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Introducing GPT-4o Mini
OpenAI has introduced a revolution in the generative AI world - "GPT-4o Mini." This model is a significant advancement in using AI applications for business, improving on its ability to resist jailbreaks, prompt injections, and system prompt extractions through OpenAI's innovative instruction hierarchy method. GPT-4o Mini presently supports text and vision and anticipates future support for text, image, video, and audio inputs and outputs, making it highly adaptable in a variety of business contexts.
Impact on the Cost and Output Balance in AI use
One of the key features of GPT-4o Mini is its cost-effectiveness. As compared to the price per million tokens of its predecessors and competitors, such as Claude Haiku and GPT-4o, GPT-4o Mini is significantly cheaper, stirring a promising shift in the cost and output balance. With an 82% MMLU (massive multitask language understanding test) score, it has outperformed the GPT-3.5 Turbo, enhancing both cost-effectiveness.
GPT-4o Mini Performance Comparison
A comparative analysis was done with the GPT-4o model, using prompts to evaluate speed, quality, and reasoning of both models. Notably, the GPT-4o Mini model demonstrated its strength in detailed solution provision by formulating a future smart home company and brand. However, the GPT-4o model proved faster and more accurate in some cases, including pattern recognisation in fruit quantity problems. Both models correctly calculated drying times for a T-shirt task demonstrating the potential in practical problem solving.
Capabilities for Future Use
Primarily accessible via the front end of ChatGPT, GPT-4o Mini offers support for text and vision in the API making it even more suited for developers. Its capabilities extend to devising specific strategies based on the target audience channels and arranging promotional events. However, several aspects of vision capability need improvement as demonstrated by problems in analyzing traffic, architecture, and weather from a photograph. Yet, it is expected that future improvements will refine such shortcomings.
Opportunities and Implications
The growing quality in AI models like GPT-4o Mini is leading to a shift in cost comparison between human labor and model deployment and maintenance. This increasing affordability and efficiency are expected to lead to more and better AI applications across various industries. Although concerns are raised about the increase in energy demand for running these models, it is important to weigh up the environmental implications and further technological advancements that could manage such challenges.
Conclusion
GPT-4o Mini's introduction marks a growth in AI enhancements with direct implications for businesses and consumers regarding cost reduction and increased competition. Companies developing chips for AI applications, such as NVIDIA, AMD, and Qualcomm, are also likely to benefit from the increased demand. Looking forward, GPT-4o Mini is set to transform the generative AI world by providing more effective, affordable, and advanced AI solutions to businesses.
Topics Covered in This Episode
1. Introduction to GPT-4o Mini
2. Impact of GPT-4o Mini on AI Industry
3. Comparisons Between AI Models
4. Accessing and Usage Implications of GPT-4o Mini
Podcast Transcript
AI [00:00:00]:
This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life.
Jordan Wilson [00:00:16]:
ChatGPT maker OpenAI just released a new model. And no, it's not GPT 5. It's actually called GPT-4o Mini. But don't think small because I think this is actually going to make a huge difference. Not just on the surface, because it looks like just a light version of OpenAI's latest model, but it's more than that. There's a lot more beneath the surface from a competitive standpoint, and I think this is going to greatly impact how we work, not just in a many way. So we're gonna be going over that, doing some live testing, and a lot more here on everyday AI. What's going on y'all? My name is Jordan Wilson, and Everyday AI, it's for you.
Jordan Wilson [00:01:05]:
It's a daily livestream podcast and free daily newsletter, helping us all learn and leverage generative AI. And this is whether you know it or not, you are going to be leveraging, GPT-4o Mini a lot in the very near future even if you don't know it. Alright? So, if you haven't already, this is one of those you gotta make sure you go to your everydayai.com. Sign up for the free daily newsletter. Check out your show notes if you're listening on the podcast. We always keep all that information in there to quickly get the newsletter that goes along with today's episode, as well as if you want to reach out, connect with me on LinkedIn, send an email, whatever you wanna do. For our livestream audience, thank you for tuning in. This is technically kind of prerecorded just by a couple hours.
Jordan Wilson [00:01:48]:
I'm gonna be on an airplane at our normal, time. So, hope you can bear with me, but I'll be in the comments after. Tell me what you think of this new model. And for that reason, make sure if you're looking for your daily deuce daily dose of AI news, make sure to check out the newsletter. Also, before we get started, yeah, one quick fun announcement. Tuesday, this coming up Tuesday, couple days, mark your calendars. That's all I'm gonna say. A $1,000 live challenge.
Jordan Wilson [00:02:17]:
If you can get every single answer right in real time, there's gonna be a $1,000 prize. Whether that's one person, if 3 people split it, they split that $1,000 prize. If you can get every single question right on ChatGPT and other large language models yeah. Yeah. We're gonna do it. We're launching a little campaign called Thanks A 1000000. So this is a little, fun event to, celebrate that launch. So make sure Tuesday, July 23rd.
Jordan Wilson [00:02:46]:
If you're normally a podcast listener, Tuesday, July 23rd, 7:30 AM Central Standard Time. We'll have the link in the show notes as well. Enough with that. Let's get into the new model. So OpenAI just hours ago released their newest model called GPT-4o Mini. So this is technically a, quote, unquote, mini or lightweight version of their most powerful, and I would say, well, not me. All tests, all benchmarks say the world's most powerful large language model right now is GPT 4. Oh, so this is the light version.
Jordan Wilson [00:03:22]:
Right? The the featherweight. Right? The the cheaper and more flexible and faster version of the world's most powerful model. So there are so many business use cases and so many ways that we're gonna be using this in the near future. Alright. So we're gonna be going over, all of that. But, also, another shout out to the newsletter. This didn't come as a surprise to me or if you listen to this podcast and read our newsletter every day. I literally told you Monday about this model, GPT GPT Mini.
Jordan Wilson [00:03:51]:
Gonna be coming soon, so make sure you read the newsletter. A lot of times, y'all, I'm a former journalist. I spend so much time researching each and every episode, even just this episode right now. I spent about 2 and a half, 3 hours reading everything, testing the model out, to bring you the latest, the greatest, and the truth. The realest thing in artificial intelligence, I'd say. Say. You know, the show's unedited, unscripted. Alright.
Jordan Wilson [00:04:14]:
So let's talk about first big picture. Let's zoom out and talk about what this means. Well, there's a lot of business use case, which we're going to get into, but I even wanna talk about in the competitive landscape. Right? Because things have changed a lot, over the last couple of months. I would say notably, Anthropic has been making some power moves. So Anthropic just released their, Claude 3.5 Sonnet. Okay? So Claude has 3 flavors, the smallest and fastest and cheapest haiku, the middle version Sonnet, and the most powerful version Opus. Well, that was until they released 3.5.
