At
Speaker 1: the last code meet up, I went to one of
Speaker 1: the guys there had actually been using it to write fiction,
Speaker 1: and so you know, he'd use it to flesh out
Speaker 1: parts of the story or actually write parts of the story,
Speaker 1: and you know, he's like, yeah, sometimes it's really good
Speaker 1: and sometimes it's really not. It's been, it's been. It's
Speaker 1: interesting to to see how far it goes. What I
Speaker 1: tend to find is that if you do a lot
Speaker 1: of people want like the one off prompt where you
Speaker 1: just write the prompt and you immediately get back the
Speaker 1: feedback that you want. What I found is that in
Speaker 1: a lot of cases you have to refine it with
Speaker 1: the AI see what you do wind up doing as
Speaker 1: you wind up saying and I forgot to tell you this,
Speaker 1: or oh you know, I need you know, I need
Speaker 1: this scheduled, you know, every Thursday, and then it's okay. Well,
Speaker 1: when I said every Thursday, I actually meant, you know,
Speaker 1: except holidays and this and that and the other, you know,
Speaker 1: or if I'm trying to get it to write code.
Speaker 1: It's so one example of this I was using lately.
Speaker 1: I've been playing with rock but this one was on
Speaker 1: chat GPT and it you know, I said, hey, I
Speaker 1: need an audio player for my website. By the way,
Speaker 1: if you go to topendevs dot com, if you go
Speaker 1: to JavaScript jabber dot com and you the player at
Speaker 1: the bottom of the page is the player that Ai
Speaker 1: mostly wrote. And I said, hey, I need an audio
Speaker 1: player for the website. You know, I need this, I
Speaker 1: need that, I need this, right, And so it's like
Speaker 1: I want a volume, you know, I want to be
Speaker 1: able to change the volume, and I want the progress
Speaker 1: bar to go, and I want it to there's a
Speaker 1: state of the there's a state of the art thing
Speaker 1: for podcasts where you tell it to not load the
Speaker 1: audio until it's actually clicked. So until you hit play
Speaker 1: or download right, it doesn't it doesn't eagerload it. And
Speaker 1: the reason is is because the the metric DuJour for
Speaker 1: a long time with podcast is downloads, and so you
Speaker 1: can actually pad your numbers by not telling it or
Speaker 1: by by forgetting to tell it not to download every
Speaker 1: time it loads the page, right and so right, But anyway,
Speaker 1: so I told it, and then I actually asked it
Speaker 1: what other features should I put in? Right? And so anyway,
Speaker 1: we're seeing this kind of thing with a lot of
Speaker 1: different people. So some people are willing to go in
Speaker 1: and use the web interface on something like this or there,
Speaker 1: what is it? Open web. There's a there's a web
Speaker 1: interface that you can run on Olama on your own
Speaker 1: machine to do a lot of these things. And so anyway,
Speaker 1: there are a lot of options for this stuff, and
Speaker 1: some people have refined it so that it will automatically
Speaker 1: use tools to go and make web searches and stuff
Speaker 1: like that. Or if you use grock will it'll tell
Speaker 1: you that it's thinking, and it'll you can see that
Speaker 1: it's loading in different pages and you can ask it
Speaker 1: for its sources. But yeah, it's it's been fascinating to
Speaker 1: just see where all of this goes. And then of
Speaker 1: course they're the specialized uses like Cursor, AI and things
Speaker 1: like that for for programmers or you know, other AI
Speaker 1: systems for other folks, and so yeah, this guy's kind
Speaker 1: of the limit for it. I think.
Speaker 3: Yeah, it's such a broad range of different things that
Speaker 3: AI can do. We're talking about using things like Cursor.
Speaker 3: There's also other tools that are more like coding agents,
Speaker 3: so like cloud code. I don't know if you've seen
Speaker 3: Mannish kind of hit seen it a week or two ago.
Speaker 3: That one's been pretty pretty amazing what it can accomplish.
Speaker 3: But just using I typically most of my coding I
Speaker 3: use is I just open up a chat and I
Speaker 3: kind of explain a specific feature that I want and
Speaker 3: then kind of treat it like a junior dev in
Speaker 3: a way. I think that's kind of one of the
Speaker 3: best ways to treat it. Kind of give it all
Speaker 3: the different use cases, what you're what I expect going
Speaker 3: into it, and what should be coming out of the code,
Speaker 3: and and why and so, and then of course you
Speaker 3: need to check the code make sure that it's going
Speaker 3: to do what it wants. It's kind of interesting because
Speaker 3: we'll see I think we're going to see more and
Speaker 3: more where AI can do more and more coding. Some
Speaker 3: of these things like cursor are getting better, but typically
Speaker 3: the larger of your code base, more complicated get things,
Speaker 3: get it kind of runs out of context window and
Speaker 3: right starts kind of not performing quite as well. So
Speaker 3: usually smaller projects are really great. And that's changing though,
Speaker 3: so which is well, been all fun, you.
Speaker 2: Know, it's interesting. I was listening to a podcast this morning.
Speaker 2: They were talking about the topic of vibe coding, which
Speaker 2: is the idea that using stuff like yeah and so
Speaker 2: it was syntax FM and so what they are The
Speaker 2: general definition is someone who wants some simple app for
Speaker 2: some business purpose or the examples were given like I'm
Speaker 2: trying to build a little game with my kid and
Speaker 2: doing it the regular way takes forever in a day,
Speaker 2: and or a way to do a quick demo of
Speaker 2: something to see how something would look or things like that,
Speaker 2: and then you're not it's not something you're going to
Speaker 2: be using, you know, long term professionally, you know, deploy
Speaker 2: it and reuse as sort of a one off type
Speaker 2: of thing, and so, you know, usually with this kind
Speaker 2: of stuff, the code quality, from what I've understood, is
Speaker 2: how do we say less than desirable? But the idea
Speaker 2: is that you can spin up something really quick, whether
Speaker 2: it's just you know, to see how something would look,
Speaker 2: or to give you an idea of, uh, you know,
Speaker 2: how something could work, and then you could you know,
Speaker 2: tweak it from there or do something from there. But yeah,
Speaker 2: like I said, vibe coding is apparently one of the
Speaker 2: new term all the cool kids are using.
