EP 332: Claude Projects - What they are and how your company can save time using them

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What are Claude projects? Maybe you've heard of Anthropic Claude and maybe you haven't, but they actually have a great feature that rolled out a couple of weeks ago that I think people aren't paying enough attention to. It's kind of like creating your own version of a large language model on your data and with some custom instructions. So we're going to be going over Claude projects today, what they are, and even building one live right here on Everyday AI. What's going on, y'? All? My name is Jordan Wilson and I'm the host of Everyday AI and this thing is for you. And it's a daily livestream, podcast and free daily newsletter helping us all better learn generative AI so we can actually use it and actually leverage it to grow our companies and grow our careers. So today's episode is, I think, especially relevant for that because we always hear a lot like, hey, Jordan, seems like there's so much going on in AI and all these large language models. What are some actual use cases? How can I actually use them? Are they powerful? Can they work with my data? Well, we're going to be answering what, all of those questions and giving you, I think, some very practical and real use cases live on today's show. All right, so before we get into that, we're going to start, as we do every day, by going over the AI news and if you are new here, joining on the podcast or the live stream. Thank you so much for tuning in. Make sure to check out the show notes on the podcast. For a link to our website, you need to go to your everydayai.com Sign up for the free daily newsletter where we recap everything that's going on in the world of AI and as well as more depth on today's episode. All right, let's take a look what's going on in the AI news. All right, so Palantir and Microsoft are partnering to boost AI for US Defense. So Palantir Technologies, which works in the defense space, and Microsoft have announced a partnership to enhance AI capabilities for the US Defense system and intelligence communities. So this collaboration will integrate Microsoft's Azure OpenAI service with Palantir's AI platforms in classified cloud environments. The aim is to operationalize AI for national security tasks like logistics and action planning using advanced models such as GPT4. The rollout of these services will depend on government authorization and accreditation. Palantir's suite of products, including Foundry and Gotham, will be deployed in Microsoft Secure Government Clouds. Both companies stress their a stress their commitment to responsible privacy standards and ethical AI practices. Yeah, this one's interesting. More AI and even from open AI making its way to the defense industries all right, next French startup and large language model maker Mistral has has announced major updates hours ago, highlighting the introduction of customizable agents and advancements in LA platform. All right, I don't know if that's how you say it in French, right? But this new alpha release of Agents offers a new way to wrap models with additional context and instructions. These agents can be used on Lead Chat or via the API to create custom behaviors and workflows. With the advanced capabilities of Mistral Large 2, their newest and very capable model, developers can build increasingly complex workflows using multiple agents, which are designed to be easily shared within organizations. Future plans for agents include connecting them to various tools and data sources, enhancing their functionality and versatility. LA Platform now supports the customization of flagship models like Mistral Large 2 and Codestro, also from Mistral, obviously. And developers can tailor these models using base prompts, few shot prompting or fine tuning, even with personal data sets. So yeah, very similar to what we're going to be going over today from Claude in their projects was just released literally hours ago, so make sure to check out the newsletter for more on that. Last but not least, OpenAI CEO Sam Altman has been teasing OpenAI's newest model, and a lot of people are thinking it's going to be coming out any day. Like maybe today. I don't know personally, but here's what's actually happening. So OpenAI CEO Sam Altman has sparked intrigue with a cryptic, cryptic tweet about Project Strawberry hinting at advanced AI developments. So Altman shared a photo of strawberries in a garden with the comment I love summer in the garden, suggesting a deeper meaning related to OpenAI's secret project. So yeah, just it was a photo of strawberries, y'. All. But according to a recent Reuters report, Project Strawberry, which was previously called Q Star, aims to develop a model with advanced reasoning capabilities, moving closer to autonomous AI agents. And also reportedly with this new Project Strawberry, there is an emphasis on these agents being able to autonomously crawl the web as well. So internal documents reveal that strawberry from OpenAI could enable AI to not only generate answers, but also plan and navigate the Internet autonomously for deep research. All right, so keep an eye out if it does break today, we'll obviously be covering it here on OpenAI or, sorry, here on Everyday AI, but, you know, it's hard to tell with these things. However, OpenAI did actually tease and release its last model, GPT4O, in a similar way, with CEO Sam Altman essentially sending out a similar cryptic tweet. So there could be some new developments from OpenAI after they've kind of been going through a lot recently with reports that they're, you know, use losing billions of dollars with other reports of some of their top leadership leaving for other companies. So it's been kind of a bad week for OpenAI when it comes to press. So, you know, we'll see if this is actually, you know, amounts to anything. All right, that's it, y'. All. Let's go ahead and, you know, hey, but as a reminder, make sure to go sign up for that newsletter. There's a lot more today, your everyday AI dot com. Let's