MEDIASCAPE: Insights From Digital Changemakers
Join hosts Joseph Itaya and Anika Jackson as they dive into conversations with leaders and changemakers shaping the future of digital media. Each episode explores the frontier of multimedia, artificial intelligence, marketing, branding, and communication, spotlighting how emerging digital trends and technologies are transforming industries across the globe.
MEDIASCAPE is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. This online master’s program is designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
MEDIASCAPE: Insights From Digital Changemakers
Thought Partners: How AI Can Unlock Your Brain's Potential with Brad Moss
What if AI could be more than just a tool—what if it could become your thought companion, helping to extract and refine the ideas already in your head? In this fascinating conversation, Brad Moss, founder of Enhanced AI, reveals how his journey from video game development led to creating a platform that empowers anyone to build sophisticated AI agents without coding skills.
Moss introduces a powerful concept: AI as "the right brain of technology." While traditional technology demands precision and operates on exact data, AI excels at pattern recognition and creative generation. This distinction helps explain why many misunderstand AI's capabilities and limitations. Through his platform, Flux Prompt, Moss aims to help users orchestrate multiple AI systems to accomplish complex tasks that would typically require engineering teams.
The discussion explores how AI has evolved from a simple tool into an extension of human thought processes. Rather than replacing human creativity, well-designed AI systems amplify it by handling routine tasks and helping organize ideas. Moss shares practical examples of professionals using his platform to reverse-engineer competitor brand profiles and develop marketing materials that outperform traditional approaches.
For students and professionals navigating this AI-first landscape, Moss emphasizes understanding patterns and frameworks. Since AI fundamentally operates on pattern recognition, those who can effectively communicate frameworks will excel at guiding AI to produce meaningful results. The conversation also addresses privacy concerns, distinguishing between consumer-facing platforms and more secure API implementations.
Ready to build your own AI agents? Try FluxPrompt and discover how orchestrating AI systems can transform your productivity and creative output. Special discount available for Mediascape listeners!
https://enhanced.ai/fluxprompt/
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
Welcome to Mediascape insights from digital changemakers, a speaker series and podcast brought to you by USC Annenberg's Digital Media Management Program. Join us as we unlock the secrets to success in an increasingly digital world.
Speaker 2:We just had an entire podcast interview before we jumped on Brad Moss. You have done so much and you continue to do so much, and I'm so thrilled to have you on Mediascape Insights from Digital Changemakers today.
Speaker 3:Oh, thank you, Annika. I'm thrilled to be here, thrilled to be here.
Speaker 2:We didn't even realize we had this video game connection. I used to work in video game magazines and launched the official Xbox magazine when that came out.
Speaker 3:And you have you've created how many Over 60 video games in my career.
Speaker 2:Okay, amazing, but we're really here to talk about AI that does tie in, so yeah it does.
Speaker 3:I can tell you a little bit of the story of that. That's actually how I got into AI.
Speaker 2:Let's do it. Yeah, please share.
Speaker 3:Yeah, so I mean my background. After video games I went to big tech and I worked inside of Amazon. I ran the third party selling platform the third party marketplace platform that everyone uses to sell on Amazon and then built a new business for Amazon. Then I left after a while after making lots of money for Amazon, their shareholders, and then I went. I'm a little bit of an entrepreneur and I went and built some e-commerce companies and whatnot, but fast forward like seven years. I love video games and I'd built a lot of games. But the game business is really tough. It's really brutal and it even is right now. There's a lot of layoffs with some of a lot of my friends who've been hit by them, but it's.
Speaker 3:The process of building games is just like so cumbersome and we've seen it from inside the industry too. So about three and a half years ago I got into AI. Before ChatGPT like exploded, it was GPT-3. We even started hitting two at the time from time to time. But when I discovered that it existed generative AI I mean I've been coding regular AI in video games. You know, 20 years ago I realized this generative AI was like really powerful and that I could actually accomplish a lot of things and the whole process. The whole goal was to streamline operations of game development, of like, hey, how do you burn through iteration cycles and make sure this is fun? Because games is all about finding the fun and you got to go through iteration after iteration until you finally find it and you refine it more and more and more. And I was trying to automate some of that process and discover generative AI, and so that's how I stepped into. The whole world is trying to just refine and make game industry, the game business, work better, because making games is super fun.