Speaker 1: Yeah. I'm not sure where the line is though, between
Speaker 1: vibe coding and actually just having the AI help you right, right,
Speaker 1: Because Yeah, at the end of the day, like in
Speaker 1: my example with the player, right, I wound up having
Speaker 1: to it it Probably seventy five percent of the code
Speaker 1: that the AI wrote I used, But the rest of it,
Speaker 1: I mean I had to use my own expertise to
Speaker 1: make it do what it do and look how I
Speaker 1: wanted it to, right, it didn't give me exactly what
Speaker 1: I wanted. So yeah, I don't know.
Speaker 2: So Matthew, let me ask you this question. You know,
Speaker 2: when it comes to this auto this uh auto generation coding.
Speaker 2: My question that I've always had that I've never had
Speaker 2: answered that I've never because I've never looked into it
Speaker 2: is what languages and tools are being used? I mean,
Speaker 2: how do you determine that? Like, you know, my focus
Speaker 2: of development, I tend to focus with letterbl and view
Speaker 2: and tailwind and inertia.
Speaker 1: And some other tools like that.
Speaker 2: And so if I'm going to tell AI to build me,
Speaker 2: you know, some little to do app, you know, to
Speaker 2: beat that one to death or even something more complex,
Speaker 2: do I say, Okay, I want you to do it
Speaker 2: with this framework and these tools in this language, or
Speaker 2: do you just say build me an app, and it
Speaker 2: builds one for you out of what it determines to
Speaker 2: be best. How does it determine that? I mean, what's
Speaker 2: what is the structure that is used to generate uh,
Speaker 2: these apps that AI is building for you.
Speaker 3: Sure, So I think there's a lot of different ways
Speaker 3: to look at this. One is I mean, if your
Speaker 3: code of yourself, I mean, you're gonna want to stick
Speaker 3: with the things that you know to some degree so
Speaker 3: that you can you can edit and and kind of
Speaker 3: understand why why things are going wrong when they do
Speaker 3: go wrong, so that you can fix things. And so
Speaker 3: typically a lot of things when it comes to programming
Speaker 3: with like using AI, like models and tools and things.
Speaker 3: There's a lot of things that are geared a little
Speaker 3: bit more towards Python. It's kind of the whole, the
Speaker 3: whole kind of with with things, and so you have
Speaker 3: a lot of tools and stuff that are kind of
Speaker 3: more geared towards Python. But if you're having it generate code,
Speaker 3: typically how I mean how lms work As you're pulling
Speaker 3: information from a whole bunch of data from like the Internet,
Speaker 3: and so the more the more data there is for
Speaker 3: a particular technology, the more likely it can perform a
Speaker 3: bit better. And so things like having like JavaScript one
Speaker 3: of the nice things a lot of times when I code,
Speaker 3: and having like AI help out with the code using
Speaker 3: something like Chatchyp's artifact or canvas and being able to
Speaker 3: have it generate code, but it can actually render that
Speaker 3: code for you, which I don't think it can really
Speaker 3: render anything else besides really JavaScript right now. And so
Speaker 3: that's really nice to build you something like that in
Speaker 3: that kind of case. But when it comes to the
Speaker 3: different technologies, I mean, it really comes down to I mean,
Speaker 3: I liked sticking with the kind of the languages that
Speaker 3: I use, React node in the back end, and so
Speaker 3: using kind of the technologies that you can help help
Speaker 3: steer it in the right way or connect it to
Speaker 3: your existing app, I think is kind of the more
Speaker 3: the way they go. Can I see right now, if
Speaker 3: you're going to go more obscure kind of languages of technologies,
Speaker 3: you're you're more likely to not get as much support
Speaker 3: from from AI helping out. One way around that is
Speaker 3: is getting the context for those technologies, like say if
Speaker 3: an ELM just doesn't really have much UH knowledge based
Speaker 3: on that, go into like the documentation for that and
Speaker 3: like giving that documentation to the relevant pieces of the
Speaker 3: documentation to t A I to help you with that.
Speaker 3: So it's kind of a way to ram as well.
Speaker 1: So one one thing with that is because I haven't
Speaker 1: really run into that. You know, most languages I use
Speaker 1: are fairly mainstream. I think Steve's in the same boat
Speaker 1: with PHP and JavaScript. But if you were using an
Speaker 1: excure obscure language, so maybe Elixir or I'm just trying
Speaker 1: to think, like, how how far obscure can you get self?
Speaker 1: I don't know ELM, ELM there might or might not.
Speaker 1: I guess it probably depends. That's one other thing that
Speaker 1: I found is that some models do much better with
Speaker 1: certain languages than others. But do you run the risk
Speaker 1: of so just to give people a little bit of context.
Speaker 1: So there are different sets of data that your LLM
Speaker 1: works from. So there's the latent space and that's everything
Speaker 1: that it got trained on, right, And you see them
Speaker 1: building these big data centers with all the GPUs, and
Speaker 1: they're trying to get as much data in as possible, right,
Speaker 1: So they come out with a new model, and generally
Speaker 1: what it means is that, hey, we've jammed more data
Speaker 1: into it. It's more training, and so you're going to
Speaker 1: get better answers from it. And then you've got the context,
Speaker 1: which is hey, for this particular query or set of
Speaker 1: queries that I make to the LM, there are prompts.
Speaker 1: You know a lot of times it's part of the prompt,
Speaker 1: but doesn't always have to be. You know, here's everything
Speaker 1: you need to know in order to give me a
Speaker 1: good answer. Right, So you're counting on its ability to process,
Speaker 1: plus whatever it's got in the lay and space that
Speaker 1: it already knows. Right. So it may already know stuff
Speaker 1: about programming in general, programming practices in the latent space
Speaker 1: because it's already been trained there. But then here are
Speaker 1: the specifics of the language that I'm using, right, Or
Speaker 1: here are the functions that are available to me, and
Speaker 1: here's how they work. Right. So maybe it's not the language.
Speaker 1: Maybe it's an obscure library that it just doesn't know
Speaker 1: as much about. Do you run the risk though, of
Speaker 1: making that or of going beyond what the context window
Speaker 1: will hold? Because the context window is essentially how much
Speaker 1: you can tell the LM right when you ask the question.
Speaker 1: It's it's all the data can hold. And so for
Speaker 1: the layperson. I'm not explaining it to Matthew. Matthew knows
Speaker 1: this stuff. But do you run the risk then of
Speaker 1: expanding beyond what the what the context will hold, because
Speaker 1: you know different models will allow you to give them
Speaker 1: different amounts of data in your context.
