Building Agentic AI Workflows with Matthew Henage - JSJ 678

or anything like that. I'm a little curious to just I guess kind of get the state of the art as far as you see it of AI and how people might or might not be using it.

do everything. It has advantages and disadvantages. It's kind of interesting A lot of people. There are a lot of businesses that are kind of just a wrapper over over top of something open AI and so which a lot of times you can just use a chat like app and really get a lot of the use out of

it that way. Some of the advantages of some of those kind of applications are they put a lot of thought and effort into the prompt engineering that's basically a way to explain to an AI like how it should behave and what rules it should follow, and so that there's a lot of utility out of that. But I think one of the big things that we're seeing a lot of move towards is AI agentic workflows. And there's

different kind of names with that. You might hear like AI swarms, like agent swarms or agent teams, and where the huge benefit from that is instead of using like one agent, like if you have an AI agent. There's a lot of different definitions for that, but one way I like to look at it is is an agent is something that you can give a prompt to and you

can get some kind of expected response from. And so if you're using something like CHATBT, you can give it like a task or asking a question and it will give you a response that's like one agent and it's more could be more general general. An agent swarm or team or like a workflow is a way to be able to have multiple different agents work together to solve

a problem or perform some kind of task. And so there's a lot of advantage of that, just like having maybe one person in it for company that has to wear all the different hats, versus having a team of people working in a company to perform some kind of task. So you have different people that are specialized at different kind of roles, and each one of them does really well and what they do, and it comes together as

a collective to provide the most value. You can do the same thing with a AI agent swarms or teams or workflows, whatever you want to call that. An example of that would be a project I used with WOWS created completely within WOWS is something called speak Magic, and you can check out the examples of that Speakmagic dot AI. And the idea is you can give one prompt and then have an agent swarm of like forty two different agents that basically take a story input, like you just

give an idea. Maybe you have ideas for a character or something like that, and it'll create up to a two minute video of these different agents working together to create like a scene. And so it'll take the story prompt, it turns that story prompt into scenes. It's turns of

scenes into scripts. It turns those scripts into shots, and then you have like different characters that are basically acting out the scene, and animates the characters like speaking to each other, and they could add like sound effects and music and different things like that to create the create it. So there's a lot of advantages of that. And so it's kind of like where I see things are going kind of the state of.

into a story with the video. Right, most of the people that I've been talking to and working with, they're using it kind of you were talking about agentic AI and the you know, kind of building out your team to do different things, and that's kind of where my interest has been, except it's more of a chat agent and less of a voice or a video agent. And so yeah, you give it a prompt and then you also give it a set of functions that allow it to do things, and then it can go off and

it can do the things. But what I'm finding most people do with that is, like you said, they have

a team of agents. And so you may have an agent that is kind of the coordinator or support agent, and then you know, it can go and talk to the scheduler agent to get stuff on the calendar, and go talk to the technical agent to you know, get more specialized technical feedback, or it can go talk to you know, another agent that has access to different other APIs to do other things, and and so that that's

kind of the deal. And so then you get into using something like model fusion for JavaScript or I've been doing a lot with ray r ai X and Ruby to do a lot of this stuff. And you just you write a tool, and a tool is essentially that set of functions and and you make stuff run. And so yeah, one of the tools might be here's the video generation API, and you know it also uses a AI to do its work. And so anyway, it's it's

really fascinating kind of see where it's all going. At the last code meet up, I went to one of the guys there had actually been using it to write fiction, and so you know, he'd use it to flesh out parts of the story or actually write parts of the story, and you know, he's like, yeah, sometimes it's really good and sometimes it's really not. It's been, it's been. It's

interesting to to see how far it goes. What I tend to find is that if you do a lot of people want like the one off prompt where you just write the prompt and you immediately get back the feedback that you want. What I found is that in a lot of cases you have to refine it with the AI see what you do wind up doing as you wind up saying and I forgot to tell you this, or oh you know, I need you know, I need

