Charting the Media Terrain: A Dive into Content Cartography with Minimap AI's Clayton Smith

Embark on an enlightening expedition with Clayton Smith of Minimap AI as he charts the uncharted territories of the media landscape. By transforming the way we visualize news distribution, Minimap AI's content cartography promises to arm you with a deeper understanding of the stories shaping our world, far surpassing the left-right dichotomy.

This episode maps out a revolutionary system that spatially arranges news topics, revealing trends and the breadth of coverage at a glance. The discussion with Clayton navigates through examples such as Tesla's activities in China, illustrating the profound impact a single story can have across multiple sectors. The conversation also sheds light on the platform's ability to connect seemingly unrelated stories, offering a nuanced perspective often lost in the chaos of traditional media consumption.

Lastly, we trace Minimap's evolution from a product search mapping tool to a beacon in the dense fog of news and social media content. Clayton and I dissect the challenges companies face in aligning their brand with their content output and discuss how Minimap's innovative algorithms can detect discrepancies that may slip through the cracks. By the end of our journey, you'll be equipped with insights into how Minimap AI not only redefines content consumption but also serves as a vital compass in the quest for authentic brand and media interaction.

Contact Clayton to learn more at https://minimap.ai

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Transcript

Speaker 1: 0:00

Man. I really meant to get this episode out a while back, but ran into some trouble with post-production. Deep breath. It's okay, though. We are back, and that's what's important.

Speaker 1: 0:11

Y'all know, a big passion of mine is media literacy and, more specifically, news literacy. I truly believe that we and when I say we, I mean the citizens of the United States in order for us to thrive and to move in the right direction as a country, we need to have a society of people that are as well-informed as possible. Notice, I did not just say informed, I said well-informed. We need to have the capacity to zoom out from our narrow understanding of the news that we consume, which typically consists of news that confirms our own bias and tells us what we want to hear, makes us feel good about the team we're on. We need to be able to see the broader news landscape to get a better understanding of the entire media ecosystem. My guest today has created an innovative way to do just that, but from a visual perspective.

Speaker 1: 1:06

Clayton Smith is the founder of Minimap, which is a cartography platform that uses artificial intelligence to visually map out the content landscape. It creates maps of large content spaces, specifically the news, because when it comes to consuming content. Most of us consumers are in the dark, especially where it concerns how much content is being produced by whom and for what end. To be able to see the content and the producers of content in a visual that helps us connect the dots in a more comprehensive way is incredibly valuable, because it helps us better understand what's going on in the world around us, or at least what we're being. What's going on in the world around us, or at least what we're being told is going on in the world around us. The mini-map gives us a way to zoom out and see the bigger picture Context about our content matters, so that we can think critically about the information we are consuming. To hear more about this fascinating topic of content cartography, keep listening.

Speaker 2: 2:22

Welcome to the Communication 24-7 podcast where we communicate about how Jen it's so good to see you again. Yeah, likewise.

Speaker 1: 2:41

I'm excited about this conversation because we have not been able to have a conversation in some time.

Speaker 2: 2:57

When was the last time we actually spoke, released our alpha of Minimap and we started just talking about what a content cartography platform looks like and just how useful it would be to visually see the content landscape.

Speaker 1: 3:13

Yeah, that was really exciting to be able to see that and just get the demonstration from you and just have that conversation about media and content and how we receive it and how it's produced and all of those things. So that's why I'm really happy that you agreed to be on the show, because number one, for selfish reasons I want to get an update on what's going on with you and what's going on with the program, but also just to be able to have a conversation with someone. I think we are aligned in our beliefs and values when it comes to the media ecosystem and yeah, so I just want to kind of pick up where we left off. So for the audience, would you mind just introducing yourself so they know, because I know who you are, but let the listeners kind of catch up with who you are and what you've been doing.

Speaker 2: 4:04

Yeah, absolutely so. My name is Clayton Smith and I'm the founder of Minimap AI. Minimap AI is a content cartography platform, and so what that means simply is we make maps of content, and so the value proposition that we're putting out there is we're taking the algorithm and basically unfolding its brain, projecting it out onto an actual landscape and showing people the content landscape as the algorithm understands it, and what that does for us being able to spatially organize content is allows us to, at a glance, zoom out and see how content is manifesting itself, where it's concentrated, where it isn't, and so it allows us to view developing stories or collections of articles in ways that we can't really see or understand through basic metrics or flat lists of results. And so that's what we're trying to do at Minimap AI is just show people the content landscape and give everybody a medium, a common viewpoint, to understand this vast information space that we're dealing with in 2024 and beyond.

Speaker 1: 5:15

Yeah, that's why I find it so fascinating, because, you know, in my previous life as a media analyst, reading through thousands and thousands of news articles and podcasts and just paying attention to what's going on in the media ecosystem, it can be overwhelming the amount of information. But when you're really paying attention to it, close attention to it, day by day, you begin to see that there are within the media ecosystem itself like you said, we tend to look at it flat, you know, like what, what tends to be left, leaning what tends to be right, leaning what tends to be covered the most often. And when you're able to have a visual representation of that, it just causes you to be able to view some of these things in a slightly different way. And, yeah, I just, I really love that it is a visual representation so that it gets us thinking about the news that we are consuming.