Speaker 3: Yeah, so a lot of models when you go beyond
Speaker 3: the context window, then they just fail and so it'll
Speaker 3: just give you an error and that could be very
Speaker 3: annoying using some different things. The other there's two other
Speaker 3: issues with main issues with larger context as well. One
Speaker 3: is the cost. So as your input context goes in
Speaker 3: like what you're supplying to it, which could be like
Speaker 3: your whole code base, then the cost for each token
Speaker 3: generation and it all goes up. And then the other
Speaker 3: problem too is as the context windows starts filling up,
Speaker 3: the less likely it's going to be able to recall
Speaker 3: and to be able to per form as well. And
Speaker 3: so that's where when you're making AI workflows kind of
Speaker 3: one of the parts is like from the whole solid concept,
Speaker 3: you have the single responsibility and I kind of apply
Speaker 3: that within each agent within it. And the idea is
Speaker 3: each agent that you create needs to be specialized, and
Speaker 3: I like to keep the context window around around four
Speaker 3: thousand tokens, which is like basically like four thousand words,
Speaker 3: but not not quite. And it seems to perform best
Speaker 3: in recall as well as be able to fall instructions
Speaker 3: with having a smaller context. And so that's one of
Speaker 3: the reasons why I like using just like a traditional
Speaker 3: just chatbot and having it generate code, is because I
Speaker 3: can limit the context exactly what I want, and I
Speaker 3: kind of pull and say, okay, I need this new
Speaker 3: file that does this thing. These are the inputs coming in.
Speaker 3: These are the outputs going out. Maybe feed it like
Speaker 3: a documentation for something you'll be using, and trying to
Speaker 3: keep that as small as possible because it'll just perform.
Speaker 1: There that way.
Speaker 3: So that's kind of how I see things.
Speaker 1: Yeah, it's it's it's kind of an interesting dilemma, but yeah,
Speaker 1: I like the I like the approach that you're advocating
Speaker 1: where you, yeah, you break it up and specialize it
Speaker 1: because then nothing has to hold too much context, right,
Speaker 1: all all your you know, your different things have to
Speaker 1: know about the things the other ones do. Is how
Speaker 1: to tell the other one to do it or how
Speaker 1: to get the data back once it's done, and so
Speaker 1: that that keeps your context smaller. Yeah, okay, ahead, So
Speaker 1: so one other thing that I'm curious about, because you're
Speaker 1: building a tool that allows people to build these workflows out.
Speaker 1: So what's that like as far as you know, not
Speaker 1: necessarily doing the prompts or putting the prompts together, and
Speaker 1: maybe you are, maybe you are doing that as part
Speaker 1: of your tool, But what's it like building an AI
Speaker 1: tool for other people to use that allows them to
Speaker 1: build these workflows?
Speaker 3: Yeah, within using WOWS. It's a it's kind of interesting,
Speaker 3: so in the sense that it's a new paradigm of programming,
Speaker 3: so kind of kind of bringing what the value of
Speaker 3: working with AI and AI workflows. I think we kind
Speaker 3: of go back a little bit kind of human evolution. So,
Speaker 3: I mean we started with language, which was a way
Speaker 3: to be able to pass on like knowledge to other people, right,
Speaker 3: and then the concept to be on having a written
Speaker 3: language where you're actually able to write down some knowledge
Speaker 3: that can be passed on really changed the way we
Speaker 3: learned and grew. Then you had like the printing press
Speaker 3: that allows it to be distributed really quickly, and that
Speaker 3: really changed humanity and that kind of sense too. Then
Speaker 3: kind of one of the next iterational steps I kind
Speaker 3: of see is is just traditional programming, where where now
Speaker 3: like with written language, was a way to pass on
Speaker 3: and to distribute knowledge, programming allowed us to capture and
Speaker 3: to pass on intelligence, and so so I could take
Speaker 3: that knowledge, I could solve a problem, create an algorithm,
Speaker 3: and then distribute that out through like a disc type
Speaker 3: of thing to other people. And now we have the
Speaker 3: Internet to be able to pass that on. And that
Speaker 3: was a way to be able to create and to
Speaker 3: distribute intelligence in that kind of sense that someone didn't.
Speaker 3: Someone could actually just reuse that algorithm to get an
Speaker 3: answer or solve a problem pretty quickly. I kind of
Speaker 3: look at working with AI workflows is kind of like
Speaker 3: another iteration where you're actually building and being able to
Speaker 3: distribute wisdom in a sense, so you can create these
Speaker 3: agents that can take a problem and have that context
Speaker 3: and understand how to apply different kind of intelligence to
Speaker 3: solve issues. And so one of the great things in
Speaker 3: that kind of sense is one of the things that
Speaker 3: AI is really good at is being able to understand
Speaker 3: like natural language and understand the intention of what someone
Speaker 3: is trying to accomplish and be able to then create
Speaker 3: many different ways of like a response. So one could
Speaker 3: be natural langages coming back and like specifying exactly how
Speaker 3: that solution could be a natural language, or could be
Speaker 3: calling a tool to perform some kind of tasks as well.
Speaker 3: So in working with AI workflows, the way I kind
Speaker 3: of look at it is almost like you're building like
Speaker 3: a team of people that would accomplish a task, and
Speaker 3: so you divide out the responsibilities between different agents that
Speaker 3: will accomplish the task. So a lot of times, like
Speaker 3: the first agent that you might make might be something
Speaker 3: called like an orchestration agent or a conductor agent, which
Speaker 3: basically will take it will take the prompt from the
Speaker 3: end user and say, okay, what team of agents should
Speaker 3: solve this problem. So if it's just a question that
Speaker 3: about the software that you're they're using, then you might
Speaker 3: just send it to this knowledge based agent that basically
Speaker 3: can give an answer really quickly. Or it might be
Speaker 3: like a task. So in the sense of speak Magic AI,
Speaker 3: you give like a story prompt and then it says, okay,
Speaker 3: all right, this is the very first prompt, So we
Speaker 3: need to create a story and we need to divide
Speaker 3: that up, so it sends it to this list of
Speaker 3: agents over here that then sequentially go through and work
Speaker 3: together to build a video basically. And that's one of
Speaker 3: the kind of different paradigm shifts as well as I
Speaker 3: think we'll see a lot more of is is. Even
Speaker 3: though a UY is very helpful and I think we'll
Speaker 3: always have a UI for like websites and things like that,
Speaker 3: there's a new kind of wave of input of natural
Speaker 3: language that we can start working with AI. So it's
Speaker 3: almost like having instead of using just a software like
Speaker 3: maybe like a SaaS product, it's almost like having an
Speaker 3: assistant that you can work with so that helps onboard
Speaker 3: you with that that or can even perform tasks for
Speaker 3: you within the software. So you say, instead of like
Speaker 3: trying to figure out, okay, let's say if we have
Speaker 3: a CRM, instead of being able to figure out, okay,
Speaker 3: how do I add a new customer type of thing
Speaker 3: and what's available here? With traditional kind of like a
Speaker 3: SaaS product, you have a real estate that is limited.