this scheduled, you know, every Thursday, and then it's okay. Well, when I said every Thursday, I actually meant, you know, except holidays and this and that and the other, you know, or if I'm trying to get it to write code. It's so one example of this I was using lately. I've been playing with rock but this one was on chat GPT and it you know, I said, hey, I

need an audio player for my website. By the way, if you go to topendevs dot com, if you go to JavaScript jabber dot com and you the player at the bottom of the page is the player that Ai mostly wrote. And I said, hey, I need an audio

player for the website. You know, I need this, I need that, I need this, right, And so it's like I want a volume, you know, I want to be able to change the volume, and I want the progress bar to go, and I want it to there's a state of the there's a state of the art thing for podcasts where you tell it to not load the audio until it's actually clicked. So until you hit play

or download right, it doesn't it doesn't eagerload it. And the reason is is because the the metric DuJour for a long time with podcast is downloads, and so you can actually pad your numbers by not telling it or by by forgetting to tell it not to download every time it loads the page, right and so right, But anyway, so I told it, and then I actually asked it what other features should I put in? Right? And so anyway, we're seeing this kind of thing with a lot of

different people. So some people are willing to go in and use the web interface on something like this or there, what is it? Open web. There's a there's a web interface that you can run on Olama on your own machine to do a lot of these things. And so anyway, there are a lot of options for this stuff, and some people have refined it so that it will automatically use tools to go and make web searches and stuff

like that. Or if you use grock will it'll tell you that it's thinking, and it'll you can see that it's loading in different pages and you can ask it for its sources. But yeah, it's it's been fascinating to just see where all of this goes. And then of course they're the specialized uses like Cursor, AI and things like that for for programmers or you know, other AI systems for other folks, and so yeah, this guy's kind of the limit for it. I think.

But just using I typically most of my coding I use is I just open up a chat and I kind of explain a specific feature that I want and then kind of treat it like a junior dev in a way. I think that's kind of one of the best ways to treat it. Kind of give it all the different use cases, what you're what I expect going into it, and what should be coming out of the code, and and why and so, and then of course you need to check the code make sure that it's going

to do what it wants. It's kind of interesting because we'll see I think we're going to see more and more where AI can do more and more coding. Some of these things like cursor are getting better, but typically the larger of your code base, more complicated get things, get it kind of runs out of context window and right starts kind of not performing quite as well. So usually smaller projects are really great. And that's changing though, so which is well, been all fun, you.

They were talking about the topic of vibe coding, which is the idea that using stuff like yeah and so it was syntax FM and so what they are The general definition is someone who wants some simple app for some business purpose or the examples were given like I'm trying to build a little game with my kid and doing it the regular way takes forever in a day, and or a way to do a quick demo of something to see how something would look or things like that,

and then you're not it's not something you're going to be using, you know, long term professionally, you know, deploy it and reuse as sort of a one off type of thing, and so, you know, usually with this kind of stuff, the code quality, from what I've understood, is

how do we say less than desirable? But the idea is that you can spin up something really quick, whether it's just you know, to see how something would look, or to give you an idea of, uh, you know, how something could work, and then you could you know, tweak it from there or do something from there. But yeah, like I said, vibe coding is apparently one of the new term all the cool kids are using.

bit more towards Python. It's kind of the whole, the whole kind of with with things, and so you have a lot of tools and stuff that are kind of more geared towards Python. But if you're having it generate code, typically how I mean how lms work As you're pulling information from a whole bunch of data from like the Internet, and so the more the more data there is for a particular technology, the more likely it can perform a

bit better. And so things like having like JavaScript one of the nice things a lot of times when I code, and having like AI help out with the code using something like Chatchyp's artifact or canvas and being able to have it generate code, but it can actually render that code for you, which I don't think it can really render anything else besides really JavaScript right now. And so that's really nice to build you something like that in

that kind of case. But when it comes to the different technologies, I mean, it really comes down to I mean, I liked sticking with the kind of the languages that I use, React node in the back end, and so using kind of the technologies that you can help help steer it in the right way or connect it to your existing app, I think is kind of the more

the way they go. Can I see right now, if you're going to go more obscure kind of languages of technologies, you're you're more likely to not get as much support from from AI helping out. One way around that is is getting the context for those technologies, like say if an ELM just doesn't really have much UH knowledge based on that, go into like the documentation for that and like giving that documentation to the relevant pieces of the documentation to t A I to help you with that.