Speaker 2: 6:18

And that's one of the problems that we're trying to solve is that, like, for instance, me as a consumer, I see articles that say, oh, everyone's talking about this, or there might be a focus in our feeds of particular topics, and the question I have is is everyone actually talking about these things?

Speaker 2: 6:38

To what degree of representation does this story have versus other things, and why is that the focus of the media's attention right now versus any of the other topics that are out there? And so for me, as a consumer, being able to actually see how these things stack up to each other, the relative sizes of them, that's what's important and that's what I need when I consume content, because there have been times where I've seen articles that say like, yeah, everyone's talking about this, this is the big thing right now. And I look and I do searches and there's nothing else out there. These articles are singletons, but I had to do research to do that. I had to spend my time trying to dig up the rest of the story, and it's it's work. It's a string and corkboard exercise that piece together, like how these things actually fit into the greater content space, and it's challenging and that's what we're here to solve.

Speaker 1: 7:30

Yeah, you bring up a very important point in that the things that we are told or the things that we read online are not necessarily an accurate representation of what's really going on, especially if you're getting most of your news through social media representation of what's really going on, especially if you're getting most of your news through social media.

Speaker 1: 7:46

You know, I know so many I can't remember exactly what percentage of people are getting their news off of TikTok or you know, any of the other social media platforms, but it's a pretty high percentage of people that are getting their news from these platforms. And so when you're constantly being fed that this particular topic is not being covered or this particular topic is being covered all the time, that may not necessarily be the case. It really just depends on who is saying this and how often is that particular thing being forced into your feet over and over again. So you could be under the misperception that one topic is being covered a lot when it's not, and then vice versa, like you were just saying, and I don't think a lot of people give that enough consideration before sharing things and before having a comment about certain topics that happen in the news. We get this emotional reaction right about certain topics that happen in the news we get this emotional reaction right.

Speaker 2: 8:46

Yeah, 100%, and even in that just kind of rewinding to something you said earlier on there, like the author, the publisher, who's actually saying, this matters just as much as what they're saying, and that is the context that we need to communicate to people when they consume this information that we want to make easily and readily accessible. But the problem still is, is that, okay, we found this article from somebody. Let's understand who that somebody is. And now it's another exercise of okay, here's a big list of their content. Let's go through and try to piece together and infer the type of actor or person or publisher that this entity is, and it's just still a lot of work.

Speaker 2: 9:27

And so, for the end user, it comes down to either time or trust. Are you going to spend the time researching these people, these publishers, or are you just going to take it at face value? Yeah, this checks out with the rest of the stuff I heard in my feed, so I'm just going to send it full blast. And so what we want to be able to do is say like, not only like okay, here are all the stories, here are all the articles that are talking about this one story. But we can also say like, okay, here are all the articles from this one publisher and just, at a high level, be able to see the footprint of that publisher without even reading any of their articles, just see that, oh, they talk about sports a lot or they talk about politics a lot, and just understand that. That that brief moment, before you even start reading their content.

Speaker 1: 10:15

Yeah, and when one thing you just said about you know figuring out on your own that's a really difficult process, figuring out on your own what's showing up on your feed.

Speaker 1: 10:25

How does this compare to other instances of this topic being talked about on other platforms or in other news organizations on one particular social media platform? And that algorithm has figured out what you like, what you click on, what you tend to spend the most time looking at, and it's going to consistently just try to feed you similar content and then you know it's that confirmation bias. It just completely reinforces it. So if you are only relying on what is showing up in your feed to to confirm you know what you're already thinking about this topic, you really don't even know what your own blind spot is when it concerns that topic. So, with with your mini map, if I were to go out there and look up particular topics, what it would give me would it run the entire gamut of everything for that particular day or that particular week. Take me into how that might help me figure out my own blind spots so that I'm actually perceiving these topics a little more accurately.

Speaker 2: 12:13

Yeah, absolutely. First, I'll start off just by describing how we map content and what that actually looks like, and then for the actual user, for you or anyone who's interested in using Minimap, just what that would feel like to actually use the platform to check their blind spots. So for Minimap, we take a top-down approach when it comes to rendering and materializing the content landscape, and what that means is we start with the high-level topics. We start with sports, politics, weather all of these we have about 50 right now that we categorize all news content into, and those topics are then organized spatially on the map. This might be a good time to actually show what that looks like for any of the viewers who are watching this podcast.

Speaker 1: 13:00

Yes, great idea. So I know the vast majority of you listeners like to listen to this on the audio format, but if you do have an opportunity, go to the YouTube channel and you'll be able to see an example of the map that we're going to show you. We'll do our best to try to verbally for those of you who are listening, though, so we can help you visualize, as we're going through it, what it might look like, and then, later on, you can go to the channel and take a look at it. All right, so we have the map up here on the screen and it looks so fascinating. It really does capture peaks and valleys of these umbrella terms, these umbrella topics that you were talking about with entertainment, real estate, technology. Yeah, so take us through this.