Speaker 3: So I mean, you don't want to stick a thousand
Speaker 3: different buttons on one page and expect someone to figure
Speaker 3: out how to use all these different buttons to do
Speaker 3: different things or forms and things like that. That would
Speaker 3: that's too much information for the end user. But the
Speaker 3: cool thing about working with something like that language is
Speaker 3: you could have thousands of different tools and tasks that
Speaker 3: it can perform for you. It can understand the whole website,
Speaker 3: it can navigate you to where you need to be.
Speaker 3: It can explain how to use it, or it can
Speaker 3: accomplish those tasks for you and fill in those forms
Speaker 3: for you, and they could once you fill those forms
Speaker 3: or have those kind of filled out, it can look
Speaker 3: at those and say, hey, I see that the way
Speaker 3: you fill this out is this way, but here's some
Speaker 3: more context on tips and tricks to make this better
Speaker 3: or maybe just improve improve it for them and then
Speaker 3: ask them to confirm, hey, this is exactly what I'm
Speaker 3: looking for. So working with it's definitely it's very different
Speaker 3: in the sense that with traditional programming, a lot of
Speaker 3: times we talk about like pure functions, where this one
Speaker 3: input is always going to give you this output. That's
Speaker 3: kind of one of the differences working with AI too.
Speaker 3: It's a little bit more of a black box where
Speaker 3: this input could be, uh, the answer coming out could
Speaker 3: be multiple different kind of outputs. So one of the
Speaker 3: things I like to do when making AI workflows too,
Speaker 3: is anytime anytime there's something that could be done with
Speaker 3: traditional programming, like say, if if it's parsing out information,
Speaker 3: it's doing math type of thing, whatever, it's typically you
Speaker 3: want to kind of steer away from AI, and so
Speaker 3: you want to parse the information out from the answers
Speaker 3: and then and then have kind of traditional programming actually
Speaker 3: a figure out what what the output should be and
Speaker 3: then AI use AI usually where where it performs best basically,
Speaker 3: and so and because one of the things that you
Speaker 3: dealing with AI's hallucinations, and so that's one of the
Speaker 3: things we're working with workflows as well, is dealing with
Speaker 3: hallucinations and understanding when it's important when it's not. Like
Speaker 3: you said, you had a friend that using AI to
Speaker 3: write stories, helping out that process, like that's kind of
Speaker 3: an interesting use case and something that kind of deal
Speaker 3: with with speak magic is because there's not necessarily a
Speaker 3: right answer or wrong answer necessarily, but there could be
Speaker 3: a preferred answer or a preferred response versus a non response,
Speaker 3: and so it could be a little bit more difficult
Speaker 3: on judging what is good and what's not. Except for
Speaker 3: having a human going and basically say hey, I like
Speaker 3: this and I didn't like this type of thing, and
Speaker 3: refining the prompts that you use with the agent versus
Speaker 3: something like where you might have a specific right or
Speaker 3: wrong answer. Then it's a lot easier to to improve
Speaker 3: the prompts and then just doing a whole bunch of
Speaker 3: iterations and tests and say, okay, this is you know
Speaker 3: exactly if this is a correct answer or correct response
Speaker 3: or incorrect response.
Speaker 1: It's kind of one of the ways working with it too.
Speaker 1: That's a little different too. So when you're writing these
Speaker 1: tools for other people, then are you doing some of
Speaker 1: that where you're actually trying to like beyond what they
Speaker 1: give you, you know, you add other things to the
Speaker 1: prompt get them better answers or yeah, so let it ride.
Speaker 3: Yeah, And that's one of the things ISH is trying
Speaker 3: to steer it down certain paths that will give you
Speaker 3: reliable outputs to what they're looking for. So uh and
Speaker 3: so one one of the ways, I mean, there's different
Speaker 3: kind of things to kind of look for in that
Speaker 3: one is when you getting like a prompt, one example
Speaker 3: was speak magic is is okay, we have it create
Speaker 3: like a story first, like a summary, and then we
Speaker 3: have it actually we actually have it parse out, like
Speaker 3: so we have within the story, we might add like
Speaker 3: some like H one tags that basically break out the
Speaker 3: story and says, okay, we're on act one or something
Speaker 3: like that. It actually chooses a story model first, and
Speaker 3: then it has these different steps within the story, and
Speaker 3: so we're having it kind of guiding it down a
Speaker 3: certain kind of framework, and then we split out that
Speaker 3: story by like having it add like h ones for
Speaker 3: the different sections, different steps of the story. And then
Speaker 3: and then we'll have it turn that portion of the
Speaker 3: story into different scenes and and then so like one
Speaker 3: of the things it'll do is like, okay, One thing
Speaker 3: I found is I would have it to do, like, say,
Speaker 3: if we're turning it into a scene. I was having
Speaker 3: it do too many different things at once. So I
Speaker 3: was saying, hey, I want you to write a scene
Speaker 3: for this portion of the story, and I want it
Speaker 3: to be in this very specific format that then I
Speaker 3: can then parse out to make shots from that scene.
Speaker 3: And so one of the issues I was running into
Speaker 3: is it was having a hard time doing both of
Speaker 3: those steps, and so what I ended up doing is say, okay, well,
Speaker 3: and all of them is actually fairly good at making
Speaker 3: a scene like a screenplay like a script basically because
Speaker 3: it has a lot of that information how to do that.
Speaker 3: And so I broke that out and said, okay, do
Speaker 3: that first, and then I have it's and it's response
Speaker 3: of that script to the next AI agent that says, okay,
Speaker 3: now let's add the formatting on here, and gave it
Speaker 3: all the rules of how to add that formatting to
Speaker 3: that script for that scene, and then that allows it
Speaker 3: to then parse and say, okay, this is how we
Speaker 3: we define all the different shots that make up that scene.
Speaker 3: And so then I have JavaScript basically go through and
Speaker 3: split all that into different pieces and into different shots,
Speaker 3: and then I can have the NEXTAI agent basically parse
Speaker 3: the shot out and figure out, Okay, what do we
Speaker 3: need to do to create this shot?