So it's kind of a way to ram as well.

certain languages than others. But do you run the risk of so just to give people a little bit of context. So there are different sets of data that your LLM works from. So there's the latent space and that's everything that it got trained on, right, And you see them building these big data centers with all the GPUs, and they're trying to get as much data in as possible, right, So they come out with a new model, and generally what it means is that, hey, we've jammed more data

into it. It's more training, and so you're going to get better answers from it. And then you've got the context, which is hey, for this particular query or set of queries that I make to the LM, there are prompts. You know a lot of times it's part of the prompt, but doesn't always have to be. You know, here's everything you need to know in order to give me a good answer. Right, So you're counting on its ability to process, plus whatever it's got in the lay and space that

it already knows. Right. So it may already know stuff about programming in general, programming practices in the latent space because it's already been trained there. But then here are the specifics of the language that I'm using, right, Or here are the functions that are available to me, and here's how they work. Right. So maybe it's not the language. Maybe it's an obscure library that it just doesn't know

as much about. Do you run the risk though, of making that or of going beyond what the context window will hold? Because the context window is essentially how much you can tell the LM right when you ask the question. It's it's all the data can hold. And so for the layperson. I'm not explaining it to Matthew. Matthew knows

this stuff. But do you run the risk then of expanding beyond what the what the context will hold, because you know different models will allow you to give them different amounts of data in your context.

generation and it all goes up. And then the other problem too is as the context windows starts filling up, the less likely it's going to be able to recall and to be able to per form as well. And so that's where when you're making AI workflows kind of one of the parts is like from the whole solid concept, you have the single responsibility and I kind of apply

that within each agent within it. And the idea is each agent that you create needs to be specialized, and I like to keep the context window around around four thousand tokens, which is like basically like four thousand words, but not not quite. And it seems to perform best in recall as well as be able to fall instructions

with having a smaller context. And so that's one of the reasons why I like using just like a traditional just chatbot and having it generate code, is because I can limit the context exactly what I want, and I kind of pull and say, okay, I need this new file that does this thing. These are the inputs coming in. These are the outputs going out. Maybe feed it like a documentation for something you'll be using, and trying to keep that as small as possible because it'll just perform.

that that keeps your context smaller. Yeah, okay, ahead, So so one other thing that I'm curious about, because you're building a tool that allows people to build these workflows out. So what's that like as far as you know, not necessarily doing the prompts or putting the prompts together, and maybe you are, maybe you are doing that as part of your tool, But what's it like building an AI tool for other people to use that allows them to build these workflows?

of go back a little bit kind of human evolution. So, I mean we started with language, which was a way to be able to pass on like knowledge to other people, right, and then the concept to be on having a written language where you're actually able to write down some knowledge that can be passed on really changed the way we learned and grew. Then you had like the printing press that allows it to be distributed really quickly, and that

really changed humanity and that kind of sense too. Then kind of one of the next iterational steps I kind of see is is just traditional programming, where where now like with written language, was a way to pass on and to distribute knowledge, programming allowed us to capture and to pass on intelligence, and so so I could take that knowledge, I could solve a problem, create an algorithm, and then distribute that out through like a disc type of thing to other people. And now we have the

Internet to be able to pass that on. And that was a way to be able to create and to distribute intelligence in that kind of sense that someone didn't. Someone could actually just reuse that algorithm to get an

answer or solve a problem pretty quickly. I kind of look at working with AI workflows is kind of like another iteration where you're actually building and being able to distribute wisdom in a sense, so you can create these agents that can take a problem and have that context and understand how to apply different kind of intelligence to

solve issues. And so one of the great things in that kind of sense is one of the things that AI is really good at is being able to understand like natural language and understand the intention of what someone is trying to accomplish and be able to then create many different ways of like a response. So one could be natural langages coming back and like specifying exactly how that solution could be a natural language, or could be calling a tool to perform some kind of tasks as well.