Speaker 2: 13:51

Yeah, and so this is as I started to say. We are organizing the landscape first by the topics, the major buckets and categories that any news article is most likely going to fall under, and so the first concept here that we're dealing with is that we want to be able to maintain continuity, a continuum between any topics, such that if you were to move between energy and space, space and sciences, food and entertainment, the articles that you see as you move from one place on the map to another makes sense, and that there's a gradual step between each of these places, and so that just makes the content more interpretable as you explore the map. So that just makes the content more interpretable as you explore the map. Secondly, by doing this, we're able to immediately see trends within the new space. For instance, right now we can see that weather and sports are the two most reported on things, and that makes sense. We're going to have daily weather reports, hourly weather reports, weather reports for all pockets of the nation and beyond, and so weather is going to always be a dominant category. But then, second to that is sports, and so this just shows us that we're able to understand the scale of these two topic spaces. And for me, just browsing the news, just being active in the news space, this makes sense and it matches what my understanding was of these two big buckets of content. And then for the rest of them, we can see just how big technology news and internet news is, relative to finance or business and all of these other spaces where news is reported on, and so this is just at a high level, how the content landscape looks and feels, and so we can see these peaks and valleys and see where there are concentrations, like the other week there or the other month now there were some.

Speaker 2: 15:47

The California primaries were rolling in, votes were coming out, and so we saw some interesting news in the content landscape. We actually made a little video about it where, for politics, we saw these two sharp spikes. It wasn't even like a mound, like we saw with sports. There were these two sharp spikes that were reporting out the results from each of the districts, and those actually came from two publishers that had a auto-generated report for each of the precincts, and so that's the type of pattern that we're able to immediately identify.

Speaker 2: 16:21

We don't need metrics, we don't need chat summaries, we just see that there are these big spikes that are sticking out of the landscape that match some movement, a bleeding edge almost, of what's happening in the news space, and so that's the content landscape. And so then, for a user, what they're able to do is we're able to show news stories, as I said earlier, like the footprint of news stories. So, like here, this story is Tesla receives their tentative full self-driving approval in China, and so what we're able to do is show all of the articles that are talking about this one story and all the topics that they fall into, and so this is transportation, this is energy, this is technology and AI and business and government, and these are all of the categories is technology and AI and business and government, and these are all of the categories, all of the angles that contribute to this story. And so, like, at a high level, we're able to understand the scope, the range of this, this bigger story that's, that's evolving and taking place right now.

Speaker 1: 17:29

So so let me jump in, you know, for anybody who's listening, the interesting thing about this is so you have, you know, an article about Tesla and, from a news consumer's perspective, when you think about Tesla, regardless of how you feel about the organization, you know the business, regardless of how you feel about Elon Musk, any of our opinions the interesting thing here, like you just said, that article on Tesla, when we pull it out and we look at all of these other topics that are connected to that, there are all of these different angles that these articles are covering, still talking about Tesla, but it could be from the technology perspective, it could be from the finance perspective or, you know, maybe government legislation, you know, perspective. So that's that's really interesting someone who is interested, you know, in this particular topic, to be able to just become better informed about Tesla from all of these different angles. I think it could really help broaden your perspective of the things that are said about this one particular topic.

Speaker 2: 18:49

I love it.

Speaker 1: 18:52

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Speaker 2: 19:48

Absolutely, and just like how Ground News focuses on breaking stories, developing stories into right-leaning, left-leaning, and they're more focused on the polarization of news, we're focused on the high-level content or topics and organization of not just news but content in general. A little background about Minimap is news wasn't actually our first application. We started with Amazon's catalog as the first data source that we were playing with, but unfortunately it was expensive to access their data. News was readily accessible. We felt that was impact. We're able to. By just being able to organize content this way, it does help our users understand and navigate these, these larger content spaces.

Speaker 1: 20:38

Yeah, it's so awesome to see how the map just fills in, you know as you look out.

Speaker 2: 20:48

It was a lot of fun learning how to write the front end. I got good at shaders, if you know what those are, but I'm very thankful that ChatGPT is here. But another interesting aspect of this and this is what I was talking about earlier as a demonstration is we were saying that not only do the articles about content matter, but also the publisher of those articles, and so one of the things that we can do is, for instance, here this is the landmark, the logo for us Us Weekly. They're a news publisher, they talk about entertainment, people, et cetera, and so what you're seeing here is this is Us Weekly, placed as the algorithm understands them, and so that means that they're in people, they're over in this area.

Speaker 2: 21:40

We had nothing to do with where it lands. That's all driven by how the algorithm understands them in the context of the content landscape. But what, then, our users can do is like okay, I want to know more about this publisher and we can highlight all of their articles on the map for whatever time range that we're looking at. So, like here, we're looking at a 30-day window that we're looking at, and so, like here, we're looking at a 30 day window and we can see that they do post or they do create content that is people focused, that is, fashion and music and entertainment focused media, and they tend to stay out of the legal and political scenes. They don't tend to report on finance and business, and so they are. We can see at a glance, without even looking at a single article of theirs, that they are consistent with their branding, their messaging and the type of content that they say that they report on.

Speaker 1: 22:32

Yeah, this I really, I really like that you're bringing up this example. Just this morning I saw Gallup had released, you know, talking about topics that immigration, of course, naturally, you know immigration remains a top concern of voters. But then follow up question as someone who likes to follow the news and you know I'm very interested in the news landscape and how things are covered One of the conversations that I have with other news consumers is how important topics are covered in different or by different news organizations and whether or not is it covered more often by certain news organizations than others. There is an argument to be made when you talk about bias in the news, bias by omission. You know some news organizations that might be right-leaning or left-leaning might have a tendency to report on particular topics more often than others.