Jordan Wilson [00:04:51]:
Middle model, Sonnet. So they don't have 3.5 for the small one, and they don't have 3.5 for the big one. And we gave you a lot of in-depth insights in our newsletter on why. That's why you gotta read it. Alright. So, anyways, also, we saw Google just also recently come out with their 1.5 Flash. So, again, a small version. So and we've been talking about this on the show as well.
Jordan Wilson [00:05:13]:
I've been saying for a long time, the future of large language models is smaller models. And it seems like everyone else has been playing there. Right? Anthropic has been playing there. Right? With, Haiku, specifically. That's their small fast model. They just announced, last week that you are also now able to fine tune that model, on AWS, Amazon Web Services. Right? So a lot of flexibility being able to fine tune a model like that. We're gonna go into more of what that means.
Jordan Wilson [00:05:43]:
Don't worry. But also the same thing with Google 1.5 Flash just announced, I believe early, I think it was, late late June, early July. So very recently in the last couple of weeks. Everyone else has been making this shift toward small models. OpenAI hasn't until today. So people are only talking about the mini part. Right? They're only talking about, oh, this is just a a smaller version. This is a crappier version of of GPT 4 o, which a lot of people, I don't understand it.
Jordan Wilson [00:06:17]:
Right? If you're a model geek like me, you might appreciate this. Everyone else, you might not, but everyone's making these complaints. Oh, GPT 4 o is worse than the previous version. GPT 4 turbo. No. It's not. People I think people have this unrealistic expectation. Right? Like, when you get something set up for, like, GPT 4 turbo, and then you try to use it exactly as is in GPT 4 0, and it might not work as well.
Jordan Wilson [00:06:40]:
Well, for a reason. Right? You have to, reengineer whatever you built. Right? You can't argue. I don't understand people that argue with math and science and benchmarks. GPT 4 o is the most capable model by far. It's not even close. Look at the benchmarks. Right? Alright.
Jordan Wilson [00:06:56]:
So, anyways, big competition here. And there's a lot of kind of upward mobility for these other companies. Right? Especially Anthropic, When they are going to release a 35 haiku, and a 35 opus, I mean, it's going to go wild. Alright. So I'm gonna try to keep my dorkiness at bay here, but this is why this is ultimately important. Alright. So, I have a screenshot for our podcast audience. So shout out Tom Keldenick, on on the Twitter machine here.
Jordan Wilson [00:07:30]:
He kind of broke all this down for us, to look at the pricing because this is ultimately what it comes down to. This isn't just a a small version, a faster version of GPT 4 o. It is a cost efficient and very capable version. So, up until recently, it was, Claude Haiku that had the, kind of most affordable in terms of power. Alright. So there's there's a quadrant that we will show. I I I should have thrown it up here for the live stream. I can probably find it, you know, if I can multitask.
Jordan Wilson [00:08:09]:
Okay. But, you know, up until recently, I would say it was Haikyu and probably Gemini Flash that had the right kind of balance of, cost and output quality. Right? So when you are looking at a small model, that's ultimately all these businesses, thousands, tens of thousands of businesses are using OpenAI's API. Should have started by explaining this. Right? So many of the of of the systems, of the software, of the tools that you use on a daily basis, you might not even know it because it's happening behind the scenes. The future of just about everything is generative AI, is large language models. And so many of these companies that you use, softwares, that that that you subscribe to, things that social media, everything. Right? They're running behind the scenes on large language models.
Jordan Wilson [00:09:00]:
And what that means in most cases is they're using an API from a company, like Anthropic Cloud, like Google Gemini, like OpenAI. So that's why this cost and output balance is so important because it it, like, it impacts our daily lives, and we don't even understand it. So before today, you know, if we look at input and output. Okay? So I'm not gonna go into tokenization, but think of tokens kind of like words. They're parts of words, parts of phrases. Right? We've gone through I've had a whole episode on tokenization, so I'm not gonna take up your time. Alright. So this is the price per million tokens.
Jordan Wilson [00:09:43]:
Alright? So, 25¢ input for Claude Haiku and a dollar 25 output. Comparatively, now we have GPT-4o Mini. 15¢ on the input versus Claude Haiku's 25¢, cheaper. Alright? Marginally. And then we have the output, 60¢ for a 1000000 tokens versus a dollar 25 haiku. And for comparison, GPT 4 o, the big boy model. Right? Let's do that. $5 per million input.
Jordan Wilson [00:10:22]:
So, again, $5 versus 15¢. And then on the output, $15 for GPT 4 o versus 60¢ for GPT-4o Mini. Alright. So now you can probably, hopefully, understand why that balance is so important. Right? The balance of power and affordability. And and, you you know, it's, we're gonna show you some some examples here of why I think this is striking the right balance. Right? Alright. So let's keep this thing going, and let's go over some of the basics here.
Jordan Wilson [00:11:05]:
Alright. So some of the basics of the model. It's more cost effective. We're gonna talk about benchmarks, but it got a very impressive 82% MMLU score. So, again, this this classification of models. Right? We're not comparing it to Opus. We're not comparing it to, you know, the most powerful models out there. These are the small, large models.
Jordan Wilson [00:11:32]:
Right? I don't know if these are technically considered small language models. I think for that, we'd be looking at, you know, Google Gemini, Nano, and and some of the Mistral models. Right? So these are technically think of them as small, large models. I know that's a little confusing. We also don't know how many parameters it is. However, it scored an 82% on the MMLU score. So we're gonna talk about a little bit more, on those benchmarks here a little bit. But that is an extremely impressive score, for a small, a smaller model.
Jordan Wilson [00:12:03]:
Also, on some, instances on the new leaderboards. Right? Right? So we talk about the chatbot arena leaderboard all the time. It is already out benchmarking GPT 4 Turbo, the previous version before GPT 4 o. That is extremely impressive, especially when you look at the cost differentiation. That's what I'm saying. It's the cost. It's the cost. It's the cost.
Jordan Wilson [00:12:28]:
And what that means, you know, the output too. Right? You have to have that cost output sweet spot. But it is the cost in the output that is going to really change things. Alright. A couple other things we talked about, 15¢ per 1,000,000 input, 60¢ per million output, and it is more than 60% cheaper, than GPT 3.5 Turbo. Also, this is replacing 3.5 turbo. Alright? That's also important to keep in mind because so many applications, developers, companies started to build a backbone for their future business off of GPT 35 turbo. Right? So this is another important thing, to keep in mind here.