So in working with AI workflows, the way I kind of look at it is almost like you're building like a team of people that would accomplish a task, and so you divide out the responsibilities between different agents that will accomplish the task. So a lot of times, like the first agent that you might make might be something called like an orchestration agent or a conductor agent, which basically will take it will take the prompt from the end user and say, okay, what team of agents should

solve this problem. So if it's just a question that about the software that you're they're using, then you might just send it to this knowledge based agent that basically can give an answer really quickly. Or it might be

like a task. So in the sense of speak Magic AI, you give like a story prompt and then it says, okay, all right, this is the very first prompt, So we need to create a story and we need to divide that up, so it sends it to this list of agents over here that then sequentially go through and work together to build a video basically. And that's one of the kind of different paradigm shifts as well as I

think we'll see a lot more of is is. Even though a UY is very helpful and I think we'll always have a UI for like websites and things like that, there's a new kind of wave of input of natural language that we can start working with AI. So it's almost like having instead of using just a software like maybe like a SaaS product, it's almost like having an assistant that you can work with so that helps onboard you with that that or can even perform tasks for

you within the software. So you say, instead of like trying to figure out, okay, let's say if we have a CRM, instead of being able to figure out, okay, how do I add a new customer type of thing and what's available here? With traditional kind of like a SaaS product, you have a real estate that is limited. So I mean, you don't want to stick a thousand different buttons on one page and expect someone to figure out how to use all these different buttons to do

different things or forms and things like that. That would that's too much information for the end user. But the cool thing about working with something like that language is you could have thousands of different tools and tasks that it can perform for you. It can understand the whole website, it can navigate you to where you need to be.

It can explain how to use it, or it can accomplish those tasks for you and fill in those forms for you, and they could once you fill those forms or have those kind of filled out, it can look at those and say, hey, I see that the way you fill this out is this way, but here's some more context on tips and tricks to make this better or maybe just improve improve it for them and then ask them to confirm, hey, this is exactly what I'm

looking for. So working with it's definitely it's very different in the sense that with traditional programming, a lot of times we talk about like pure functions, where this one input is always going to give you this output. That's kind of one of the differences working with AI too. It's a little bit more of a black box where this input could be, uh, the answer coming out could

be multiple different kind of outputs. So one of the things I like to do when making AI workflows too, is anytime anytime there's something that could be done with traditional programming, like say, if if it's parsing out information, it's doing math type of thing, whatever, it's typically you want to kind of steer away from AI, and so you want to parse the information out from the answers and then and then have kind of traditional programming actually

a figure out what what the output should be and then AI use AI usually where where it performs best basically, and so and because one of the things that you dealing with AI's hallucinations, and so that's one of the things we're working with workflows as well, is dealing with

hallucinations and understanding when it's important when it's not. Like you said, you had a friend that using AI to write stories, helping out that process, like that's kind of an interesting use case and something that kind of deal with with speak magic is because there's not necessarily a right answer or wrong answer necessarily, but there could be a preferred answer or a preferred response versus a non response, and so it could be a little bit more difficult

on judging what is good and what's not. Except for having a human going and basically say hey, I like this and I didn't like this type of thing, and refining the prompts that you use with the agent versus something like where you might have a specific right or wrong answer. Then it's a lot easier to to improve the prompts and then just doing a whole bunch of iterations and tests and say, okay, this is you know exactly if this is a correct answer or correct response or incorrect response.