Speaker 1: 23:36

You know, and you had mentioned ground news earlier you know that's one of the things that they like to show, you know, in their information, what's considered to be a blind spot be a blind spot on the right versus a blind spot on the left. And so if you were to bring up the topic of immigration, I would be just because this is at the top of my mind seeing that article from Gallup this morning. I would be interested in seeing from this map you know, visually how that would span out across the. You know the different areas here and, like you said, it doesn't show left leaning or right leaning, but from a topical perspective, you know, it is interesting to see where all of the different dots that you have here you know falls under government politics. Regional news.

Speaker 2: 24:35

Yeah, absolutely, and so here we just search for immigration.

Speaker 2: 24:41

This is a search that's done just by matching, it's not a keyword search.

Speaker 2: 24:47

This is anything that is immigration or immigration similar, and so we can see all the areas where immigration pops up and seems to be a pretty ubiquitous or universal concept across all major categories, across all major categories, and so we can see it, of course, in politics and government and transportation and world news, but it appears to be coming up in unexpected places as well, like real estate and technology and entertainment and media.

Speaker 2: 25:16

Even sports has a couple of hits for immigration, and so this is now we're able to see not just the major or dominant topics that we'd, of course, expect this conversation to come up in, but the unexpected places as well, such as sports, and so what I'd like to do here is do a refined keyword search to actually bring up the articles that are specifically talking about immigration, and then we can see that this drops down to 77 articles in the last 30 days that we've been able to scrape, and, of course, we're still seeing a wide coverage, and we're even seeing hits out in natural disaster, environment and climate change, and so that might be immigration.

Speaker 2: 26:01

These could be stories, or likely are, that are focused on displacement because of climate change issues or natural disaster, and so that's. Unfortunately, I lost my search, but that's what we're trying to show is that there are, of course, the dominant topics and categories that we expect this news to fall into, but there are blind spots that are going to be difficult for people to reach, to see and understand if they were to only look at these search results in a flat list or as summarized by a chat GPT like algorithm.

Speaker 1: 26:41

Right, yeah, that's so fascinating to be able to look at this through this particular lens. You know, thinking about US immigration, and then you know just some of the news articles that are news organizations that I tend to pay attention to and how they cover US immigration. You know, it's interesting to be able to drill down into this map, you know, yeah, with the politics, and figure out who's reporting on this from that particular aim or crime. Or you know, like you just showed climate change, which news organizations are covering this particular topic from that angle. You know. So, for anyone who is really interested in doing a deep dive and learning more about this aspect of the news, and not only what's being covered but who's covering it and the angle you know at which they're approaching these particular topics, it's fascinating to be able to do this.

Speaker 2: 27:40

Yeah, it's been rewarding. This has been a dream or vision of mine for a long time, and after my last job, I was in a position where I needed to have a crypto firm. I came in being able to build data pipelines. I could do the technical work. That wasn't a problem. But crypto was, and still is, volatile. It changes fast and if you're in the crypto space, especially if you're a decision maker, you have to be able to react and understand the news around you.

Speaker 2: 28:26

And, as almost a foreigner to the space, I was not subscribed to the right people. I didn't know who to follow, and all crypto news broke on Twitter first. It did. That's where crypto was. It was on Twitter, it was on X and without being able to use X's search, because it's bad, it's really bad. Earlier today I found somebody on X. I was just trying to figure out who they are. I looked at their account and it is difficult to even look at the list of tweets that a single person put out there, because it's conflated with ads. And here are some other people and here are all their retweets. It's like no, just show me what they've actually written. And so just being unable to get the information I needed to understand the crypto landscape. I felt like I was a liability and this had been something I had been thinking about long before I even started that company, and after that, I felt it was time, and so I took the leap and started this and started building out Minimap and just to take it a step further for prospective users and anyone that's interested, one of the things that I wanted this platform to be is an actual mini-map for users, and what that means for us is that I want to continue browsing Reddit or Twitter or any of my news feeds.

Speaker 2: 29:53

As you said earlier in the podcast, a lot of people get their news information from social media, and I don't think that's going to change. I don't think that readers are going to start moving over or new content. Consumers are going to start moving over to the traditional news websites New York Reddit, it's YouTube Shorts, where people hear these news events and get these snippets of information, and so what we built out is actually a browser extension that tags along with the user, and so if we do go to AP News, there's a ton of content here, and so right now I have the browser extension plugged in and it's saying to me that there are 117 articles on their front page. That is overwhelming. That's a lot of information. Yeah, I'm not going to. I can't handle that. That's too much for me. And so what Minimap does for the user is we have this browser extension, drive by extension, and we can actually show what it looks, what a user's newsfeed looks like, and so on one window I have AP News.

Speaker 2: 31:23

Doing my browsing as usual, and on the map I have all of those articles highlighted. I can see, oh sorry, it looks like there's a bug. These things are supposed to hide away, but on the map I can see each of those articles highlighted and I can see where there are concentrations, if any, and where there's a sparsity, if there's no coverage. And it looks like on AP News' website. Wow, here we go. There is a gap in coverage for energy and transportation. I know what these mounds are because I spend every day with the map, but these areas it's a huge blank spot, exactly. And so they're not talking about Boeing in the news, even though I know Boeing's Starliner is starting to ramp up and they have all the whistleblower coverage. That's right.