Speaker 1: Awesome? So we're using Jason mode because I know some
Speaker 1: of these allow you to send the data over as
Speaker 1: Jason as opposed to straight text or other formats. Most llms,
Speaker 1: I will say, are pretty good about pulling text apart
Speaker 1: and putting it back together and figuring out what you want.
Speaker 1: The Jason just gives you more accuracy, is what I found. No,
Speaker 1: I totally agree.
Speaker 3: So like with speak Magic, I'd say about eighty percent
Speaker 3: to ninety percent of all the agents I use actually
Speaker 3: using the tool use the Jayson mode and and that
Speaker 3: there's a lot of vantages for that. So if it's
Speaker 3: writing story, then I just typically I'll just have it
Speaker 3: just write the story using kind of just plain kind
Speaker 3: of texts. It's usually marked down, formatted coming out.
Speaker 1: And so.
Speaker 3: But with a Jason there's, uh one of the cool
Speaker 3: things that that one of the strategies I use with
Speaker 3: that is is with a Jason mode, you can actually
Speaker 3: like say okay, I want it's basically have a jasent
Speaker 3: object that has different properties that needs to fill in.
Speaker 3: And so one of the cool things about it is
Speaker 3: that actually has an order of how those things are
Speaker 3: because elms are sequential, so they do token by token
Speaker 3: and so it'll the top. Even though like maybe an
Speaker 3: object in JavaScript isn't necessarily has has an order when
Speaker 3: it's filling this out, it's actually has an order. So
Speaker 3: so one of the things I've done with it is
Speaker 3: is have it actually think through step by step of
Speaker 3: how to approach a problem and maybe several of these
Speaker 3: properties that's filling out. It never really ends up using.
Speaker 3: But it's a way to force it to think like
Speaker 3: lineally in a certain way that prepares it to give
Speaker 3: accurate answers, if that makes sense. So it's kind of
Speaker 3: like a form of like the thinking model that you
Speaker 3: see things like, oh, one model that kind of does,
Speaker 3: but you're you're able to then specifically guide it through
Speaker 3: a whole step of like, Okay, let's build our own
Speaker 3: context of like and have it think through different steps
Speaker 3: by filling in these different properties and then being able
Speaker 3: to use that context and it's thinking to be able
Speaker 3: to force it to then give the answer that or
Speaker 3: the properties that you're actually going to use for the
Speaker 3: next steps or for the end user.
Speaker 1: Basically, yeah, makes sense. So one other thing that I'm
Speaker 1: looking at, so just give a little bit of context
Speaker 1: for people here. I have two things going that I'm
Speaker 1: wanting to put together. One of them is I would
Speaker 1: like to have some kind of AI help agent kind
Speaker 1: of thing on top end depth. Right, So if people
Speaker 1: are looking to go through some of our courses or
Speaker 1: things like that, you know that there's essentially an AI
Speaker 1: agent that can help you figure out what to learn
Speaker 1: next or you know, maybe next steps for your career
Speaker 1: or things like that, and so I could see that
Speaker 1: as a kind of a coach if nothing else. And
Speaker 1: I'm trying to figure out how to manage the context
Speaker 1: right because people may ask a lot of questions or
Speaker 1: give it a lot of information. The other thing I'm
Speaker 1: running into is if somebody leaves and comes back, right,
Speaker 1: then do I summarize the previous context and hand it
Speaker 1: to a new query or can I just pick up
Speaker 1: where I left off? So, do you have any recommendations
Speaker 1: on something like that where maybe it's not one continue
Speaker 1: as session?
Speaker 3: So me, maybe can I re explain that that question again?
Speaker 1: Yeah, so so let's say that it's it's an ongoing tool, right.
Speaker 1: So the other the other idea I have that I
Speaker 1: want to build out is essentially I've hired virtual assistance
Speaker 1: in the past to help me do a lot of
Speaker 1: kind of routine things with the podcasts, And so with
Speaker 1: that one, I kind of see more of the team
Speaker 1: model and I can just come in and you know,
Speaker 1: I can just use a tool for it to go
Speaker 1: look up the information about my podcast, and then you know,
Speaker 1: I can tell it to do tasks one off at
Speaker 1: a time. And so for that one, I'm more thinking Okay,
Speaker 1: how do I just make sure that the workflow works.
Speaker 1: But for the other one, you know, where I wanted
Speaker 1: to remember things about the people who are coming and
Speaker 1: asking for help, and I want if they show up
Speaker 1: and say, okay, I just got a job interview, how
Speaker 1: do I prepare for this? You know, it's smart enough
Speaker 1: to say, okay, well, what can you tell me about
Speaker 1: the company? You know, maybe get a little more information,
Speaker 1: and then turn around and actually remember enough about them
Speaker 1: right to help them. Do do I have to store
Speaker 1: that myself to get the continuity or can I you know,
Speaker 1: is there some form of cashing the context window that
Speaker 1: the different lms do, because most of the time you're
Speaker 1: hitting these over APIs and so anyway, that's kind of
Speaker 1: what I'm wondering, guys, is allowing people to pick up
Speaker 1: where they left off when they're using an agent.
Speaker 3: Yeah, so when using all these different LM models, every
Speaker 3: time you send a request the API, you have to
Speaker 3: actually give it the full context of the history. And
Speaker 3: so that was one of the things that kind of
Speaker 3: hit me. I thought, for some reason I thought when
Speaker 3: I first got it involved a couple of years ago,
Speaker 3: is that you just remember your past calls like maybe
Speaker 3: you get like like a conversation idea or something like that,
Speaker 3: and it would handle all the state and the memory
Speaker 3: of past conversing with that like conversation idea or something
Speaker 3: like that. But it turns out like it's actually more stateless,
Speaker 3: and so you have to send it all the information,
Speaker 3: which has advantages of disvantage one and the disvantage would
Speaker 3: be be a lot simpler just to maybe have it
Speaker 3: manage it. But then you have a whole bunch of
Speaker 3: control over what kind of context that you're giving to it,
Speaker 3: and so you need to keep track of that information
Speaker 3: that could be I mean typically would be like in
Speaker 3: a database or something like that, and then you can
Speaker 3: pull It's also important though too. We talked about context window,
Speaker 3: and different models have different sizes of context window. I
Speaker 3: mean you're looking at like different ones. I mean some
Speaker 3: could be like only eight thousand output tokens. Some of
Speaker 3: them have like one hundred and twenty eight thousand, like
Speaker 3: opening eyes. A lot of them one hundred and twenty
Speaker 3: eight or two hundred thousand context window. And so sometimes
Speaker 3: what you need to do is if you want kind
Speaker 3: of the more full context of things, once you're you
Speaker 3: start using more and more up is to summarize the
Speaker 3: information or to pull out key pieces of information that
Speaker 3: are really important. And that's one of the things we do,
Speaker 3: like speak Magic for instance, is we have kind of
Speaker 3: a kind of in a sense like an adjson object
Speaker 3: that keeps all the important information like what kind of
Speaker 3: style does the video you need to be? Is it
Speaker 3: more animated? Is it live action like type of thing,
Speaker 3: because you want that consistency from shot to shot, from
Speaker 3: scene to scene for the video, like who are the
Speaker 3: characters and like character profiles of each one, so we
Speaker 3: have very descriptive of a lot of different things of
Speaker 3: that character. And that way, when when we're generating an
Speaker 3: image of that character, it will have all that context.