get straight into it. Let's talk about Claude projects, what they are and how your company can save time using them. And we're also going to be looking at how these new Claude projects compare to GPTs from OpenAI. All right, so here's what we're going to be going over. We're going to be going over Anthropic CLAUDE overview. So if you are brand new to Anthropic Claude Claude, we're going to be giving you the high level. Don't worry. Because, yeah, we talk about Chat GPT a lot on the show, but if you don't know Claude, don't worry. We're going to go over an overview of that. We're going to go over a projects overview and then we're going to say why your, you or your company might want to use Anthropic Quad's new projects feature. We're going to build a project live here on the show. Yeah, if you're listening on the podcast, we actually do this as a live stream. This is unedited, unscripted, the realest thing in artificial intelligence. And then last but not least, we're going to compare the Claude projects to GPTs from open AI. We're going to briefly go over the pros and the cons, as well as look at some of the outputs. All right, live stream audience, thanks for tuning in. Appreciate y' all, as always. So, uh, Kathleen joining us. Thank you. Uh, Jennifer, good to see you back. Zane and Jason and Gordon, uh, everyone else, big Bogey face joining on YouTube. Cecilia, Tara, let me all, uh, let me know, have you guys used Claude projects before. What are your biggest questions? Get them in now. I'll try to answer them either as we go or at the end of the show. All right, so let's first start with a Claude overview. So if you are brand new to Anthropic Claude, it's pretty simple. It's a large language model, right. A lot of people, you know, maybe they just know of AI as Chat gbt. AI is not new. It's been around for decades. Generative AI, you know, the definition of it is always changing, but essentially you could say the birth of generative AI took place sometime from 2020 to 2022 with chat GPT and the GPT technology. However, Anthropic was founded by former OpenAI executives. We talked about that on the show on Tuesday. But it is a very capable model, a very capable large language model. And their newest version of this is Claude 3.5 sonnet. So it scores very high. A lot of people say that Claude 3.5 Sonnet is the most capable model out there right now. More so than OpenAI's GPT4O? I would say not so much. I still personally think GPT4O is better, but that's up for personal taste. Right. When you look at benchmarks, for the most part, OpenAI's GPT4O is still leading, at least for products that are available for consumers on the front end. All right, so that's a super high level overview of Anthropic Claude. It is backed by Amazon in a huge way, I believe a four billion dollar investment. And I do think that they are recruiting. Anthropic is some of the top talent in the world. Right? So we talked about this earlier. Some of the top executives, even in the last week or last couple of months, have been leaving OpenAI to go to Anthropic. So very capable model. I think from a features and functionality standpoint, they are behind, at least when you Compare them to OpenAI and chat GPT. We'll talk about that a little bit more. But if you are brand spanking new to Anthropic Claude, that is it. There is a free plan, which is very limited. I think the Chat GBT free plan is much better. So there is a free plan. There's not a lot of outside features and functionalities. And let me just put this one out there first before we even get into any comparisons. A downside right now of Anthropic Claude and why I tell like, right, So a lot of people hire us to, you know, consult them. And help us, you know, implement or to help them implement these kind of systems for their companies, right? You see all these, you know, you know, studies from, you know, McKinsey Digital that say, you know, 60 to 70% of manual knowledge work is going to be automated by generative AI and companies are like, yo, I want that. So we help companies, you know, you can hire us, we can walk you through this. But one thing I always talk about with companies is hey, even if Anthropic Claude looks great right now, I don't recommend it for a lot of entry level use cases mainly because it is the only, I think, major large language model maker. At least if you kind of look at the quote unquote big four, OpenAI, Microsoft Copilot, which technically uses OpenAI's technology, Google, Gemini and then Anthropic Claude. Claude is the only one that doesn't have some sort of Internet connectivity out of the box. And that is extremely important in a huge downside for Anthropic because essentially large language models, to oversimplify this, they have a knowledge cutoff date, right? So these models are trained on data up to a certain point, right? And that's where you can run into trouble because think of your, your business, think of how fluid things are, how much trends change in your industry. You, if, if you are working with a large language model, you want that model to have the ability to connect to the Internet in real time and you want some sort of ability to navigate it, right? If you do want to start bringing your business operations inside of a large language model, which you need to, you need to know what that model, what that model is capable of and what it cannot do. So right now it is worth in, you know, as we're going over a Claude anth overview or sorry, an anthropic cloud overview, it's important to point out it doesn't have access, right, to the Internet in real time. Obviously if you're using third party softwares, if you're using Anthropic's API and building something on your own, you can access the web via other ways. But out of the box, if you are just logging on to, you know, Claude AI, right, that's the, the address. If, if you want to go there and sign up for a free account or use a paid account, that's where you would access it. But out of the box it does not have real time Internet access, which is a huge downside. All right, yeah, Michael, I, I, I agree