Speaker 2: 32:13

They don't have any conversations out in the energy sector. I know that there's a lot of new battery tech happening right now. People are trying to find like longer term, you know, zero waste batteries, and so this is telling me just at a glimpse. I'm not even reading a single article. What? Oh, even here's another one. Natural disaster is missing on the map too, which might be a good thing, you know, that's actually probably a great thing if there's no natural disasters to report.

Speaker 1: 32:41

Right. Maybe there's just not something going on right now, but I'm so happy that you pulled this up and you're using this as an example. You know, looking at Associated Press News, the website, and then using your extension, and it is giving you an indication of all out of all of those articles that are on the front page right now, you know there's important news that's happening that that we're aware of. Just from reading other news organizations, you know information that they're they're publishing. But then here you can see that there there is a big, big area looks like a desert covering that information, and we're not saying it's good or that it's bad, but it is notable that it's missing off of the front page, and so does this extension. It only scours that particular page that you're on at that time, right.

Speaker 2: 33:36

Yeah, so we have a small list of websites that the extension is allowed and approved to work on, and so that's us telling the Chrome store and the browsers like only these sites.

Speaker 2: 33:49

And then when we visit, when the Chrome extension sees or the browser extension sees, oh okay, we're on AP news or we're on Reddit.

Speaker 2: 33:59

If you try it on Reddit, it's only oldreddit, not with their new layout, but AP News, cnn Reddit then it will activate, it will look at all of the content that's on the page in front of the user and then it will send that content over to the map to be understood by the algorithm and then plotted and mapped out for the user and mapped out for the user. And then in that we're able to still highlight and navigate the content landscape as we would if this was just the bare or native content landscape or if we were just on Associated Press. Because again, here's the smaller, refined vertical feed of news and I can from there go right back to the page on Associated Press. We're not looking to host content ourselves. We merely just want to be a, not a dictionary, but almost like yellow pages, we want to be able to say like here, here's where all the content is that you're looking for. Here's where it lives, and let us get you there and organize that content spatially for you.

Speaker 1: 35:07

Yeah, yeah, and I really like the idea of who's talking about this today.

Speaker 2: 35:13

Exactly yeah.

Speaker 1: 35:14

Yeah, that's so fascinating. So you really didn't have until you worked for that crypto company and you were finding that there was information that you needed to be able to access and you were having a difficult time being able to access that. That gave you the idea to create this, this mini map. So have you been doing this on your own? Do you have, do you have, people who are helping you? Like how this seems like it's an incredible amount of work. To be honest with you, I don't know anything. I don't know jack about coding, okay, but I can imagine this looks like hella work.

Speaker 2: 35:51

So I've had help. I left the company, the crypto company DeFi Pulse, back in December of 2022. And I've been working on Minimap ever since. Literally the next day after I left, I started working on Minimap, and at first it was just me for several months and then an old coworker of mine actually a former intern joined me in, I think, july of 2023. And so I'd been working on this for six months, just going through and getting the. Actually, if you give me a quick second, I have a fun picture book that just shows the evolution of Minimap.

Speaker 1: 36:41

That's just I love it. I love it that you have a picture book.

Speaker 2: 36:47

You know it's a, a it's a visual thing. We're here to visually communicate things, and so I feel like it is fun that this is a visual representation of of the news landscape.

Speaker 1: 36:57

I think that's why I get so excited about it. I'm a bit of a nerd that way, but fellow nerd nerds, if you want to check it out, I highly recommend it. Oh, here you go. Okay, I see you, yeah.

Speaker 2: 37:10

So this is the progress, our timeline In December of 2022,. I founded Minimap, I incorporated, created the LLC, all of that and more, and the original idea was I wanted to be able to map out search results from any search bar anywhere where there's a search bar on the internet and our algorithm can understand that content. That's that's the space that we want to be, especially for, like amazon's catalog, which is overrun with drop sellers, so there are so many duplicate products out there, and so for us, what that would look like on the landscape is like that example earlier, where those two publishers were making the identical reports for every single precinct in California and we saw those spikes. That's what Amazon's catalog would look like on the map, where all of those drop sellers would have hundreds of identical products. We don't care how many items are in the search results. What we care about is how many unique items there are, and so, deduplicating those spatially and just stacking all the similar things up, we wanted to be able to show the full space of search results for users.

Speaker 2: 38:19

And so here, like I'm looking at this one sneaker as an example and I just want to be able to highlight where this is, and so this was the initial idea and this image I made by hand. This isn't, and so the first it's not to scale. Is that what you're saying? Yeah, it's not to scale, made by hand, no algorithms involved. This is the concept.