reliable outputs to what they're looking for. So uh and so one one of the ways, I mean, there's different kind of things to kind of look for in that one is when you getting like a prompt, one example was speak magic is is okay, we have it create like a story first, like a summary, and then we have it actually we actually have it parse out, like so we have within the story, we might add like some like H one tags that basically break out the story and says, okay, we're on act one or something

like that. It actually chooses a story model first, and then it has these different steps within the story, and so we're having it kind of guiding it down a certain kind of framework, and then we split out that story by like having it add like h ones for

the different sections, different steps of the story. And then and then we'll have it turn that portion of the story into different scenes and and then so like one of the things it'll do is like, okay, One thing I found is I would have it to do, like, say, if we're turning it into a scene. I was having

it do too many different things at once. So I was saying, hey, I want you to write a scene for this portion of the story, and I want it to be in this very specific format that then I can then parse out to make shots from that scene.

And so one of the issues I was running into is it was having a hard time doing both of those steps, and so what I ended up doing is say, okay, well, and all of them is actually fairly good at making a scene like a screenplay like a script basically because it has a lot of that information how to do that.

And so I broke that out and said, okay, do that first, and then I have it's and it's response of that script to the next AI agent that says, okay, now let's add the formatting on here, and gave it all the rules of how to add that formatting to that script for that scene, and then that allows it to then parse and say, okay, this is how we we define all the different shots that make up that scene.

And so then I have JavaScript basically go through and split all that into different pieces and into different shots, and then I can have the NEXTAI agent basically parse the shot out and figure out, Okay, what do we need to do to create this shot?

and so it'll the top. Even though like maybe an object in JavaScript isn't necessarily has has an order when it's filling this out, it's actually has an order. So so one of the things I've done with it is is have it actually think through step by step of how to approach a problem and maybe several of these properties that's filling out. It never really ends up using. But it's a way to force it to think like lineally in a certain way that prepares it to give

accurate answers, if that makes sense. So it's kind of like a form of like the thinking model that you see things like, oh, one model that kind of does, but you're you're able to then specifically guide it through a whole step of like, Okay, let's build our own context of like and have it think through different steps by filling in these different properties and then being able to use that context and it's thinking to be able to force it to then give the answer that or

the properties that you're actually going to use for the next steps or for the end user.

of thing on top end depth. Right, So if people are looking to go through some of our courses or things like that, you know that there's essentially an AI agent that can help you figure out what to learn next or you know, maybe next steps for your career or things like that, and so I could see that as a kind of a coach if nothing else. And I'm trying to figure out how to manage the context right because people may ask a lot of questions or

give it a lot of information. The other thing I'm running into is if somebody leaves and comes back, right, then do I summarize the previous context and hand it to a new query or can I just pick up where I left off? So, do you have any recommendations on something like that where maybe it's not one continue as session?

a time. And so for that one, I'm more thinking Okay, how do I just make sure that the workflow works. But for the other one, you know, where I wanted to remember things about the people who are coming and asking for help, and I want if they show up and say, okay, I just got a job interview, how do I prepare for this? You know, it's smart enough to say, okay, well, what can you tell me about

the company? You know, maybe get a little more information, and then turn around and actually remember enough about them right to help them. Do do I have to store that myself to get the continuity or can I you know, is there some form of cashing the context window that the different lms do, because most of the time you're hitting these over APIs and so anyway, that's kind of what I'm wondering, guys, is allowing people to pick up where they left off when they're using an agent.

hit me. I thought, for some reason I thought when I first got it involved a couple of years ago, is that you just remember your past calls like maybe you get like like a conversation idea or something like that, and it would handle all the state and the memory of past conversing with that like conversation idea or something

like that. But it turns out like it's actually more stateless, and so you have to send it all the information, which has advantages of disvantage one and the disvantage would be be a lot simpler just to maybe have it manage it. But then you have a whole bunch of control over what kind of context that you're giving to it, and so you need to keep track of that information that could be I mean typically would be like in a database or something like that, and then you can

pull It's also important though too. We talked about context window, and different models have different sizes of context window. I mean you're looking at like different ones. I mean some could be like only eight thousand output tokens. Some of them have like one hundred and twenty eight thousand, like opening eyes. A lot of them one hundred and twenty