Jordan Wilson [00:13:13]:
Alright. So now I'm gonna go ahead. I kind of talked about this sweet spot. Right? So I'm gonna go ahead now, for our livestream audience. I'm gonna go ahead and share my screen. I'll try for our podcast audience to describe this the best I can. Okay. So this is essentially, you know, you have your x axis, your y axis, a little quadrant.
Jordan Wilson [00:13:35]:
Right? So price, the more expensive is on the right, and then quality is down. So you don't wanna be writing down. That means you're expensive and not good quality. So right now, you kind of have GPT 35 Turbo there. You have Command r, some other models. Okay? But you want to be in the upper left hand quadrant. Okay. That means you have the highest quality at the cheapest price.
Jordan Wilson [00:14:02]:
So is GPT-4o Mini the absolute cheapest, when it comes to, you know, price per million input and output? No. Mistral and Llama are a little bit cheaper, but the quality of those is much, much lower. I mean, an MMLU score I don't think people understand an MMLU of 82 is wild for this price. Wild. Right? And I'm getting excited about it because I read these, like, scientific papers, like, on the weekend. I'm a dork, and I've been reading them for years. Right? So even getting something in the eighties, like, a year or 2 ago was was mind blowing, but expensive. The fact that it is this cheap, if you are a business leader, if you are looking to figure out what is your future in a generative AI world, this changes it.
Jordan Wilson [00:14:56]:
This changes it. It's not even close. Alright. So that's kind of an overview of the model. And, again, we talk about this significantly decreasing costs. Alright. So this is a little little graph here, tweeted out by the OpenAI developer account. And it just shows a downward step in the price over time.
Jordan Wilson [00:15:20]:
Right? The fact that in March of 2023, so about, what, 15 months ago, it was $2 for a 1000000 tokens. Now it's 24¢. That's a blended. That's a blend right there. Just just FYI. That's a blend of input output. Going from $2 per 1,000,000 tokens to 24¢. Right? It's a 90% reduction, and the quality is astronomically better.
Jordan Wilson [00:16:00]:
Things are getting cheaper, and they are getting better, but faster than anyone ever could have predicted. Right? The very famous interview with, Bill Gates and, Sam Altman on Bill Gates' podcast, that was one of the biggest takeaways was they were both flabbergasted by how fast the development has come and how how much cheaper, essentially all these models are because it is cheaper to train them as well. Right? That's that's another piece of the puzzle. Right? When we talk about, you know, chip makers, GPU makers like NVIDIA, they're becoming more and more efficient and in turn more and more affordable, which drives everyone's costs down. Alright. Let's keep this thing going. Let's talk about some capabilities and features of the new GPT 4 o model. Alright.
Jordan Wilson [00:16:52]:
So it does support text and vision in the API. It's also, important to note, this is accessible on the front end of ChatGPT. I'm gonna show you all of that here in a minute. So this is accessible via the front end of ChatGPT. If you log in to your ChatGPT account, you can use it. There's I don't think there's a lot of use cases if you're using it on the front end. There's a couple that we'll talk about, but for the most part, if you're just logging into ChatGPT, and using this as a front end user, you're probably not gonna be using this new model a lot. However, if you are a developer, if you're working on the back end, GPT-4o Mini is your new best friend.
Jordan Wilson [00:17:31]:
It is amazing. It is gonna change the way that you can do business. Right? Because essentially and this is just an explanation if you are a beginner here like myself. I'm kind of a beginner, but I'm kind of not. But, you know, for for the everyday person, like, why is Jordan talking so much, about the cost and and and the training and all of this? Well, again, like I said, this is going to change how all almost all companies online. You know, everyone's AI enabled. Right? Go on anything, everything. You know, on the TV, everything's AI, AI, AI.
Jordan Wilson [00:18:06]:
Guess what? They're all going to be paying significantly less now, which ups the competition in the marketplace. But also, in theory, for consumers, we can start demanding lower prices, or there will be competitors that come up that can provide better quality at lower prices. Right? If the, quote, unquote, power or if if the cost of electricity goes down by 90% and you're in the business of using electricity, guess what? That changes your business. Alright. So it does support text and vision in the API and in the chat interface. So it features support for text images and coming soon video audio inputs and outputs, more on that in a bit. Context window in the API, not in the chat interface, is a 128 1,000 tokens. Alright? It supports up to 16 k output tokens per request, and same knowledge base, same knowledge cutoff is October 2023, as well as an improved tokenizer for non English text.
Jordan Wilson [00:19:08]:
In other words, it converts the inputs for non English better to tokens. That's the thing a lot of people don't understand when we talk about tokens. Go listen to that episode. But, essentially, ChatGPT doesn't know words, both when you give it words and it gives it back. It converts everything to tokens to to to I actually understand what you're talking about. Right? So the tokenization, in other languages is very important. Alright. Last but not least here in our bullet points, benchmarks and performance.
Jordan Wilson [00:19:39]:
So let's just go straight into the chart for this one. If you're on the podcast, the benchmarks are silly. They're they're they're silly. Like, never, even if you follow large language models, did someone a year ago, 2 years ago, could look at this chart and think that this would ever be possible at this price? Okay? GPT-4o Mini is outperforming all other small, quote, unquote, small models, except for the Math Vista. Alright. So essentially, we have, what, 8 different yeah. We have 8 different benchmarks. So from the MMLU to the drop to the mgSM, math, human eval, MMMU.
Jordan Wilson [00:20:26]:
Right? So all these different benchmarks that all researchers put models through, you get a score that tells you is a model good or is it not? Is it a bunch of marketing or does it actually have the chops? This is the thing I don't understand y'all when people are like, oh, g p t four o is is so so much worse compared to GPT-4 turbo. No. It's not. You just have to relook at what you're doing. Right? Large language models are generative. That's the thing people don't understand. Just because you had something working in a previous model doesn't mean it's gonna work in the next model. Get to get to know it.
Jordan Wilson [00:20:58]:
You're you might have to reengineer some things. Are some things worse? Absolutely. Across the board? No. You can't argue with more than a 1000000 votes in the chatbot arena leaderboard. Alright. So some of these worth noting, some of these metrics. I just wanna point out a couple. So MMLU, that is the massive multitask language understanding test.
Jordan Wilson [00:21:21]:
So that is, I would say, still the gold standard. I think we're probably gonna be moving to the MMMU as the gold standard because that's multimodal. But the, think of this as text based. Right? MMLU is essentially the ACTs, for large language models. It is the gold standard. What is your score? Right? And it, measures the model's knowledge across 57 different subjects, including STEM, humanities, social sciences, everything. Right? Y'all, this blew everything out of the water. It blew out Gemini Flash, Claude Haiku, Claude Haiku, g p t 3 35 turbo.