Speaker 2: 38:40

And so the first place to start is let's just start with two things. And so the first idea was okay, let's see if we can't make a spectrum between boots and sneakers and have the algorithm organize things as a gradient between those two, and so, on the right, here we have sneakers. On the left, here we have boots. And then the idea was okay, well, can we have somebody, be it Skechers or Keen Boots, define those polls, those endpoints, and just that could be the business model. And so, like, okay, brand you can represent, like this poll and other brand, that poll, and then we can organize any and every piece of content relative to those two things. And so that was where we started. And then this here is that first implementation, and so it's a diamond, for reasons that doesn't really matter. What does matter is that as you go from left to right, it goes from most boot-like boot to most sneaker-like sneaker, and at the very top is the most boot-like, most sneaker-like thing.

Speaker 1: 39:43

That's a nice combination of the two.

Speaker 2: 39:47

The maximal combination and at the bottom we had the minimal combination. Like these aren't boots, these aren't sneakers, these are cars and airplanes.

Speaker 1: 39:55

Yeah, and then it's like what are those doing?

Speaker 2: 39:57

Yeah, and over here we have a car sneaker, but the algorithm saw that, hey, this is substantially sneaker, so we're going to that belongs over there and in some capacity it's right. And so this is showing us that we can organize things onto a spectrum, a gradient between two understood topics, and also distill a semblance of relevancy where we, if you're sticking to this top most border. These are going to be the most relevant boots or sneakers, or boot sneakers, whatever you want to make the spectrum out of. And so this was the first success that we had in February, two months after I started experimenting with the algorithm. Wow, that's amazing. And so then in March, we turned this into a full 3D thing, and so now it's like, okay, we know how the math works for the 2D scenario, let's make this 3D so we can define these topics and then organize several spectrums at once. And so what we're looking at here is these diamonds. Upside down, diamonds represent the topics. These are the boots, these are the sneakers, these are anything that we thought was relevant for defining the content landscape at a high level, and then everything else, these peaks and valleys and little spikes. That's the content, and this is with Amazon's product catalog, and then, of course, you can see the logos, and so these are the seller logos, and they are placed based off of the algorithm's understanding of either the seller themselves or the content that they're selling. And so behind logo, there's a Barbie thing over there. There's in the far top left corner, there's mighty skins. They do like skins for your drones or your like unibords, all that kind of stuff. But this was this was our first success with 3d mapping.

Speaker 2: 42:01

However, the math needed to be improved, and so we started experimenting. So I started experimenting with different approaches, some weird spirals and donuts when trying to organize these high dimensional point clouds of information figured out. I figured out how to actually make this real, how to meaningfully organize the content around these topics, and so I can see that there's a landmark behind that valley over there. But that's, this is. This is the first success, and this is now the algorithm that we're running with today. And so then, afterwards, I had to move on from Amazon. As I said earlier, they're expensive, they're difficult, they'd only give back 1,000 search results and it would take 10 minutes. They limit us any of their vendors, through their regular APIs, to two requests a second, and if they only give 1,000 requests or results in their searches, it would take 10 minutes to act for us to actually get a user's real time search, and so that wasn't going to be tenable for us.

Speaker 2: 43:13

And so what you're looking at here is the social media platform, blue sky, and so now we've we, I turned to blue sky to actually create content maps, and so what you're looking at here on the right or, sorry, on the left is the major topics by the counts of documents and then some relevancy scores. This was my proof that I could search for crypto content and not just highlight all of the content that's on crypto island, but all of the content that's off of crypto island, because there is content that is financial or not financial. Let's space, let's say space focused, and it's like we figured out how to use blockchain to do a thing on the ISS, space, space, space, and so that's a space dominant topic, but that's crypto over in space, and so you otherwise probably would have never been able to see that exactly discovered it, and so, like now we can.

Speaker 2: 44:16

I can see all of the places that crypto content is popping up throughout the content landscape that's outside of a crypto dominant context, and so this was a big success. And then this is the time where one of our other software engineers came on the platform. We started experimenting with highlighting by date, highlighting by sentiment analysis, refining the actual UI, and so now it's getting a little bit more sophisticated. Refining the actual UI, and so now it's getting a little bit more sophisticated. We're bringing on landmarks from from companies on Twitter. So what I did here is I went through and collected 20 companies and took their top tweets and gave them to the algorithm, and now all of these logos that you're seeing are organized by how the algorithm understands their posts.

Speaker 2: 45:07

And one of the really interesting findings here is NVIDIA. I thought we had a bug. I thought that NVIDIA is a tech-focused company. They're AI-focused, they are huge in the tech space and so unfortunately, I didn't capture the topic that this little island represents. But this is corporate and corporate news, corporate-focused stuff, business, also adjacent to influencers and social media, and I was like why is NVIDIA over in the corporate space? Why is NVIDIA? Why aren't they over in tech or AI?

Speaker 2: 45:46

And it turned out it's because their top 10 tweets actually to this day on LinkedIn, on Twitter, across all of their socials nine out of 10 of their posts are about their CEO or their executive teams or their leadership and what they're doing to further NVIDIA as a company, and so they are talking about themselves at a corporate or executive level always, and so, as far as the algorithm understands it, they're not a tech company, they're a enterprise company. They're a social media influencer. They're a enterprise company, they're a social media influencer. They talk about themselves versus the tech that they make, and so this is an example of a misalignment between what the company actually does and how they portray themselves out on the social landscape.