Speaker 2:Yeah, well, and you just used AI to help you create a new game. You want to talk?
Speaker 3:a little about that. Yeah, this is kind of a random thing so I could grab it. I've been working on a board game idea I've had for about six years and it's all about the not nearly what this whole podcast is going to be about, but this board game idea I've had for about six years and I think the point of it is that AI is just empowering and I've had this idea for a long time. I prototyped it with my son with old card deck of cards, but then over time, you know, I started using AI and I generated like these, you know this, this whole card game. You can't really see it with my, with the blur on it no-transcript, don't go on that direction, go in this direction. But it's kind of this thought companionship that we do or that I've been doing both that like writing executive briefs to my board, you know, and to my investors. Using AI is almost a companion and a lot of things to get out of my mind, things I have but I need to kind of pull them out a little bit more.
Speaker 2:You are preaching to the choir. This isn't AI related. But a couple of years ago I was writing a book chapter for a book about women and business and business on purpose and having really purposeful lives, and I was having writer's block, not because I didn't know what I wanted to say, but because I don't have time to sit down and type it all out. So I started just voice memos to myself.
Speaker 2:Now this is before Gen AI is what it is today, so I don't know if that was an option, but now that's absolutely something I would do, and I speak to Claude every single day as my thought companion my favorite one, right now at least and so it's really exciting to see how we can really utilize this technology in the best examples. Right, it's not about writing a paper for us, it is about taking our thoughts, our original thoughts, and putting them in order and helping us think through what are we missing. So I really love that, and that's where Enhanced AI really comes in. I'm in your agent building class right now, just started this week really exciting and we met at an AI conference earlier this year, so I'd love to talk about that. So you discovered AI and then why did you decide to create this brand, this company.
Speaker 3:Yeah. So when I discovered AI and the power of it, I realized pretty quickly so you got to remember this is before ChatGPT existed. This genre of technology was really fascinating, but to me it could do, and I actually bring this up in the class. I consider, and I came to this conclusion after about a year of thinking through it and working with it. Ai feels like the right brain of technology where we're used to dealing with the left brain of technology. Right, like technology, is very precise, exact. It's like a database. You can get exact things out. That's very much like the left brain.
Speaker 3:Ai is like the right brain where it's really creative and it's generative and it's inspiring, where it's really creative and it's generative and it's inspiring. And one reason we misunderstand it at first is we're not used to dealing with a technology that can do these things, but we're also expecting the right brain to do things that the left brain can do. Right, we're expecting it to be exact and precise. It's like guess what? It doesn't. It actually knows nothing, AI, it only knows patterns. It doesn't know any actual knowledge. That's what's stored in a database and so actually, when you're communicating with it, you got to be able to connect the right and the left brain together the right way and this is a very long-winded way about saying why I got into this, but starting to realize that I realized that generative AI just needed structure, and if you give it the right structure and you break it up enough, then you can get really, really accurate responses and results for what you want.
Speaker 3:And so, but as my thought, my Amazon brain was kicking in of like, well, how does this scale? I realized quickly I was going to have more ideas and more things that I would want to build than any engineering team could ever build for me, and so what I needed was a whole layer that was like a business, user friendly layer to be able to configure AI prompts and responses talking to my Excel sheets and sending reports and connecting to the internet, various websites, etc. And I would need a kind of a layer that I could just configure all this and create a system that would do accomplish whatever goal it would be and that would take an engineer I could write out the the details and take an engineering team time to do that but I realized there's way too many of these that I would want to do and AI could do them so quickly. So I built, I started enhanced AI just all on my own and built my first prototype myself, and then it started growing because it's this whole platform, it's this Flux Prompt platform, it's what we call the platform inside of Enhanced AI, and Flux Prompt is where you know, Flux pulls things, pulls it all together into one place and you're prompting these various different components and it puts it in one place and makes you be able to orchestrate that's the best word for it Like you're the conductor of the orchestra, right?
Speaker 3:You're orchestrating these AI agents to do, or these AI calls to do all sorts of various things and tasks and, at the end of the day, I can now build. I could build five or 10 things in a day, versus, like what I could have done before with an engineering team would have taken me, you know, five times as long to accomplish that, and so that's the ultimate reason I just want to build more and build more faster, and that's really why we started it.