Jordan Wilson [00:22:01]:
It is not close. Right? You might look at it on this chart and be like, oh, it's only a a 3 point difference behind Gemini Flash and about a, what, a 7 point difference behind, Claude Haiku. That in MMLU, that is a lifetime away. When we look at the biggest models, you know, they're usually within, like, a half point, half point, maybe even a point one. I believe Opus, you know, Quad 3 Opus when it first came out was like point one higher than GPT 4 Turbo. Right? Usually, it's like a half point. To be 3 to 7 points ahead of your nearest small model competitors means that this is freakishly good. I cannot under understate that, how much that little, seemingly small margin means in the MMLU.
Jordan Wilson [00:22:51]:
Same thing. In math, purpose blows everyone out of the water. We're talking, like, 30 almost 30 points ahead of the others. Wow. So math purpose is essentially complex math topics such as calculus, linear algebra, higher level mathematics. So it is freaking really good at math. Alright? And that's important for data analysis. Again, we have to think not just what you are going in to do inside of Chatt GPT.
Jordan Wilson [00:23:19]:
That's not what this is about. Where this is going to pay dividends and change the way we all work is through the API, is through those 100 or 100 of 1000, right, of different companies, startups, products that you've used. You you know, you're probably playing with a bunch of AI tools all the time. I have dozens of AI tools that I use on a week to week basis. Mostly all of them use GPT. They use the the GPT 4 from OpenAI. Right? So this is gonna change and improve the quality of all these other tools that you use, especially if you're someone in marketing, advertising, communications, or if you're just someone who's constantly playing with AI tools and large language models. This changes everything.
Jordan Wilson [00:23:58]:
Alright? Alright. Now we're gonna have some fun, y'all. Let's go ahead and do a couple of things live. Alright. So, a while back let's see if we can get a good good view here for our live audience. And, hey, podcast crew. I'm gonna do my best to describe what we have going on here. Pretty pretty simple stuff.
Jordan Wilson [00:24:30]:
Alright. So about about a month or so ago when Claude 3 Opus, or sorry, Claude 35 Sonnet came out, we did some comparisons. We just did some some live prompts, and we compared the output. Some of them were deterministic. Right? There's a right answer. There's a wrong answer. Some of them required us to make a call. Alright? So now what I'm doing, I am in the OpenAI Playground.
Jordan Wilson [00:25:00]:
Okay. So what that means is kind of the back end. If you build something, you know, all developers who are using the API, this is where they go. But I'll tell you this. You don't have to be a super technical person. You can get a free account. You can go in there and just play in the playground. It's a great way to learn.
Jordan Wilson [00:25:16]:
If you wanna become better at prompt engineering, if you wanna become better at understanding models, do this inside of the playground. Anyways, so I'm comparing 2 different models side by side at the same time. So we're gonna be looking at we're gonna be asking some questions that have a simple yes and no answer. Is it right or is it wrong? And then we're probably gonna be doing some that require a little bit of, let's use our brains. Okay? So, again, I did this previously, between Claude 35 Opus and GPT 4 o. So now we're going GPT 4 o on the left, GPT-4o Mini on the right. Doing the same prompts, we're gonna be looking at speed, we're gonna be looking at quality, and we're gonna be looking at, in some cases, a little bit of reasoning and logic. Alright.
Jordan Wilson [00:26:00]:
So the first one, again, if you've tuned in to the show, we've done these a couple of times. We're not changing the system prompts. We're leaving everything as is. So out of the box, here we go. The first one, I just woke up today with 6 apples and 3 bananas. Yesterday, I ate an apple and or sorry. Yesterday, I ate a banana and 2 apples. This morning, I will eat 1 apple and no bananas.
Jordan Wilson [00:26:26]:
However, I don't really like apples, and one banana may turn brown tomorrow. Assuming nothing else changes, how many apples and bananas will I have tonight? Alright. We're gonna hit run, and we're gonna see who finishes 1st, and do they both get it right? Wow. Okay. So GPT-4o Mini was faster there. Pretty impressive. Alright. And let's see if they got it who got it right and who got it wrong.
Jordan Wilson [00:26:54]:
Interestingly enough, GPT-4o, got it right. O Mini, Got it wrong. There we go. We already have one use case. This is one, I've seen similar ones like this. I kinda made it up, you know, or modified something similar that I had already seen. So it looks like let's see where GPT-4o Mini kind of got tripped up. So it got tripped up on something that I put in there to trip models up.
Jordan Wilson [00:27:20]:
I test models all the time. Yeah. I told you guys. I'm a geek. Alright. So I put something in there about eating, eating the fruit yesterday. But guess what? I started it off by saying, I just woke up today with 6 apples and 3 bananas. So whatever I talk about yesterday is irrelevant.
Jordan Wilson [00:27:37]:
So, GPT-4o Mini did not understand that. GPT 4 o got it right. The correct answer is 5 apples and 3 bananas. And GPT-4o Mini said 3 apples and 2 bananas. So he got tripped up by yesterday. Hey. If I give you today, yesterday doesn't matter. Alright.
Jordan Wilson [00:27:59]:
Let's do another one. Kind of kind of a tricky one. We're going to I think for the most part, I'm always going to refresh. It shouldn't it should well, actually, I'm not. It's gonna take too long, because we're not gonna be worried about the context here. Alright. The next one here. I'm saying if it takes 3 hours to dry 10 T shirts in the sun, how long will it take to dry 30 T shirts in the sun? Alright.
Jordan Wilson [00:28:24]:
So let's go ahead. Click run. This should be a quick one. Alright. So this one, g p t four o was faster. O Mini was slower. So the opposite last time, o Mini was faster. Alright.
Jordan Wilson [00:28:41]:
The correct answer should be 3 hours. So let's see. G p t four o. Got it right. It said it will also take 3 hours. Let's look at, 4 o Mini. Let's see. And it says it will still take 3 hours.
Jordan Wilson [00:28:57]:
Okay. So our score our score so far for those keeping keeping track. So we're gonna do fast, and we're going to do right and wrong. Alright. So so far, we have, it's it's tied in fast, but in the end, fast doesn't matter if you're wrong. Right? So one, we have many, and one, we have o. And then right, So right now, we have, we're gonna do this. We're gonna do right.
Jordan Wilson [00:29:24]:
Sorry, y'all. I want I wanna make sure for our podcast audience, we can we can recap this at the end. Alright. So so far, we have we have, o. Right. Sorry. I'm making a little chart here. Oh, right.