with what Michael said here in the comments, said Claude's free plan is so limited. So I upgraded even there, if I'm being honest, even their paid plan is extremely upgraded in terms of limits. I, I hate saying this, right, because I think Claude is a great product. For me, it's almost unusable. Right. Again, I am a power large language model user. I believe it's 30 or so messages every five hours. I could be wrong on that. It might be 50, but the limits are so low. Obviously on chat GPT we have a teams plan which actually gives you double the limits. But even the paid plan of ChatGPT is much, much better in terms of limits than the quad anthropic plan. So that's just something to throw out there. All right. Yeah, a lot of people, a lot of people on, on the live stream here loving Claude. You know, Darcy says she loves it, Zayn says loving it. Gordon, same thing. Yeah, but the limits, the limits are bad. So, hey, if someone from Anthropic is watching, listening, you need to address that. I do not. Literally. When companies ask me, hey, what are the pros and the cons and how should we start implementing generative AI? I say Claude's a great model, but two of the main reasons you shouldn't be using it, at least if you're trying to use it as a front end user, right? If you're using it via the API, those limits are gone and you're just essentially paying for usage. Right? But a lot of companies want to get started, especially medium and small companies, they just want to get started by logging onto the front end of, you know, a claw AI or a chatgpt.com or a Gemini. And right now, Claude, if you're just logging in via their, even their paid platform or their teams platform, the limits are so low, it is, I think, not super usable, which is worth noting. All right, so now that we have our Claude overview out of the way, let's talk basics on what these new cloud Claude projects are. Okay, so if you've used custom GPTs from OpenAI, they are essentially that, if you don't know, let me explain this to you. So think of Claude projects, right? Think of all the different ways that you can use a large language model for your work, for your business, for whatever manual knowledge work tasks that you do, I always recommend start moving all of these things that you do on a day to day basis, these knowledge domain expertise tasks that you do, whatever it is, and start moving them into a large language model like ChatGPT or Claude. So we're going to go through some Examples and be doing this live. But essentially, CLAUDE projects is a way to create many different customized versions of their model, right? So essentially there's pros and cons with big, these huge, large language models. Essentially, they are capable of everything. But sometimes, you know, it's the saying, jack of all trades, master of none, right? These models, by default, out of the box, they can do anything. But if you're not good at prompt engineering, if you're not good at the basics of working with a large language model, you might be kind of disappointed. When you want a model to do a very specific task for you, it takes a lot of work, right? And that's where projects comes in. So essentially, think of projects as this. You can create an unlimited amount of smaller versions of claude, right? You can essentially, it's not as far as saying, you know, you're fine tuning it, but you are essentially kind of training it to just work on a specific task. So you're saying, hey, huge model that's capable of everything in the world. When we work on this project, here's what you should be focusing on. So you can put in your own instructions and, and also your own knowledge base. Essentially you can drag and drop different files into this CLAUDE project. So you're saying, oh, you know, let's say you, you have a data analyst project and you dump in, you know, all of your company's data. Again, I need to put out the disclaimer out there. Never upload private, confidential, sensitive pii phi, private, you know, private or personally identifiable information, private health information. Don't put any of that into a large language model unless, you know, someone in charge of your company says, yes, we can do this. We checked with, you know, legal compliance, whatever, you know, all laws, right? Laws in the EU, etc. Right? But essentially you can dump all of your data or whatever data that you want and then create a project. And then when you chat with that project, you are still using the base model of claude. So you would probably want to be using the most powerful version, which is Claude 3.5 sonnet, until, you know, they release 3.5 opus or something else, you know. So essentially you are creating a customized version of CLAUDE on your documents and you essentially give it instructions. All right, hey, let me know, live stream audience, we're gonna get, we're gonna get building here, but I want to know from, from you all, are you using CLAUDE projects? Are you using GPTs, are you using neither? And why? What questions do you have? Brian said, I, I use CLAUDE in a few packages. Like Merlin and A Fori, but not in their interface. Yeah, same. I think I use Claude more via other products. So like as an example, if you're using Perplexity, you can use Claude 3.5 sonnet. That is my default when I use Perplexity. If you have a paid plan. Yeah. Douglas, we're doing this live. Douglas, we're doing this live. All right, so that is the overview of Claude projects. Before we start doing these live, let's talk about why your company might even want to use these. Well, I kind of already referenced it, but I think the most successful companies in 2025 are going to be the companies that in 2024 started to move their business operations inside of a large language model or that have already started to build on top of APIs. As an example, using anthropic Claude, using OpenAI's GPT technology, Microsoft Copilot, Google, Gemini, etc. The most successful companies in 2024, 2025 are going to be the ones that brought the majority or at least the majority of their time consuming, not like manual knowledge works