Speaker 2:Yeah, that's amazing because you thought like, if I have this and I can create this for myself, other people probably have the same thing. And that's an area, that where you and I really connected at AI Mavericks Conference, because I have a million and one things, I'm doing a million and one ideas, but I'm one person and finding the right people to scale with you can be difficult. And, yes, we still need to hire people.
Speaker 3:If we can use technology better to do a lot of the tasks, management and the ideation with us and be that thought partner, then that takes. I mean, our time to market is so much faster. Yeah, it also. It improves the quality. At the end of the day, when you say, find the right people, you find the people. And now, instead of your time being spent on saying, you know, rewording a paragraph or a sentence and now maybe you still need to do that in some cases but instead of spending a lot of time on doing that over and over again, you're spending a lot of time on just a little higher level, thinking of like well, should we even be talking about this paragraph? Like maybe we should throw this out, right, and you know there's people who do that now in layers, but you can consolidate to one person. So I think it's actually the quality of the work actually improves.
Speaker 3:Also with what we're doing, and people have said this before right, we're all. Ai is kind of up-leveling everything in society and right now it's a shake-up and so it's a little bit nerve-wracking, but it's the same. As you know, moving, as people have said, moving from the plow into the tractor, right, and where you're. Everyone can do more higher level work and move on. And once you learn to use these systems, that's actually the best part is, once you learn to do these, it's not engineer only, and that's the best part, right, you don't have to be actually an engineer to be building these things, and you can build amazing, fascinating systems that accomplish things that no one's ever thought of before. It's such an exciting time to me.
Speaker 2:Yeah, so what prompted you to have these classes, these courses that people can take? And because a lot of people offer a product to the market, people can buy the subscription, they can maybe look at the tutorials and they'll use your product and it's fine. But you actually are investing in people's capacity to utilize your tools to the fullest advantage.
Speaker 3:Yeah, yeah, what inspired. So I think about nine months ago we would get a lot of questions like, oh, that's so amazing, I just don't know if I can do it, or like I just don't know if I can, and realizing that people were just feeling they weren't feeling empowered the way they should. And for me, I'm just kind of a determined entrepreneur, so I just like figure it out Right. But and I'm fortunate that I've had been able to have time to do that A lot of people are really busy. They have a lot going, a lot going on, a lot of stresses, pressures from every parts of life, and so what?
Speaker 3:What we thought was like hey, let's try, just as an experiment, to teach business executives and business folks how to use or build the agents on their own and make it kind of low impact where you could do it once a. You know, the current course is set up where it's once a week for one hour, so it's easy to do, for anyone can spare an hour and we got some homework, so we make sure people are learning and doing things inside the class and that way it's, it becomes a less, more approachable thing for the very busy person who actually really wants to get into this and maybe a little scared to get in, doesn't know where to start, and we've had some people who are, who are definitely scared to start. They've been consultants or had other jobs in various spots, never deep in technology. There was always a different tech team. There was always a different tech team. They were never on that. But they jumped in and now they're building chatbots and they're building sophisticated, automated forms that are doing all sorts of things with these agents and they realize, hey, it's actually this technology, it's so cool because it's so approachable and all you have to do is you type a prompt and now you have like a new logic system, like, hey, it's going to do whatever your prompt says every time where that would have taken so long in engineering and coding and learning a language before.
Speaker 3:So we found it was a lot of people were just weren't feeling as empowered as they should be, as we felt they should be. So we decided to start our own course to do it and we cover topics of AI, right, fundamental foundational principles, and then we've still just found it's easiest to build these agents through our platform, and so we're showing people how to do it and it's the same principles you'd use if you were coding something. To be honest, if someone you know was going to engineer the exact same agent, they would maybe instead put it up doing it in our platform with the node base kind of setup. They would maybe do it in code, but it's the same principles of well, I got to call this agent now and then call these two systems and then call this agent with this information, but it's just so much faster and quicker and easier to do in the platform which it's designed to be that way.
Speaker 2:Yeah, it's been great, and this program, the MS in Digital Media Management. It's part of Annenberg School for Communication and Journalism, so you know we're talking a lot about marketing and tech stacks and ad tech and video and different components of the digital landscape, but none of those exist now without some element of AI. Even on the public relations side, I've used tools for better pitching, better matching profiles for articles or podcast interviews, things like that. I've been able to see how you can create better brand, deeper brand intentions and they'll have better personas and even the research that you can get on your potential audiences.