Jordan Wilson [00:29:37]:
Got 2, and then we have many. Right. So far, just 1. Alright. Next prompt here. This is pretty well, this one is it takes a little logic here. Alright. So here we go.
Jordan Wilson [00:29:51]:
And and what I do love is at the bottom, you can see the latency or how long it took and also the tokenization. Alright. So the next prompt here. A box is locked with a 3 digit numerical code. All we know is that all digits are different. The sum of all digits is 9, and the digit in the middle is the highest. What is the code? So I've even gotten tripped up on this and models have gotten tripped up. Before I hit enter, there's actually multiple answers.
Jordan Wilson [00:30:20]:
One time I got confused and a model said this is the only correct answer, and I'm like, Yeah. That's right. And, no, there's actually multiple answers. So let's go ahead and run it and see how it goes. Alright. So both are going pretty fast, still computing. Alright. So they finished just about at the same time.
Jordan Wilson [00:30:41]:
It looks like, Mini was just a little bit faster, but let's see who got it right. So I'm looking here at GPT 4 o. It's walking me through things. It says the digits are different. The sum of the digits is 9. The middle digit is the highest. So it's doing a little bit of a formula. And let's see.
Jordan Wilson [00:31:04]:
It's, so it's going through different cases. Case 1, case 2. So it says, I don't even know if either of these finished generating. They may have stopped. It looks like they both stopped halfway and didn't complete. So let's I'm gonna try this one more time and see if they finish this time. Okay. So it doesn't look like g p t I'm I'm double checking here.
Jordan Wilson [00:31:35]:
It doesn't look like g p t four o finished this, which generally when I do this, it finishes. I don't know if it's because I'm in the compare mode and doing head to head. It really shouldn't make a difference. So it's going through yeah. I'd have to read this. I'd have to read this. I'd have to take a little time. There's a lot of math on the screen.
Jordan Wilson [00:31:54]:
It didn't give me a clear answer. I'm gonna try the prompt, and I'm gonna say at the end, I'm gonna say, please respond only with the correct answer or answers. I'm gonna say you do not have to show your work. Sorry. I'm getting confused. Too much math on the screen. It's late. Alright.
Jordan Wilson [00:32:15]:
Here we go. That worked a little better. So, neither of them got it right. We'll just say both. Okay. So neither neither got it right. So g p t four o, said the code is 273. Middle digit is the highest.
Jordan Wilson [00:32:27]:
However, those do not add up to 9 when combined. And GPT-4o Mini said the code is 147 also do not add up. So they both got that one wrong. Alright. We're gonna do just one more test. Maybe maybe we'll do 2 more. So, I'm gonna do one. I'm gonna say, please, let me get let me get the, this other prompt that I had, ready to go here.
Jordan Wilson [00:32:54]:
So this one is about kind of starting a company. Alright. So let's go ahead, and here it is. I did this before. This one's a little longer. So I'm gonna click run while they go, and I'll look at the latency because this one's a little longer. Okay. So what I'm saying here is create a new company and brand for a future smart home device.
Jordan Wilson [00:33:17]:
This will solve a problem that does not currently exist. To start, come up with the company's name and its first flagship product. Give the product a new branding campaign, go to market strategy, tagline, and rationale for why it'll work, respond in a succinct way keeping responses to short bullet points, but with ultra specific facts. Alright. So, from a speed standpoint, it looks like GPT 4 o was slightly faster, but not by a lot. Alright. And there is no right or wrong here. This is subjective, so I'm not gonna read the whole thing.
Jordan Wilson [00:33:49]:
So let's just see the takeaway here with what GPT 4 o came with. It came up with Verity Solutions. The the flagship product was Nomezy. Not sure what that is. It's eliminating and stress associated with man managing and memorizing the various names and access code for different, smart devices in a home. Alright. Okay. I see that.
Jordan Wilson [00:34:12]:
Let's see. The branding campaign is smooth. Management starts here. I got some social media campaigns, simple go to market strategy in good, multiple phases. Good. The tagline is no meezy simple. Okay? Not the best, but not not terrible. Right? Again, how how I wanted the output.
Jordan Wilson [00:34:33]:
Right? I said keep it succinct. Give it to me bullet points. So I think this is where in theory GPT 4 o may have shined over GPT-4o Mini. Anyways, let's look at GPT-4o Mini, what it came up with. So it is Home Vigil, and the flagship product is Lifeguard. So, again, I'm asking it to solve a problem that does currently not exist in a future smart home device. Alright. So GPT-4o Mini came up with home vigil company name.
Jordan Wilson [00:35:05]:
The flagship product is Lifeguard. It is a smart home device equipped with AI driven sensors to detect subtle changes in a household's atmosphere, such as smells, noises, movements, etcetera, to predict potential safety hazards. Alright? That's pretty good. Alright. The branding campaign is safety in the silence. Oh, okay. I like that from Lifeguard, safety in the silence. Same thing, go, go to market strategy.
Jordan Wilson [00:35:33]:
It even gave us a target audience, which I don't think that GPT 4 o did. Gave us different channels. Alright. I like that. Gave us even a launch event. Whereas for the most part on GPT-4o, it just went through phases. It was awareness, engagement, expansion. Where on the other hand, GPT-4o Mini really took it a little more granular.
Jordan Wilson [00:35:54]:
It gave us a target audience, different channels, you know, different strategies for those different channels, a launch event. So pretty good. And the, oh, I just realized y'all why the why we didn't get the, the full from the, code one. It's because I think I have a a cutoff here. That's that's probably that's probably what it was. That's probably what it was. Alright. So, anyways, I think for g for this test, I would say GPT-4o Mini won this one.
Jordan Wilson [00:36:30]:
Alright? So I'm I'm actually I'm, cranking back up the maximum tokens. I feel I didn't give a fair shake. I I I kept wondering. I'm like, why is this cutting off when we are asking, about the locks? So we're gonna do that one one more time. But, however, I'm giving that one if I'm being honest, I'm giving that one to Minnie. I didn't expect many, to to win in something that you know, I'm I'm not saying this involved logic and reasoning, but it was much better. Right? GPT 4 o gave me something kinda complicated. Again, large language models are generative.
Jordan Wilson [00:37:04]:
I'm pretty confident if I did that a 100 times, GPT 4, would win out against many, but that just shows many is capable. If you don't think it is, that just shows to to me, that shows me it's capable. Alright. I'm gonna run this, this code one one more time. That's why it was cutting off. I forgot. I I had that setting on there. You can restrict how long the output is so you don't accidentally spend way too much money.