Speaker 1: 46:34

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Speaker 2: 47:35

Yeah. So right now, as I showed, we are able to highlight different companies out on the social media scape and whether or not there's brand misalignment. That's still pretty difficult. What we have to do is then read like, let's go back to Us Weekly here. Oh, are we going to get it? It should be loading. And so what it really means is that just understanding the you know what they're actually talking about versus where the algorithm is placing them. And so right now, we're placing them based off the content that they produce, and so what we could do to actually highlight this is there is a description that's going to load shortly about what Us Weekly is, and then there's all the content that we have, and we can look at the difference between how the algorithm understands their description versus how the algorithm understands the content that they produced, and that's how we could identify a misalignment or incongruency between the brand and their content.

Speaker 1: 48:40

Right. So if I'm an organization, I claim to focus my content on sports, but then I look on the map here and I see, wow, there is an awful lot of information they're producing on politics. That could be an indication of a misalignment there.

Speaker 2: 48:59

Yeah, that actually could be a very fun layer that we could add to the map to show where these types of misalignments are happening. Yeah, it would be a fun side project for us.

Speaker 1: 49:13

I'm good for that. I'm good for for side projects. Hey, can you? You know it'd be cool.

Speaker 2: 49:20

Yeah, but it's it's, it's real, it's impactful and there are a lot of brands out there, Like, for instance, Wendy's food company, but the way they and I'll scroll- back here.

Speaker 1: 49:31

Yeah, I mean they're. They're so famous for their, their tweets.

Speaker 2: 49:34

Exactly, and so you can see out in the distance. Here there's five guys just towards the top of the screen, but Wendy's isn't there, but five guys in Wendy's burger chains fast food.

Speaker 1: 49:45

So why?

Speaker 2: 49:45

aren't they next to each other? And that's because Wendy's portrays themselves as more of a meme-y social media account. They don't really push their burgers as much as they push just the idea of Wendy's and self-promotion, and that kind of stuff, and so it even highlights the difference in media strategies that these companies are taking.

Speaker 2: 50:05

And so for users, again, this gets back to being able to understand these large entities that have vast amounts of content at a glance. And there's no way that you can do that with any number of metrics or chat descriptions. It just doesn't work. I like to give the example of like I could draw a squiggly circle and I could try to describe to you that circle in as many metrics as I want, and there is no way that you could recreate that circle.

Speaker 2: 50:35

It just it doesn't work that way, and so how do we expect to understand the dynamics of the content landscape when all we have are metrics, chat descriptions or vertical scrolls? And so that's why I think we have like something's going to give, it's going to. Things are going to change at some point, whether it's by our hand or somebody else, but just to continue on. You know, we kept evolving, we kept iterating, like here's just showing how similar stories group together and occupy like very close, like proximity on the map, and then in September and October that's when we released our beta the landscape looks very different. Now we had to change because we had to make it easier to like see things.

Speaker 2: 51:22

When you move your application from the 2D space to a 3D space, everything goes out the window in terms of UI design and color dynamics and all of that, and so it's a tough challenge. I was not prepared for the level of color science needed to think the light and yeah, and so we just kept iterating. We filed patents for the mapping algorithm that we have, and that brings us to today with the content landscape as you see it.

Speaker 1: 51:52

Yeah, that's so fascinating. So where do you see this going next?

Speaker 2: 51:58

We are trying to raise on Kickstarter right now to get ourselves to market and in front of users. What we want to be is a medium between users who need to understand the content landscape. We're at a point where that's just a must now, because brands have big tools for this kind of stuff. There's Muckrak, there's Cision, there's all of these social listening tools that can break down the content landscape into any number of metrics and nuancy, descriptions and chat. But it's that's that's still like a problem that we're trying to solve. Like again, I don't think that we can understand the content landscape through just metrics and descriptions, but the problem is these social companies. They have the tools needed to understand the landscape as it's relevant to them, but their understanding of the content landscape is not the same as content consumers.

Speaker 2: 53:01

Content consumers are in the dark, and so we first want to be a tool for the content consumer just to get their bearings on what's going on in the world around them, to be able to take their random piece of social content that they discovered on reddit or whatever platform and, okay, show me everything else that's like this, yeah. And then, second, we want to be a medium that allows brands to represent themselves brands, communities, anyone, those landmarks. Now there's an opportunity for brands to represent themselves in. Now there's an opportunity for brands to represent themselves in contextually, in the spaces that are relevant to the content that they produce, to the spaces that that consumers are looking for them. And so it's either you know you see walmart and then you see walmart on the map, you're gonna be like okay.

Speaker 2: 53:50

Or starbucks food you see their logo on the map, be like okay, I, I kind of know what this is because I know what starbucks is and so I have a sense of what I'm going to find next.

Speaker 2: 53:59

Or vice versa the user is in food, the user sees a logo that they've never seen before, but that logo is in food and it's next to starbucks and it's like okay, this is probably going to be a coffee shop and and so, just like Google Maps allows users to look at a city at a high level and see logos and landmarks and the little spoon and fork icons or museum icons and understand like, oh, okay, food districts, entertainment district, all of these things. We want to be able to give users the ability to contextualize content, landscape in that way and give brands the ability to express themselves and show themselves to users in the same view and just have a unified view. Companies and massive brands view themselves, and how users understand the world around them is vast. There's nothing out there that's attempting to bridge that gap or even cater to the basic needs of users with these problems that we talked about today.