Speaker 2:But this is actually the next step, the next phase, and I would argue that it doesn't matter what business you're in. You still need to understand this technology, this side of the landscape, and we were talking before we actually pushed for cord about some of the needs and I'm like I just want bots to do this and that. So I need a whole team of agents to be able to be better at the job that I do, to be that strategic, creative brain, the one who's actually speaking with clients, consulting with them, coming up with their strategies and implementing and then using this technology to help me implement all the different components and go out and do that research or find out if this is the right person to be on the podcast or not.
Speaker 3:Right, yeah, yeah, entirely so it's. You know, it's funny. One of our earlier users is actually a very senior brand manager inside of Kimberly Clark Wow, and she was not necessarily tech savvy, even though she's brilliant in almost anything she does. But we found actually this AI you know kind of in the in the platform. It's though she's brilliant in almost anything she does, but we found actually this AI you know kind of in the in the platform. It's like she's like oh, this platform looks like works a lot like my brain does, because you can kind of put like a bunch of different you know like chats around. You're like, oh, I'm just gonna experiment with these, like different prompts and things, and it's all there, exposed right in front of you. And then it's like, okay, then I'll create this automation over here.
Speaker 3:But she was creating, you know she was doing some amazing things like reverse engineering a brand profile based on packaging of competitors, right, so she would load up a competitor's package and reverse engineer who their brand persona and profile was and then figure out where their brand persona and profile should fit inside, like next to that one. And it's like, whoa, like I don't think you would even do that, like without AI technology like it's, it's actually enabling us to do things that we should be doing, that we know we should be doing, that we're not because we don't have time to do it Right, and and those are like really exciting things. And but it she just kept going like into like writing scripts that you know, consulting firms were trying to do but didn't quite get the. They wouldn't quite nail their head on the scripts for these commercials. And then she, we drafted several. She drafted several scripts inside of the system and like nailed it. It was like 90% there when the other firm was, you know, doing just 50% of where they wanted to get to. And then she took that and gave it to them and they were able to, you know, adjust it and improve their work.
Speaker 3:But it's like the swath of things that you can do we found that there's about 30% of the usage in our platform is not even automation. It's just like ideation and creating these one-off, you know, like tests and trials and just playing with all the AI systems in one place. Like we didn't even say that that our platform is integrated 150 different AI platforms in one place and systems, so it's easy just to try them all together. It's like, well, I want to chain Anthropic with OpenAI, with Gemini and then do image generator between all these different systems and audio and everything. So it's kind of a nice as a playground to start playing around and actually ideate. And then we found then there's another 70% of the users or the usage. That's like building an agent that does automations and systems for you that just make your whole life easier, because you know that's the automation you want and that's the agent you need. And so you build it out and it just repeats it either every day or waits for your input, or whatever it might be.
Speaker 2:Yeah, okay, so I realized we should probably backtrack, because a lot of our audience knows what AI is. They probably don't know. They think about AI in terms of generative AI, right? Not in terms of the big AI and ML and DL and all that stuff. So AI agents might not be a concept people are familiar with listening because I know a lot of the students in the program. Some have been told they can't use AI in their businesses or in their undergrad courses and I'm going no, you have to use it in everything you do, almost right. So can you talk about agents? Because this is something I've been really excited about, because I do want to be able to leave a memo to my personal agent and say I need a book.
Speaker 2:I keep forgetting to book dental appointments for me and my daughter. Can you do that? Can you schedule the dog's next visit to the vet, right? Can you remember to order this? Can you order this medicine for me? Go into my profile. And because I keep forgetting that one of my dogs needs more gallop, rant or whatever. You know those things that take up time and that if we're so busy, I didn't iterating and working and then also trying to spend time with our families. Sometimes those things fall by the wayside.
Speaker 3:Yeah, yeah, they definitely do. Yeah, I think to answer kind of the base, foundational question and this is this is such a fascinating time. I've seen it probably three times in my career now. Every time a new kind of big wave happens, all these terms start getting thrown out there and they slowly coalesce into like what is the actual term. So the term agent has meant different things to different people all along, but I think we're actually getting closer to a coalescing of what an AI agent is, and I think it's my opinion. I've seen it many other people and again, it's still coalescing.