Jordan Wilson [00:37:29]:
Right? Because you're paying for the tokens, but they're pretty cheap. Alright. So wow. Okay. A lot here from GPT-4o Mini, and we still just have more math code problems. So, here we go from okay. So we got a we got multiple correct answers from GPT 4 o when we reran this. So, essentially, it needed more memory.
Jordan Wilson [00:37:54]:
That's all it is. It couldn't do it with the limited tokens. It actually wasn't done. I cut it off early. So g p t four o got multiple correct answers. 153. That's correct. Middle digits, highest adds up to 9.
Jordan Wilson [00:38:08]:
163 is not correct. That adds up to that adds up to 10. So it did get one correct, variation, 153. Alright. So let's go ahead and look at GPT-4o Mini. So it says one valid code is 243. That's correct. So that adds up to 9.
Jordan Wilson [00:38:34]:
So it didn't give me any wrong answers, g p t 4 o Mini. G p t 4 o gave me one wrong. There's actually a lot more correct answers, And GPT 4 o one time gave me a list of, like, I don't know, like 15 correct answers. So in this case, actually, GPT-4o Mini got it a little more right because it didn't give me a wrong answer. I'll give that one like a half. It didn't give me any wrong answers. It only gave me one correct answer, whereas GPT 4 o gave me a correct answer and an incorrect answer. So, alright.
Jordan Wilson [00:39:12]:
Let's do one more, and this is our last one. This isn't supposed to just be a live head to head comparison, but I wanted everyone to be able to get at least a good understanding of this. So for this one, here's what we're gonna do. We're gonna test the vision capabilities. So I just dropped a photo. I took this on the road in Chicago. Alright? And I'm putting in a prompt. So this one, I am pretty curious which one's going to, finish first.
Jordan Wilson [00:39:42]:
So I'm saying, please identify where this picture is located, what direction the photo is facing, and every other detail that you can make out. Alright. So I'm gonna go ahead for our livestream audience. I'm gonna throw this up on the screen big so you can look at it. I got I'm telling you. I got the perfect photo to use for this because it shows, like, I don't know, 20 cars, but you can't even see any license plates. So it's not like I'm even putting anyone's, you know, personal bat out there. I was kinda happy about that one.
Jordan Wilson [00:40:11]:
However, the correct answer is we're on 9094 heading southeast, and you can tell that by so this requires actually a lot of knowledge, which is why I use this. Because the way that you can tell this aside from the, the order of the buildings. Right? That's one way because you have the Chicago skyline. And depending on which way you're you're looking at it. Right? If you're looking at it like north straight down, the the buildings, the skyline are gonna be in a different order. If you're coming up from the south, they're gonna be in a different order. Right? So that's one way. But also you can see 2 different exit signs.
Jordan Wilson [00:40:42]:
So as an example, we have 46 a, California, 47 a, Fullerton. So in theory, it should know. Right? It's it's heading in, this direction. The the the numbers are going up. Right? So it has plenty of clues. Alright. So let's jump in. Let's run this live and see how each model does.
Jordan Wilson [00:41:01]:
Let's go. Alright. So put the prompt in. GPT 4 o, much faster. Mini took a while to get start okay. Interesting there. So Mini took longer to get started, but was actually done much faster. GPT 4 o got started way quicker, but took a little bit longer.
Jordan Wilson [00:41:27]:
But, ultimately, what I care about is who got it correct. Alright. So interesting. So, the GPT 4 actually, let's keep going left, left to right here. Great job. GPT-4 o. This is this is very impressive. Alright.
Jordan Wilson [00:41:46]:
So it says the photograph is taken in Chicago, Illinois. Landmarks in the background, Willis Tower, John Hancock Center. Cool. It says direction. The photo is facing southeast towards downtown Chicago. Correct. Specific highway details. It's breaking them all down.
Jordan Wilson [00:42:03]:
Right? 47 a for Fullerton. 47 b for California. Yield sign. It's talking about traffic. It's even telling me it's probably during rush hour. It wasn't. It was on a Sunday. I think there's a Cubs game.
Jordan Wilson [00:42:15]:
Anyone else a Cubs fan? Are we Sox fans? Who's your favorite baseball team? Let me know. Infrastructure. This is great. It's giving me a huge break. It's even going into vegetation. Y'all, the vision models are nutty like a squirrel on keto. Alright. Weather and time.
Jordan Wilson [00:42:33]:
My gosh. This is good. It's it's giving me, guesses. It says general vibe. It says typical urban highway. This is good. So it answered everything correctly. Not only did it answer the simple question about where I was and what direction the photo was taking, but I also asked it, give me every other detail that you can make out.
Jordan Wilson [00:42:53]:
So interestingly enough, GPT-4o Mini's vision doesn't give me a lot of, doesn't give me a lot of the same. It essentially says I can't identify specific locations or details about the image you provided. It does say there's traffic, there's cars, there's tall buildings. You know, it it shows that there's Fullerton Avenue and California Avenue. There's photos in the sky. So that's interesting. Right? So, actually, what I'm gonna do so hands down, I'm giving, I'm giving o the win on this. Alright? Many did not win.
Jordan Wilson [00:43:27]:
GPT 4 o won this. So I'm gonna say, I'm gonna add add add some more things. I'm gonna put, please take your time, go step by step, and understand the context clues in order to provide the answers. Alright? So I'm wondering with a little more I mean, quote, unquote, prompt engineering. Right? Yeah. With a little more prompt engineering, I'm wondering if GPD 4 l Mini can do this because I was assuming it could have. Again, this is available in the API. So if you're thinking, oh, should I be tapping into the API? I mean, it didn't get anything wrong, but it also didn't get it right.
Jordan Wilson [00:44:13]:
Or am I just, really underestimating, or not giving enough props to the level of detail that GPT 4 o gave? Alright. So I'm doing it one more time. I'm just doing a rerun. Just I'm curious if a little bit of extra prompting, will help GPT-4o Mini. So same thing, GPT-4 o got started right away. Looks like we're getting stalled out. Looks like we're getting stalled out here on, GPT-4 o Mini. Yeah.
Jordan Wilson [00:44:41]:
Server errors. I'm I'm I'm not surprised y'all. This just came out. People are building on it. They're they're breaking it. I'm actually surprised how fast it's been, if I'm being honest. GPT-4o Mini, considering probably all dorks in the world are trying to break this thing right now, including myself. We're gonna try it one more time.
Jordan Wilson [00:44:59]:
See if see if we're gonna get another, error message. But, hey. I'm curious. Podcast audience, drop me a line. Are you gonna be using this? Are you gonna be moving everything over, to, GPT-4o Mini? What tests are you running? Alright. So here we go. Same thing. GPT 4 o did a great job.