Speaker 3: It's okay, this character has curly black hair or something
Speaker 3: like that, and and different kind of things, so that
Speaker 3: when it generates the image before it turns into a video.
Speaker 1: It'll be correct.
Speaker 3: But and we also don't want to overload the context
Speaker 3: as well, so it doesn't need So it's basically, when
Speaker 3: when you're working with a call, you want to do
Speaker 3: kind of a need to know like context, and so
Speaker 3: you want to be dynamically being able to decide on
Speaker 3: what context is important. For that specific call. This is
Speaker 3: really important also, so and you kind of have different
Speaker 3: types of memory as well. So you're going to have
Speaker 3: memory for what is the state important for this specific
Speaker 3: call at the moment, there's going to be kind of
Speaker 3: memory where it has to do with the whole run
Speaker 3: of the workflow, especially when you have cycles of humans
Speaker 3: going through so like you get a question or you
Speaker 3: give a prompt, you get a response, and then you're
Speaker 3: continuing that conversation, and so you kind of need more
Speaker 3: of a more global kind of memory that matters as well.
Speaker 1: Gotcha, Okay, Yeah, I guess my concern was if I'm
Speaker 1: gathering information, you know, if I have to store information
Speaker 1: when people are asking more personal things like should I
Speaker 1: quit my job or should I you know, you know,
Speaker 1: they tell it I'm not happy where I'm at and
Speaker 1: so I'm looking for another place or things like that.
Speaker 1: I mean, I think the expectation is that it's confidential, right,
Speaker 1: I mean, if I'm given it a creative prompt and
Speaker 1: I'm having a create part of my story, maybe I'm
Speaker 1: you know, I don't feel as compromised by somebody seeing oh,
Speaker 1: you know, he generated a video with a you know,
Speaker 1: with a girl with black curly hair and a you know,
Speaker 1: and a you know, a villain creature thing that does
Speaker 1: you know, has certain powers or whatever, whatever, you know,
Speaker 1: whatever my story's about, right, it's different than if it's, oh, well,
Speaker 1: we didn't know that Chuck was not happy here and
Speaker 1: was going to start looking for another job, or you know,
Speaker 1: asked how to answer questions about things that he's not
Speaker 1: proud of in his career past, and so, you know,
Speaker 1: so keeping that confidential seems pretty important. But that's a
Speaker 1: common problem for a lot of other things. So you're
Speaker 1: telling me that, I that's something that I have to
Speaker 1: figure out because the LLLM isn't gonna keep track of
Speaker 1: it for me. One last question, because we're kind of
Speaker 1: getting toward the end of our scheduled time, is because
Speaker 1: I know a lot of people are interested in getting
Speaker 1: involved in AI and learning AI, and to be perfectly honest,
Speaker 1: I feel like if you're not if you're not getting
Speaker 1: a feel for how AI works and what it can
Speaker 1: do and how it can at least help you where
Speaker 1: you're at, let alone, where it can help the users
Speaker 1: of your applications where they're at, then in a year
Speaker 1: or so, you're going to be way behind. And so
Speaker 1: if people are sitting here and maybe you have a
Speaker 1: different opinion on that, and you can say that in
Speaker 1: a minute, But at the end of the day, if
Speaker 1: somebody's going I need to understand AI, what do I
Speaker 1: need to do in order to get there? Where do
Speaker 1: you recommend people start and what are the kinds of
Speaker 1: things that they ought to be picking up in order
Speaker 1: to be successful with this?
Speaker 3: Yeah, I think in some ways I would say kind
Speaker 3: of putch it like the same of like learning programming. So,
Speaker 3: I mean, there's so many different directions you can go
Speaker 3: with it, but it's usually best to pick out, like
Speaker 3: if you want to learn more about something like a
Speaker 3: programming language, is to pick out like a simple small
Speaker 3: project and that you're interested in or maybe that's what
Speaker 3: you want to provide type of things of value and
Speaker 3: then and then learn the parts of that to accomplish
Speaker 3: that kind of programming task.
Speaker 1: And so.
Speaker 3: I mean one of the great resources for that is
Speaker 3: is using like a AI chatbot to do the research
Speaker 3: for you and answer the questions for you. It's actually,
Speaker 3: I mean, it's a great way I've been using it
Speaker 3: for I've been learning Java recently, and it's a great
Speaker 3: way to You could have it like quiz you on
Speaker 3: what you know, what you don't know, or answer the questions.
Speaker 3: It's like a fantastic tool to be able to learn
Speaker 3: new technologies and things. But there are I mean, there's
Speaker 3: different ways of kind of approaching it, and so you
Speaker 3: can using JavaScript, you can go just plane, vanilla node
Speaker 3: type of thing setting you're accessing these different APIs UH
Speaker 3: work with maybe one API at first. A lot of
Speaker 3: them are fairly similar to each other when it comes
Speaker 3: like l MS, and so you can there's different tools
Speaker 3: you can use if you want to go beyond kind
Speaker 3: of figuring out that kind of thing yourself, UH which
Speaker 3: you can have AI help you write things different parts
Speaker 3: of creating these workflows and stuff. You can use tools
Speaker 3: though that get you a little further ahead. If you're
Speaker 3: using JavaScript, like something like line chain dot j s
Speaker 3: will give you a bit of a framework of how
Speaker 3: to to work with things. There are other tools out there,
Speaker 3: like if you're looking more for like if you're more
Speaker 3: interested in just AI workflows, you can use tools like
Speaker 3: uh in eight in which allows you to like a
Speaker 3: visual kind of way of putting together these workflows. Wows
Speaker 3: AI h is a way to do the kind of
Speaker 3: same thing where you're just using Localde tools, so you're
Speaker 3: building things out visually, and then you can use JavaScript
Speaker 3: expressions to basically to parse information, to decide what contexts
Speaker 3: to use in different places, and to kind of help
Speaker 3: control of how the workflow is ran. But there's I mean,
Speaker 3: you can use things like Cursor, which would be more
Speaker 3: of like an assistant that helps you within like your
Speaker 3: ide to build, to help you generate code quickly that
Speaker 3: could help you in the process of doing things.