Speaker 3:But is any basic, like the most basic agent is any system that is including an LLM, some kind of large language model, in its call, in its process. That's like the most basic form of an agent in my mind. So it could be. You know, I have a tech system that has to hit an LLM call to do something particular and then sends back some information to me in a different structure, like in. Fundamentally, that could be called an agent. Now, some people would be like that's not smart enough to be an agent. I get that argument too, but to me it's like you don't necessarily know exactly what they're calling. Anyway, that's my initial impression for the low bar.
Speaker 2:Yeah, like you go onto a website and you have a customer service bot, right? Yeah, is that an agent?
Speaker 3:Yeah, it probably is, because it's using an LLM to reference its memory and its system. And so I think the next level up there's probably degrees right, like a really base agent. And then the more advanced agents, like, well, an agent that can actually call and use AI and LLM to call multiple different systems to like accomplish an objective and a goal, like that's probably another like the mid tier, like level of defining an agent and I think. And then the top tier is like an orchestration, where you have like one agent calling multiple agents and each one of them have their own kind of tasks, and that's like a larger kind of agents system that I would say. So those are probably the three. I would three ways I define it.
Speaker 2:Yeah, I was listening to another podcast that was talking about that. In this form concept is like you may have an agent that acts as a CMO, but it's probably and goes into a meeting for you, but that agent probably isn't just one agent, it's a whole series of agents. Each one knows there's like an expert in this one thing. Yeah Right, so instead of having to have a whole bunch of human experts, you can have that. So where does you know what's the play between humans and AI? Because that's a question people get really scared about. I always try to emphasize no, no, no. It's not something to be scared of. We need to be able to utilize this technology, but we still need our brains, because AI just knows what everybody's input into the internet, and that can be good, bad, average everybody's input into the internet and that can be good, bad, average, right, yeah, and so I think we're in a.
Speaker 3:Really my opinion we're in a fascinating place. Maybe I'll go a little too deep on this one, but and you can, you can pull me back out. I think we're in a fascinating place because a lot of people expect like, hey, can ai, can you go do all of these various things? For me now, a generative ai is like it's coming. All it is really doing is it's trained on trillions of patterns and it's finding a completion of those patterns in text, in particular, or images, and so what it's doing is trying to complete a pattern based on your input. And so, like I said, it doesn't know anything, but it knows.
Speaker 3:George Washington was president because it was told that 10,000 times through the patterns, that's the pattern it knows. To finish, it actually doesn't know it. So when you think about agents, we start getting into this multi-orchestration. Where does the agent know the pattern of how to get this your dog food Like? It hasn't. There's not a million. It wasn't trained a million times on getting dog food right. It's because these are paths that we're creating, sorry if I'm getting a little deep here.
Speaker 3:It's like when you think about an agent and this is actually why I think it breaks down, still like it's not great yet is because it hasn't been trained on all these paths to like, get dog food. So you actually have to be very specific about how you train that agent to do exactly what you want it to do, because if you don't, it will break. And so it because it doesn't know, it doesn't have enough data, it hasn't been trained enough on how to do it, and so there's a lot of this kind of work on like hey, can this agent do everything for me? And it just doesn't. It's not trained, there's not enough patterns for it to know how to do these things. Now they're working on things like MCP is a potential you know solution for that. That's more of just like allowing them to talk better together.
Speaker 3:But I think like what we produce just as an end result, like we produce like 99 or 100% accurate documentation through all through AI systems. So people put in like five different documents about a medical patient. It reads it all, it breaks it all down and spits out a perfect report that it looks the exact same, formatting everything every time with the new patient information. Now, like no LM can do that. The way we do that is we structure exactly where the you have to structure where the data goes and how the LM is going to digest the data and spits it out. So, like you have to create these, I guess, guardrails to kind of make it look exactly the way it should look, and I see a lot of these larger agents and orchestrations of agents. The same way is that you have to create these guardrails of like what it does.
Speaker 3:So in that CMO in the meeting, yes, you theoretically and you probably will have all these like micro CMO agents that are doing various things, but they've been told exactly what they should be doing. And like that's my job, that's my only job, I'm just going to go do this, always look for the, you know the opportunity in the room and then find you know something to do with that opportunity. Like that's that whole agent's job. So like though that's how you would break these down. And that's why they talk about swarms of agents, because AI can't necessarily create all these and do it very accurately. But if you're doing these individually and you're making them, you know it can be 99 or 100% accurate with the kind of way that you're building these agents and then you can use that forever, right? That agent is just really good at what it does. And then you have another agent that can call it when it needs to, and it kind of knows its way of calling it.