Jordan Wilson [00:45:20]:
Let's see if, GPT-4o Mini did a little bit better. So it did with a little bit more, prompting and a little bit more help on my end. It did say Chicago. It noticed the Willis Tower. Although, what's the Willis Tower? Y'all, that's Sears. Don't mess. Now it's now it's giving me more information, traffic situation, road signs, direction. So let's see if it got the direction.
Jordan Wilson [00:45:48]:
The direction the photo is facing appears to be toward the city skyline, suggesting the photographer is moving toward the downtown, Chicago. Okay. Correct. It didn't get it. So I'm gonna try one more time. Again, we're giving the win on this one to GPT 4 o. I'm gonna say given all of those context clues, what direction is the photographer facing? Alright. GPT 4 already got this right.
Jordan Wilson [00:46:19]:
I'm just wondering if we can squeeze the right answer out of GPT-4o Mini. Gosh. I mean, GPT 4 o. Super impressive. Alright. Let's see. So it said, given the context clues, particularly the presence of Chicago, Conlon in the background, the photographer is facing east toward downtown. Yeah.
Jordan Wilson [00:46:36]:
It's east. It's southeast, but got it right. So with better that's yo. That also goes to show, number 1, understanding generative AI. It's It's a roll of the dice. You're gonna get something different if you go in there with a weak prompt. I went in there with a weak prompt. The the point of this wasn't prompt engineering.
Jordan Wilson [00:46:51]:
But when I followed it up with a little bit better prompt and I followed it up with a secondary you know, when I, prompted iteratively or just followed it up with, hey. Now let's try it again in this way, It eventually got it right. Alright. So that wraps up our live look. Now I'm gonna quickly go over what no one is talking about. I know this is a longer one, but I cannot underestimate the importance. So couple things. If you are on a free account, it's actually hard to access this.
Jordan Wilson [00:47:21]:
Right? On the front end of ChatGPT, they actually changed it. So previously, you had a traditional model switcher. Now all you have is and this is brand new as of today, I believe. Now you just have ChatGPT, which is if you're on the free plan, that's what you're on. And then it says ChatGPT plus. So, oh, I forgot my other, my other slide here. But, essentially, you can still access, Let's let me go ahead and share share my screen here and show you. So you can still access the updated version here.
Jordan Wilson [00:48:03]:
So what you have to do, first, you have to prompt. Right? And then after you prompt, then you get the new model, selector. So if you actually want to try out GPT 4 Mini and you are on a free plan, you can't select it on the normal drop down. You have to first do a prompt, and then there is a model switcher right underneath ChatGPT's response, and then you can do ChatGPTmini, or or sorry. GPT-4o Mini. So now I'm on mini, and it's obviously asking me, is this better, the same, or worse? Alright. I'm not here to train you right now, model. I'm in the middle of a podcast.
Jordan Wilson [00:48:40]:
Alright. So number one thing you need to know, it's a little hard to access for free users on the front end of chat gbt, which I think a lot of people are. So that's why I wanted to throw this out there. This is everyday AI for everyday people. Right? Number 2, something people aren't talking about. What does this mean for the future of Apple's plans? Right? Because number 1, people aren't talking about this fact either. Apple is actually they built their own large language model. They didn't even mention it by name.
Jordan Wilson [00:49:10]:
Man, I feel bad for all those software developers at Apple, during Apple's WWDC in June. They didn't even mention their own large language model by name. They didn't even really explicitly in the keynote, at least, I'm talking about in the keynote, they didn't even explicitly, decipher that, yes, when we're doing all of this Apple intelligence, quote, unquote, that there's actually 2 different models. We have our own Apple, on device edge AI model running, essentially all the queries that just have to do with the information on your device. And for most everything else, you're going to be prompted to use GPT 4. I I believe GPT 4 0. Right? But what does this mean now? A much smaller model. Again, we don't know how much smaller it is.
Jordan Wilson [00:49:53]:
I'm sure we'll find this out soon. I don't think that this model will be small enough to fit on a future, on a future device. Right? At least a phone. I think right now, you know, the the Google smartphones, the s 20 fours, that actually have a large language model living locally on the phone. That's why it's important. Right? There's more privacy. It's faster. It's it's more energy efficient.
Jordan Wilson [00:50:18]:
Right? So, I I think the future is working edge AI, right, on device smaller models. But I still think that this new model, from OpenAI GPT-4o Mini is probably too small, to run locally, but I would think the next version of it, probably whether that's in 6 months or a year, is probably gonna be able to fit, which is wild to think about. Right? Which number 1, the impact on the environment. Right? The more edge AI that you can run, the better the environment is. Right? One of the biggest problems right now is this uses a lot of power. Right? A single prompt to ChatGPT takes up 10 times more power, than a Google search. Right? And as we're using large language models more and more, you have to think of the environmental factors. So moving to edge is better for everyone.
Jordan Wilson [00:51:09]:
Moving to on device is better for everyone. It's faster. It's more secure. It's better for the environment. But right now, it's not really possible for a lot of use cases. Right? To get the power that you want, you know, squeezed on a on a on a little device, not possible. But I think probably in the future, it's going to be. Maybe the next version of GPT-4o Mini might be able to run locally on a smartphone, which would be amazing.
Jordan Wilson [00:51:34]:
So you have to look at the future of this Apple and OpenAI partnership. Here's another. Aside from cost, there's actually a reason you might want to use, GPT 4.0 Mini. I talked about, hey, if you're on ChatGPT, you probably aren't gonna wanna use it because there's real no advantage. You're not paying for it anything differently in ChatGPT. Right? There, if you're on the ChatGPT Plus plan, you're paying $20 a month and so you don't have to worry about cost. The main advantage here is cost in the API. If you're building a third party application piece of software or whatever, you know, building, fine tuning your own model for a company with with, retrieval augmented, generation.
Jordan Wilson [00:52:11]:
Right? But here's the other key benefit that no one's really talking about. It is the 1st model to apply OpenAI's brand new, which we just talked about on the show this week, instruction hierarchy method, which helps to improve the model's ability to resist jail breaks, prompt injections, and system prompt extractions. So, I mean, that right there is a great reason in why I think so many companies are going to be flocking to GPT-4o Mini. Right? You want your business to be safe. Right? The last thing you want is prompt inject injections for someone to jailbreak. Right? A lot of companies don't take proper safety precautions. Right? And part of it is because it's so easy. It's so easy to essentially create a wrapper.