Speaker 1: Or you could use like more of.
Speaker 3: A a coding agent like clawed code or like Manus,
Speaker 3: which I think Manus has a that's a not necessarily
Speaker 3: public for everyone right now as far as I know,
Speaker 3: and that will help generate larger kind of projects from
Speaker 3: single prompts you do more kind of like the vibe
Speaker 3: coding which was brought up earlier. So those are kind
Speaker 3: of some different tools that you could use. Another tool
Speaker 3: maybe look at if you want to use a lot
Speaker 3: of different models, would be something like open MCP, which
Speaker 3: is a JavaScript kind of implementation of that allows you
Speaker 3: to help standardize using different models and different kind of
Speaker 3: tools that you could use. I mean, we have something
Speaker 3: kind of similar that we added a long time ago,
Speaker 3: and to WOWS that we have like thirty I think
Speaker 3: it's thirty three different models that you can access, and
Speaker 3: they have has a very similar architecturecause you're just looking
Speaker 3: at bring a prompt in and then a response out basically,
Speaker 3: and then we handle all the API kind of stuff
Speaker 3: for you. So those are kind of different things.
Speaker 1: I kind of I.
Speaker 3: Would recommend for something that's learning different the tools and
Speaker 3: things that they can get involved with and use.
Speaker 1: Yeah, one thing that I'll add to that is and
Speaker 1: you've kind of alluded to it in the way that
Speaker 1: you've told people to approach stuff, is you know, before
Speaker 1: you're even writing code, you can just go in and
Speaker 1: go to like chat, gpt dot com or grock dot
Speaker 1: com or anthropic. I can't remember that you are all
Speaker 1: to use claude, you know, but just just get in
Speaker 1: and just start asking it questions and kind of get
Speaker 1: used to how it works, because ultimately what you're gonna
Speaker 1: be sending over is prompts a look a whole lot
Speaker 1: like your questions anyway, and so you can figure out,
Speaker 1: you know, the different tricks that work and then from there.
Speaker 1: I also recommend that people go pick up a course
Speaker 1: or a book or something that does some explanation on
Speaker 1: prompt engineering. The reason is is because the one is
Speaker 1: knowing how to access tools and access the AI and
Speaker 1: how to ask go questions. But the rest of it
Speaker 1: is just going to be down to your prompts and
Speaker 1: how you format them so that you're getting the best
Speaker 1: answers possible from your AI.
Speaker 3: So and there's a lot of iteration. So, yeah, something
Speaker 3: doesn't work with your prompt engineering, try tweaking things. Pull
Speaker 3: out pronouns like don't use this or that, be very
Speaker 3: explicit on what you mean about things. Keep the context low.
Speaker 1: Yeah, all right, well let's go ahead and do some pics. Now.
Speaker 1: I don't think you've been on the show before, so
Speaker 1: let me just explain what they are real quick. It's
Speaker 1: just us shouting out about stuff we like. So a
Speaker 1: lot of times people do TV shows or movies or
Speaker 1: technology tools or anything in between. We'll let Steve go first,
Speaker 1: and then I'll go, and then you can go last,
Speaker 1: and that way you can kind of get a feel
Speaker 1: for how we do it.
Speaker 2: Yeah, Matthew, Just one thing he didn't mention is that
Speaker 2: the high point of every episode of ours is my
Speaker 2: dad jokes, dad jokes of the week, and so anyway,
Speaker 2: make sure hopefully my sound effects are working properly. Okay.
Speaker 2: So as an example, having a conversation with my friend
Speaker 2: and I said I actually have a half brother and
Speaker 2: he said different mothers. I said, nope, shark attack, thank you,
Speaker 2: thank you. So last week was Saint Patrick's Day, I
Speaker 2: think was a week ago today, and I bought a
Speaker 2: diamond ring for my wife, but it turned out to
Speaker 2: be a fake. They gave me a sham rock. And
Speaker 2: then my dentist, who's actually a good friend of mine,
Speaker 2: I'm known for.
Speaker 1: A long time.
Speaker 2: He he got this local award where he was voted
Speaker 2: the dentist of the year. He didn't get a trophy though,
Speaker 2: he just got a little plaque. And then finally question
Speaker 2: in King Arthur's time, which of the Knights of the
Speaker 2: Roundtable collected taxes? Because you know they collected taxes, sir,
Speaker 2: charge those are the dad jokes.
Speaker 1: Of the week. All right, well how do you how
Speaker 1: do you follow that? Very humbly, very humbly. Right. So, yeah,
Speaker 1: so I don't know if I've played any new board
Speaker 1: games lately, so I'm just gonna throw out something that
Speaker 1: I've picked in the past. This is something that we've
Speaker 1: played before, me and the guys. I did find out though,
Speaker 1: that that has a different mode to it you can play,
Speaker 1: so I'm gonna pick it again even though I haven't
Speaker 1: played the mode. The campaign mode the board game is
Speaker 1: called Heat Pedal to the Metal. It's a racing game,
Speaker 1: so everyone's in race cars. You play cards in order
Speaker 1: to move forward. If you go around the turns too fast,
Speaker 1: then you take heat from your engine and put it
Speaker 1: into your deck. The heat cards don't do anything, and
Speaker 1: you have to do specific things to get rid of them.
Speaker 1: That will often make you less efficient moving forward, and
Speaker 1: so you start figuring out how to get through as
Speaker 1: many turns as possible as quickly as possible so you
Speaker 1: can get to the straightaways and take off. The campaign
Speaker 1: mode is you play multiple races and you collect money
Speaker 1: and then you can upgrade your car board game. Geek
Speaker 1: rate waits it at two point one nine, which is
Speaker 1: pretty casual gamer ish as far as that goes. It
Speaker 1: says ages ten plus. I think somebody a little younger
Speaker 1: than that could play. The strategy is not terrible, and
Speaker 1: if you kind of help them. With the mechanics, I
Speaker 1: think I think you could get like a six or
Speaker 1: seven year old to play and that would be fine.