Speaker 1: 55:10

Yeah, that's fantastic, just from a brand awareness, brand management perspective, any organization, it doesn't matter the industry that you're in, I mean, you are a brand, so being able to have a mechanism in place to try to figure out, okay, your consumers do they have a perception of you that matches what you hope it is? You know one thing that I say a lot you know as a communication person is the message you send may not necessarily be the message received, and I think this is a perfect example of that. If you are a brand, whatever your, you know whatever organization, wherever you are, whatever it is that you're selling, or you know the content that you're producing, it's really important to be able to keep that in mind. Is the message we're sending, the message that's being received? And if there's a misalignment there, we need to be aware of that so that we can figure out what changes we might need to make. You know in our messaging and how we're getting it out, where we're getting it out. You know, yeah, that's fantastic.

Speaker 2: 56:15

Yeah, absolutely. I firmly believe that there is. There's a lot to be gained from just a shared understanding of the content landscape and then from there just building out.

Speaker 1: 56:28

Yeah, absolutely so, if anyone is interested in playing around with the map or they want to get in contact with you or they want to help in some way, because I know that you have a Kickstarter campaign going on right now, so give us, give us, all the information that we need for that.

Speaker 2: 56:48

Absolutely. We're looking for feedback. We're trying to get users on the platform to understand how they engage and what their first impressions and experience are with the map. They're the first contact and so getting feedback. Join our Discord, give us that feedback and, if you like what you see and you get the vision and you understand where we're trying to go with this, because we want to be multi-domain, multi-modal in the sense that it's not just news, but news, social media, e-commerce, anything that the algorithm is capable of understanding. That's what we want to be able to put onto a map. My goal. It's nowhere within reach right now, but I'd love to have a map like this of music. I'd like to take a snippet of a song that just I really jam to like. Sometimes they're just these small 10 seconds. I'm like that. Find everything with that vibe and give it to me.

Speaker 1: 57:39

That would be fun, that would be great yeah.

Speaker 2: 57:41

Yeah, exactly, and that's where we want to go with, again, anything that the algorithm is capable of understanding. And so right now we have our proof of concept, our minimal viable product out there that demonstrates the concepts at play here, and what we need is to get the funding to bring this to market, to get stable revenue and get to the point where we can expand the algorithm to our algorithms to encompass social media, e-commerce and even images and video things that are out of domain of just text, but multi-domain, multimodal and, yeah, maybe even at a point where we can do music and audio. But that's far in the future, and so right now we're just looking to get funded to get this crowdsource campaign off the ground.

Speaker 1: 58:32

Okay, yeah, so how long is that campaign going to last?

Speaker 2: 58:35

So we have 16 more days of our campaign. All right, we need 23rd.

Speaker 1: 58:39

So got to get cracking on that.

Speaker 2: 58:42

Oh, I know, yep, I'm watching the countdown.

Speaker 1: 58:46

Yeah Well, how can we get in touch with you, any listeners who might want to contact you directly? You mentioned Discord. Are you on any other social media platforms?

Speaker 2: 58:55

You mentioned Discord. Are you on any other social media platforms? I'm on Discord and LinkedIn. I don't go on Twitter too much, it's just I have not enjoyed their experience, for obvious reasons, and so yeah, and if anyone wants to contact me directly, it's just Clayton at Minimapai and I always read my emails, and so you know I'd love to.

Speaker 1: 59:17

I'd love to have a conversation. Yeah, that's fantastic, and I'll make sure to have the link to the website in the show notes as well. Well, my friend, it has been exciting seeing this journey since the first time you and I had a conversation talking about the media landscape and you introduced what it was that you were working on, and just to see some of the changes even between then and now, I'm super excited to see where this is going. So we're definitely going to have to have another conversation in the future so that we can think back to this moment in time and do the whole remember when kind of thing, kind of you know conversation, because I think it's fantastic.

Speaker 1: 59:55

So many applications and just from a consumer perspective, a news consumer perspective I think it is filling an important need that exists out there, and the fact that you're coming at it from a visual perspective, I think it provides something that all of the other metrics cannot provide at the moment, and so I think more information is always better. So I just appreciate what you're doing and I appreciate that you're out there making sure that we can get as informed as possible, because it's so critical. We need to have a society, we need to have a community that is as well informed as possible. So yeah, yeah.

Speaker 2: 1:00:43

Thank you for doing this. No, thank you. I appreciate that it's motivating.

Speaker 1: 1:00:48

Oh yeah, I'm a fan, so for the listeners more to come, make sure that you join this Kickstarter campaign. Let's make sure that the this keeps growing and the usability, I think when you get out there and you start playing around in there and, like Clayton said, they're looking for people to go to the website and and give your feedback. So that's really important. Be a part of it now. How exciting is that you can?

Speaker 2: 1:01:13

be a part of it and Join us day one. It's exciting, it really is. Thank you, jen, I appreciate it.

Speaker 1: 1:01:21

Absolutely All right. You have a great rest of your day and, listeners, I'll have a great rest of your day as well, and we'll see you next time. Take care now. Thanks for listening. If you enjoyed this episode and you'd like to help support the podcast, please share it with others, post about it on social media or leave a rating and a review.