Jordan Wilson [00:52:57]:
Right? Put in a little bit of your company's data, do a little fine tuning. You know, it's not super expensive like it used to be. It's almost so easy that too many companies are doing it, and they don't take proper safety measures. So this new, version of GPT-4o Mini in their API will be a godsend, for those companies that are maybe facing some of those issues. Alright. Another advantage, or sorry. Another thing no one's talking about. And why is no one talking about this? I should have started the show out with this.
Jordan Wilson [00:53:29]:
Ready? Do people not like reading? I still like reading. This was toward the bottom of OpenAI's blog post announcement. So they said, today, GPT-4o Mini supports text and vision in the API with support for text, image, video, and audio inputs and outputs coming in the future. Oh, look at that. I believe this is the first time in writing that we've seen, OpenAI said in the future, there will be, audio and video input output. So. Right? Yeah. Sora accessible via an API.
Jordan Wilson [00:54:14]:
Sora accessible via ChatGPT, presumably. That's huge. And also video input. Did you guys know that you can actually input a video into ChatGPT right now? Go try it. See. Tell me tell me what happens. So this is huge, though. Again, this is all reasonable assumptions pointed to this.
Jordan Wilson [00:54:34]:
Right? Yeah. You'll be able to. It's it's multimodal. Right? The future's multimodal. That's the whole point of the GPT 4 o, which is omni. Instead of using 3, technically, 3 separate models on the hood, it's using 1. So this was always the assumption, but I believe this is the first time in writing that OpenAI said, hey. Input, output, you're gonna have, you're gonna have video, input and output.
Jordan Wilson [00:54:54]:
So pretty big. Right? Right now, that's actually one area Gemini has been crushing it in with their super long context window being able to upload videos, and it can scarily, know everything. Yeah. Talk about, wild use cases of generative AI. Alright. Number 5, more and better AI is coming everywhere. So like I said, you probably don't even know this. Right? As an example, maybe the wealth management company you use has a little AI advisor that you talk to or maybe a software that you use for work, has a customer support bot powered by GPT.
Jordan Wilson [00:55:30]:
Right? You probably don't realize it. Apps on your smartphone, so many things use GPT behind the scenes. Alright? And so many of them are about to get so much better. Alright? Which is related to number 6. The prices are going to be the prices are right now, but they're going to be getting so affordable. Companies can't afford not to build on generative AI. Let me say that again. Prices for building on top of a large language model are getting so affordable, companies can't afford not to build on it.
Jordan Wilson [00:56:11]:
Right? If you would have asked me a year and a half ago, hey. Should all companies be building on top of large language models? I said it very early on in the podcast, probably, like, 13 months ago. I said, no. Companies shouldn't because look at the prices back then, and the quality wasn't good. The quality wasn't good, and it was too expensive. Now? My gosh. Quality is great, and it is cheap, and think now. Right? If your company is is on the fence today, well, first of all, reach out to us.
Jordan Wilson [00:56:41]:
Right? We offer consulting services. We have other partners we work with. We can walk you through that if you're not sure. Right? I'm lucky enough I get to talk to super smart people all the time. I just had a conversation today with, by the time you guys hear this yesterday, with, you know, a senior director at Microsoft AI. I'm I'm lucky enough to get to talk to literally the smartest people in the world building things. So we can help walk you through this, so reach out to us. You know, I have my my email, my LinkedIn, and the podcast, and, you know, you guys know how to get me here on the livestream.
Jordan Wilson [00:57:11]:
But companies need to be building. If you haven't made that decision now, look at this visual on the screen, this step down in price. Right? And then think of the increase in quality. You need to be thinking now. Okay? You have to be thinking. So it's it's getting it right now where you need and this is gonna this is gonna sound bad, but you need to start comparing the cost to train, deploy, and upkeep a model versus the cost of humans because it's gonna be lopsided soon. It's gonna be lopsided. Right? That's why I think everyone is going to be working.
Jordan Wilson [00:57:52]:
Not everyone, but so many people are going to be, quote, unquote, working in AI. Right? Taking, you you know, this first I call it first party company data and turning that into knowledge for your model. Alright. Let's see if we can beat the hour mark here. Number 7, energy demands are gonna be bonkers. That's my last thing you need to know. I kind of already alluded to that. But all of these things adding up, these models getting faster, cheaper, better, easier to work with, demand is going to be I and I'm not just saying OpenAI GPT-4o Mini, because guess what comes next? Anthropic's gonna strike back.
Jordan Wilson [00:58:34]:
Google's gonna strike back, which is going to make OpenAI strike back. Energy demands are going to be bonkers until we can get all of these models or many more of these models running locally. Energy demands are gonna be off the out of the roof. And, and so and at the same time, watch NVIDIA's stock, watch AMD's stock, watch Qualcomm's stock, all the companies that are making chips. Obviously, NVIDIA has an unfair head start. Their stocks are just going to continue to rise in the long run because we all need the GPUs. Alright. So that's it, an in-depth look.
Jordan Wilson [00:59:14]:
I hope I hope if you stuck with me to the end. Let me know. I I always I'm always curious. Did you make it to the end? I don't know. Send me an email or DM on LinkedIn with the word pancakes. I'm just curious. Did you make it at the end? Let me know. And I'm hungry for some pancakes.
Jordan Wilson [00:59:32]:
So I hope this delivered and I hope now you better understand GPT-4o Mini because like what I talked about in the beginning, it's actually not mini. It's actually, I think, a pretty big deal. This is a pretty big deal. I think this is going to greatly change the way that we all work and really the future of generative AI. Alright. Thanks for tuning in y'all. If this was helpful, please consider reposting this. Leave us a rating if you're listening on Spotify or Apple.
Jordan Wilson [01:00:05]:
Appreciate the support. And don't forget Tuesday, July 23rd. Hopefully, you're listening or watching this before that time. Mark your freaking calendars, y'all. 7:30 AM Central Standard Time, July 23rd. The link for the LinkedIn livestream. Hopefully, LinkedIn doesn't go down. If so, you can join on YouTube.
Jordan Wilson [01:00:26]:
You gotta get your quest you gotta get your answers in live. Alright? A $1,000 to anyone who gets a perfect score. If no one gets a perfect score, no one gets the money. So there's gonna be some easy questions. There's gonna be gonna be some trick questions. But if you are an avid listener of the Everyday AI Show, you're probably have a decent chance that you're gonna get most of these right. You gotta be quick, but you gotta be there. So please join us, and make sure to go to your everydayai.com.
Jordan Wilson [01:00:54]:
Sign up for the free daily newsletter. Thanks for tuning in. Can't wait to see you next week and every day for more everyday AI. Thanks, y'all.
AI [01:01:01]:
And that's a wrap for today's edition of everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.