Speaker 1: I've played it with four players that plays up to
Speaker 1: six and it takes about an hour to play. So anyway,
Speaker 1: a lot of fun. This is. This is one of
Speaker 1: the favorites lately. In fact, on Board Game Geek. It
Speaker 1: actually is number forty one overall on the games. So yeah,
Speaker 1: really enjoying that. So I'm going to pick that. And
Speaker 1: then lately I've been watching a couple of shows and
Speaker 1: I think I've picked them over the last few weeks,
Speaker 1: but I don't remember, so I'm going to pick them again.
Speaker 1: The first one is nineteen twenty three and it's prequel
Speaker 1: to Yellowstone, and I'm really enjoying that.
Speaker 2: Is that.
Speaker 1: The one is Harrison Ford. Yes, yep, yeah, I've heard that.
Speaker 2: Him trying to swagger down the street like a cowboys
Speaker 2: not the best picture, but.
Speaker 1: Yeah, yeah, he's definitely old, but it's funny.
Speaker 2: He does a new uh, he does a new commercial
Speaker 2: for I said Jeep or land Rover, and the very
Speaker 2: last thing he says is, yeah, yes, I'm doing an
Speaker 2: ad for them. Even though my last name is Ford.
Speaker 1: A sort of funny guy.
Speaker 2: I thought, that's just me.
Speaker 1: So anyway, watching that, I'm also watching Reacher. I'm enjoying that.
Speaker 1: And then I'm about done with the book Rhythm of War,
Speaker 1: which is a Brandon Sanderson book. It's the fourth book
Speaker 1: in the Stormlight Archives. So every time he releases a
Speaker 1: new book, which takes like forever to listen to on
Speaker 1: Audible because it's you know, I mean, I've been listening
Speaker 1: to the Rhythm of War, I think for almost a
Speaker 1: month now, just because I listened. You know, I have
Speaker 1: a ton of time to listen, so I listen when
Speaker 1: I'm like trying to go to sleep or when i'm
Speaker 1: you know, out in the car or something. But yeah,
Speaker 1: so he released wind in Truth and I just haven't
Speaker 1: gotten yet. So anyway, Rhythm of War I'm really enjoying
Speaker 1: as well. And then I think that's pretty much all
Speaker 1: I've got for picks this time. But Matthew, what are
Speaker 1: your picks? Okay?
Speaker 3: So I love doing research on AI, so I'm always
Speaker 3: trying to keep up on the newest models. It seems
Speaker 3: like lately a lot of new audio like texts to
Speaker 3: speech audio models have been coming out, which I've been
Speaker 3: waiting for I had some ideas of how to kind
Speaker 3: of produce some myself, but now with these coming out,
Speaker 3: like I'm pretty excited to add that to speak magic.
Speaker 3: So one of them that kind of hit that was amazing.
Speaker 3: It's called sessing Me, and it brought a lot of
Speaker 3: has a lot of emotion and kind of context, aware
Speaker 3: of of of how to provide like in real time
Speaker 3: like conversations. It just sounds a lot like a human.
Speaker 3: There's been a few others that have come out has
Speaker 3: recently been great too. One of them from open Ai.
Speaker 3: If you go to OpenAI dot fm, it has a
Speaker 3: way to be able to control like how like how
Speaker 3: the the speech should be generated, so you can put
Speaker 3: emotion in it, you can give it different things like
Speaker 3: have a kind of accents or different things too. It's
Speaker 3: pretty incredible as well. So those are kind of some
Speaker 3: of the new AI models that have been pretty exciting
Speaker 3: coming out recently. So see and then I brought up
Speaker 3: before kind of looking more into open MCP, which is
Speaker 3: seems like a using JavaScript a way to be able
Speaker 3: to connect to different models and different other kind of
Speaker 3: services as well. Is pretty exciting. Let's see, I've watched
Speaker 3: some Reacher too, So I guess they have one more
Speaker 3: episode left on that's next week.
Speaker 1: That's been Yeah, it's definitely getting there where it's yeah,
Speaker 1: it's gonna wrap up.
Speaker 3: So it sounds like it's a lot closer to like
Speaker 3: the series and like the movie, like the books basically
Speaker 3: than the movie. I guess it's pretty cool. Yeah, that's
Speaker 3: kind of that for me. But that's uh.
Speaker 1: Yeah. I meant to throw out one AI pick and
Speaker 1: I forgot about it, and that is if you want
Speaker 1: to play with some of the large language models, especially
Speaker 1: around text. I've been using an open router. I don't
Speaker 1: know if you've used them. You can also get some
Speaker 1: of them to run on your own machine if you
Speaker 1: get them off a hugging face. So those are the
Speaker 1: two resources that I'm going to recommend. One of open Router.
Speaker 1: You can use their libraries to connect to them, and
Speaker 1: then they connect to all the other models, so you
Speaker 1: can try out the LAMA three model, the open AI
Speaker 1: GPT models, you can try out Claude, and you can
Speaker 1: switch between them so you can see which ones work
Speaker 1: best without having to do a whole lot of extra
Speaker 1: work to program against each one. And then hugging Face
Speaker 1: they have their a whole bunch of other models and
Speaker 1: you can run those all locally, and then I think
Speaker 1: that's huggingface. Dot co is where you get those nice
Speaker 1: all right, matt If people want to find your stuff,
Speaker 1: where do they find you online?
Speaker 3: Yeah, check out speak Magic ai. You can see kind
Speaker 3: of we need to add some newer examples on there.
Speaker 3: Our quality has gone up and with some of these
Speaker 3: new models that come out, they'll be even better. So
Speaker 3: you can check out Wiles at Wiles dot ai and
Speaker 3: uh so, kind of interesting kind of looking at transition there.
Speaker 3: So I'm trying to decide on how to take things
Speaker 3: further with that, possibly maybe going to open source kind
Speaker 3: of route or kind of make it more community driven.
Speaker 3: So if you're interested with that, you can send me
Speaker 3: a message at Matthew at Wiles dot ai. Yeah, that's
Speaker 3: kind of kind of two kind of places for me.
Speaker 1: Awesome. All right, Well, let's go ahead and wrap it
Speaker 1: up here until next time, folks. Maxxed out