eight or two hundred thousand context window. And so sometimes what you need to do is if you want kind of the more full context of things, once you're you start using more and more up is to summarize the information or to pull out key pieces of information that are really important. And that's one of the things we do, like speak Magic for instance, is we have kind of a kind of in a sense like an adjson object that keeps all the important information like what kind of

style does the video you need to be? Is it more animated? Is it live action like type of thing, because you want that consistency from shot to shot, from scene to scene for the video, like who are the characters and like character profiles of each one, so we have very descriptive of a lot of different things of that character. And that way, when when we're generating an image of that character, it will have all that context.

It's okay, this character has curly black hair or something like that, and and different kind of things, so that when it generates the image before it turns into a video.

types of memory as well. So you're going to have memory for what is the state important for this specific call at the moment, there's going to be kind of memory where it has to do with the whole run of the workflow, especially when you have cycles of humans going through so like you get a question or you give a prompt, you get a response, and then you're continuing that conversation, and so you kind of need more of a more global kind of memory that matters as well.

I mean, I think the expectation is that it's confidential, right, I mean, if I'm given it a creative prompt and I'm having a create part of my story, maybe I'm you know, I don't feel as compromised by somebody seeing oh, you know, he generated a video with a you know, with a girl with black curly hair and a you know, and a you know, a villain creature thing that does you know, has certain powers or whatever, whatever, you know,

whatever my story's about, right, it's different than if it's, oh, well, we didn't know that Chuck was not happy here and was going to start looking for another job, or you know, asked how to answer questions about things that he's not proud of in his career past, and so, you know, so keeping that confidential seems pretty important. But that's a

common problem for a lot of other things. So you're telling me that, I that's something that I have to figure out because the LLLM isn't gonna keep track of

it for me. One last question, because we're kind of getting toward the end of our scheduled time, is because I know a lot of people are interested in getting involved in AI and learning AI, and to be perfectly honest, I feel like if you're not if you're not getting a feel for how AI works and what it can do and how it can at least help you where you're at, let alone, where it can help the users of your applications where they're at, then in a year

or so, you're going to be way behind. And so if people are sitting here and maybe you have a different opinion on that, and you can say that in a minute, But at the end of the day, if somebody's going I need to understand AI, what do I need to do in order to get there? Where do you recommend people start and what are the kinds of things that they ought to be picking up in order to be successful with this?

of putch it like the same of like learning programming. So, I mean, there's so many different directions you can go with it, but it's usually best to pick out, like if you want to learn more about something like a programming language, is to pick out like a simple small project and that you're interested in or maybe that's what you want to provide type of things of value and then and then learn the parts of that to accomplish that kind of programming task.

new technologies and things. But there are I mean, there's different ways of kind of approaching it, and so you can using JavaScript, you can go just plane, vanilla node type of thing setting you're accessing these different APIs UH

work with maybe one API at first. A lot of them are fairly similar to each other when it comes like l MS, and so you can there's different tools you can use if you want to go beyond kind of figuring out that kind of thing yourself, UH which you can have AI help you write things different parts of creating these workflows and stuff. You can use tools

though that get you a little further ahead. If you're using JavaScript, like something like line chain dot j s will give you a bit of a framework of how to to work with things. There are other tools out there, like if you're looking more for like if you're more interested in just AI workflows, you can use tools like uh in eight in which allows you to like a

visual kind of way of putting together these workflows. Wows AI h is a way to do the kind of same thing where you're just using Localde tools, so you're building things out visually, and then you can use JavaScript expressions to basically to parse information, to decide what contexts to use in different places, and to kind of help

control of how the workflow is ran. But there's I mean, you can use things like Cursor, which would be more of like an assistant that helps you within like your ide to build, to help you generate code quickly that could help you in the process of doing things.