Speaker 2:Now, that was like sorry that was probably way too broad of a term, Because I think people need to understand this because, even if they're not using it now, these are concepts that are being talked about and even if they're not mainstream, they will be. Yeah, so you know, we want everybody to have as much data as possible and have the understanding of what all these concepts mean. Another concern that people have is privacy and security. That was actually the class that we had on Wednesday of this week was about GDPR and CPRA and different like those kind of consumer privacy restrictions and laws, but then I had all the students also add in the AI component which states and countries have AI regulations, which ones don't? What do those regulations mean? Right? And so I'd love to hear a little bit, because that's a big question people have, and that's why I use certain products more than others, Because you know, and so if somebody is building stuff within Flux Prompt, you know it's all secure.
Speaker 3:Yeah, yeah, yeah, it is secure. Well, actually, we leave it open to the user. I mean, it's secure inside the platform but the API the platform but the API. So, as of now, the recording of this the most secure legal documentation is in both Anthropic and OpenAI, from what we've reviewed in terms of, like, what they do with your data, and that's particularly now. Don't get that confused. If you put stuff in ChatGPT, it's like everywhere, so don't put stuff, anything in ChatGPT. Well, there are some like tiers and then there's some loose legal documentation around like the team tiers Enterprise. It actually gets to much better legal documentation.
Speaker 3:But if you're using API calls into these systems, then there's actually like really strict security measures around, like what you're sending in and there's really clear guidelines of like what is shared and what's trained on and what's not trained on. And, as of this, you know, as of last time we looked at, which was, I think, earlier this week, those are the two most secure systems and all of our calls. If you're calling in a third-party LLM, like we said, we have about 150 in our system. It's all through API connections to these other systems and so we're looking at all the license agreements between those API between those calls and how secure that data is, and we've been trying to expose that to people like, hey, these ones are, you know, a lot more secure than the other ones, and so if you want to be secure, that's one way.
Speaker 3:There's more ways of going about it. One of the reasons Facebook's models have been big is the Lama models and then DeepSeek is because they open source their models, which means I can take it and put it on my own server, which means it will never get trained on anything because it's on my server. I control what's getting trained, and so in those scenarios that's 100% secure if you can put one of those models in your old system, and there's many companies who've started building stuff on top of those models. That's the reason open source is good, because someone can take it and make it secure around their own systems. So that's like the extra secure way of doing the LLM calling Calling into a third party system like OpenAI and Anthropic. Those two are the most secure from a legal perspective. Now, yeah, anyway, that's.
Speaker 2:No, but that's, I think, important things for people to think about, right? So for students who are listening, where should they start? Maybe they've tried. They've used Grammarly or Chat or Cloud or name your favorite L, that you know the big ones. Maybe they've done that. Maybe they've tried out a couple of other tools. But what else do they need to know right now and how do they move forward in this AI first world?
Speaker 3:I would say, first of all, it's amazing that they're students. That's really good, because AI what it is at its core. It doesn't know anything, but it's a pattern machine. It loves, loves, loves patterns thing but it's a pattern machine. It loves, loves, loves patterns. So if you know patterns and frameworks and theories, which is taught in schools, then you're going to have a leg up, particularly if you know how to create patterns and theories.
Speaker 3:And now the kind of next level is using AI to create patterns and theories, which is actually really fun, to be honest. That's how part of the thing I do with my board game that I created is creating an ecosystem that works with certain foundational principles. But the idea is that, if you know, really lean heavier into the idea of creating a framework. Or when I say framework, I mean like hey, this marketing letter should have this kind of a title and should be this long, and then this kind of an opening paragraph should be this long. This, then this kind of an opening paragraph, should be this long, this mid body, and then this, this conclusion, right, like general marketing is like hey, call out the pain and solution, right, so like that's a framework pain, solution, framework, right, or it's usually part of a little broader one, but knowing those frameworks, and then you can tell AI hey, I want you to create this based on this, follow this framework, and then you give it your framework and you can make it your own. You can customize someone else's a little bit to be, or a well-known one a little bit to be your flavor, and then it's actually going to be able to create really good content that you, that comes almost from you and your guidelines and your frameworks. And that's kind of the world we're moving into. Is this world of everyone's kind of the conductor, the orchestrator? You're giving the AI the tasks of what to do and if you give it the right tasks of what to do, it'll do a good job. If you give it the wrong tasks, you'll still do a poor job. Right, you're not even giving it the right tasks to do so. I would actually encourage students to think a little bit more meta not Facebook, but think a little more meta in the concept of dealing with AI and how to create frameworks and structures in your prompts and how you're prompting it and then even using it to help you create those Miveta prompts and those higher level prompts. But to the actual tools themselves. I mean, I would say, definitely try Flux Prompt. Come and try it, hit us up at any point, we can help you.