of some different tools that you could use. Another tool maybe look at if you want to use a lot of different models, would be something like open MCP, which is a JavaScript kind of implementation of that allows you to help standardize using different models and different kind of

tools that you could use. I mean, we have something kind of similar that we added a long time ago, and to WOWS that we have like thirty I think it's thirty three different models that you can access, and they have has a very similar architecturecause you're just looking at bring a prompt in and then a response out basically, and then we handle all the API kind of stuff for you. So those are kind of different things.

com or anthropic. I can't remember that you are all to use claude, you know, but just just get in and just start asking it questions and kind of get used to how it works, because ultimately what you're gonna be sending over is prompts a look a whole lot like your questions anyway, and so you can figure out, you know, the different tricks that work and then from there. I also recommend that people go pick up a course or a book or something that does some explanation on

prompt engineering. The reason is is because the one is knowing how to access tools and access the AI and how to ask go questions. But the rest of it is just going to be down to your prompts and how you format them so that you're getting the best answers possible from your AI.

thank you. So last week was Saint Patrick's Day, I think was a week ago today, and I bought a diamond ring for my wife, but it turned out to be a fake. They gave me a sham rock. And then my dentist, who's actually a good friend of mine, I'm known for.

called Heat Pedal to the Metal. It's a racing game, so everyone's in race cars. You play cards in order to move forward. If you go around the turns too fast, then you take heat from your engine and put it into your deck. The heat cards don't do anything, and you have to do specific things to get rid of them. That will often make you less efficient moving forward, and so you start figuring out how to get through as many turns as possible as quickly as possible so you

can get to the straightaways and take off. The campaign mode is you play multiple races and you collect money and then you can upgrade your car board game. Geek rate waits it at two point one nine, which is pretty casual gamer ish as far as that goes. It says ages ten plus. I think somebody a little younger than that could play. The strategy is not terrible, and

if you kind of help them. With the mechanics, I think I think you could get like a six or seven year old to play and that would be fine. I've played it with four players that plays up to six and it takes about an hour to play. So anyway, a lot of fun. This is. This is one of the favorites lately. In fact, on Board Game Geek. It actually is number forty one overall on the games. So yeah,

really enjoying that. So I'm going to pick that. And then lately I've been watching a couple of shows and I think I've picked them over the last few weeks, but I don't remember, so I'm going to pick them again. The first one is nineteen twenty three and it's prequel to Yellowstone, and I'm really enjoying that.

month now, just because I listened. You know, I have a ton of time to listen, so I listen when I'm like trying to go to sleep or when i'm you know, out in the car or something. But yeah, so he released wind in Truth and I just haven't gotten yet. So anyway, Rhythm of War I'm really enjoying as well. And then I think that's pretty much all I've got for picks this time. But Matthew, what are your picks? Okay?

It's called sessing Me, and it brought a lot of has a lot of emotion and kind of context, aware of of of how to provide like in real time like conversations. It just sounds a lot like a human. There's been a few others that have come out has recently been great too. One of them from open Ai. If you go to OpenAI dot fm, it has a way to be able to control like how like how the the speech should be generated, so you can put emotion in it, you can give it different things like

have a kind of accents or different things too. It's pretty incredible as well. So those are kind of some of the new AI models that have been pretty exciting coming out recently. So see and then I brought up before kind of looking more into open MCP, which is seems like a using JavaScript a way to be able to connect to different models and different other kind of

services as well. Is pretty exciting. Let's see, I've watched some Reacher too, So I guess they have one more episode left on that's next week.

You can use their libraries to connect to them, and then they connect to all the other models, so you can try out the LAMA three model, the open AI GPT models, you can try out Claude, and you can switch between them so you can see which ones work best without having to do a whole lot of extra work to program against each one. And then hugging Face they have their a whole bunch of other models and you can run those all locally, and then I think

that's huggingface. Dot co is where you get those nice all right, matt If people want to find your stuff, where do they find you online?

So if you're interested with that, you can send me a message at Matthew at Wiles dot ai. Yeah, that's kind of kind of two kind of places for me.