Speaker 3:This is designed for the business person we is. We almost picture this like Excel. I don't know if you remember the first time you opened Excel, but it's got formulas, it's got like references, all sorts of things, but then once you learn it, you're like, oh, this is actually pretty easy and you get to use it. It's very similar. We found very similar experiences. Folks come in, don't quite understand it at first and they start learning like, oh wait, this is super easy. And they start creating these incredibly powerful AI agents and systems, and so I would say, definitely try our system out. There's several other systems that you can try in terms of agent building, orchestration, but ours is our favorite because it's easiest to use in our mind. But it's OK, we're biased.
Speaker 2:I have two questions for you. One is when you're on a run and you're speaking to AI, what folder are you speaking to?
Speaker 3:That's a really good question. I've used Perplexity, and the reason is I'll give you my two or three reasons. Perplexity is internet, first, and the other ones have been slow to catch up, so it can use the references of the internet when I need it to. Number two is I could switch the model I use on it. I can use Claude, I can use OpenAI, I can use Perplexity's own model, and I still love Claude the best, and so I use Perplexity with Claude as the model that responds to me. And then, third, is that it had the easiest interface for me to talk and run at the same time.
Speaker 3:Okay, but, and I'll tell you this, it's a weird trick is that I don't ever press like record my audio on the actual app, because on any of them, because it like tries to automatically stop at the wrong time and like it waits till I pause and I'm like no, I'm still running, I'm just breathing heavily, like I want you to keep recording me. And so I actually use the text to speech on my phone, natively on my phone, and I use that to record my audio and I may have like five minutes of talking or whatever. And then when I'm finally done with my concepts. I press stop and then it converts it all into text, and then I say go and then it creates the whole thing. And it creates the whole thing and then press the play audio In perplexity it's been the easiest for me to use and it plays the audio back. So then I listen to the response for the next five minutes. So within, like you know, within one mile, I'm listening to, like you know, maybe a couple of responses back and forth.
Speaker 2:Yeah, between me and Bob, into it Whenever I'm walking my dogs is when I remember something that I need to do, and then, yeah, I can either text myself or voice memo, but I'd rather, if it's something more complex, obviously I'd rather just talk to my AI.
Speaker 3:Yeah, yeah, yeah.
Speaker 2:Fantastic and I understand we also are going to have a special link discount for listeners if they want to sign up for the class that I'm in right now I mean, obviously it would be different cohort and learn how to build their own agents.
Speaker 3:Yes, yeah, we're providing a special discount just for listeners here. We love what Anika does and the podcast, so and again, the class is designed to be a cohort, so you actually work with other professionals around you and you get to know them. There's actually a lot of great relationships that people build, but it's everyone's learning how to build agents in their own AI agents and, believe me, we're coming to a world where there's going to be 1000s of agents, and that's okay, because half of them, you know, you'll have your own several dozen agents or a couple 100 agents at some point, and you want to be able to configure them on your own, in our view, and be able to do exactly what you want. And this is kind of the very beginning of getting your feet wet. We've had people build amazing things just coming out of the cohort. They've created businesses with just what they've built from the class of. They've learned how to build and we kind of run through everything. So we would love yeah, love people to come and check it out.
Speaker 2:Fantastic, brad. Thank you so much. It's always fun to speak with you and we can talk for so long about all the different ideas we have and how to implement different technologies and strategies. So I look forward to continuing the conversation. And, yeah, thank you for joining us on Mediascape.
Speaker 3:Yeah, thank you for having me. It's a pleasure being here, absolutely.
Speaker 1:To learn more about the Master of Science in Digital Media Management program, visit us on the web at dmmuscedu.
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