knowledge work tasks inside of large language models or building on top of APIs, right? So you need to start rethinking how you do work. I talk about that on the everyday AI show all the time, right? Just because your company has been successful over the last couple of years or a couple of decades doesn't mean you will be successful in an AI first or an AI native world. You have to. Which I know sounds weird, right? You have to start unlearning some of your businesses or some of your own personal good habits, which is weird, right? It could be the things that led to you getting a promotion. It could be the things that led that caused your company to be a leader in this space, right? Maybe you hang your hat on doing things the old fashioned way and you know, rolling up your sleeves and printing out hundreds of pages and marking them up. That is not a recipe for success in 2025. That is a recipe for success to get leaped by your competitors. You have to start unlearning what your company and what you have done in years past. And you have to start relearning how to work with generative AI. That is the future of work, y'. All. If you don't already, if, if your company is not already using AI and large language models, you are in for a rude awakening, right? Especially if you are here in the US where the rules and regulations are lax. That is non existent. They're are really no real rules, right? If you're in the eu, you know, there's many more hoops that you have to jump through and many more boxes you have to check depending on, you know, where you are, what country you're from. But hey, here in the U.S. if you are a small, even a small company, 100 employees, if you're a medium sized, you know, enterprise with a couple thousand employees, if you have not already started to move some of your more time consuming operations inside of a large language model or building on top of a large language model, let me be honest here, you're going to be screwed. Yeah, let me say that again. If you haven't already, if you have hundreds or thousands of employees and you are based here in the US and if you haven't already started to integrate generative AI and large language models into your most time consuming manual knowledge work workflows, you are going to get passed. You are asking to go out of business. So that's a reason why your company should probably be using something like CLAUDE projects. But let's talk about some, some simple use cases, right? So market research, analysis, right? Think of all the, all the time you might spend that might be your job. You might be on a team of 10 people in market research or analyzing your competitors, right? You can do this as an example inside of CLAUDE projects, all right? Maybe sales, strategy development, maybe you're spending so much time, you know, gobbling up data from past sales cycles, from past sales and marketing campaigns, you can do that inside of CLAUDE projects. You could do product development, roadmap, right? Collecting user feedback and putting, you know, files of all kinds into CLAUDE projects. It's another thing, we're going to be doing that live here in a minute. It's not just PDFs, right? You can upload documents of all kinds of employee training programs, right? Maybe this is, maybe you have a large HR team and you're spending a third of your time just working on employee, employee training programs, right? You can do that all in CLAUDE projects. Essentially, if you have domain expertise, if you have internal company ip, right? Your secret sauce, chances are it's recorded somewhere. It's in documents, it's in conversations, it's in transcripts, it's in data, it's in sales numbers, it's in website analytics. Think almost everything that makes your company successful or you know, your company's secret sauce, almost every single thing can be turned into data, can be turned into structured data or unstructured data, like I said, transcripts, documents, etc. And then you can use all of Those and build projects out of them, right? An unlimited number of projects inside of Claude. The same way you can build an unlimited number of GPTs. So think where are you or your team spending the most time in these areas? All right, should we do one live, y'? All? Hey, Michelle, don't worry if you jumped in late. We're gonna do this live. Are you guys ready? Yeah. Jason. Jason says quote of the day, screwed. All right. And hey, if you are on the podcast, I'm gonna do my best. Live stream audience, you guys ready to do something live here? We do it every once in a while. Things can go. Things can go awry when you do things live, right? Like if something goes down, there goes the rest of the show. But podcast audience, I'm going to do my best to describe exactly what I'm doing here so you can follow along. But that's why, just check out your show notes. This might be one of those. You just come back and, you know, watch the video on YouTube or LinkedIn. All right, so let's go ahead and build one of these things live, shall we? Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead of and some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on Gen AI. All right, let's do this thing. So I'm going to try to zoom in. Let me know. Live stream audience, can you see? Can you see my screen? All right, so now I am in. I'm logged into my Claude plan. I'm on the professional plan. So that's another thing to know. If you are on the free plan, you cannot build projects, you cannot use projects. It's Another big difference with GPTs. With GPTs in OpenAI, even if you are on a free plan, you can use custom GPTs, you cannot build them, but you could as an example, pay for a month, build 50 GPTs, cancel your subscription, and then use all those GPTs. So I am on the professional plan here because we're going to be building a project and you do even need to be on a professional plan to use any project. All right, so I am logged into claude now. I you go to the side panel here, you click Projects and then we're going to click Create Project. Okay, so what we're going to do here is we need to give the project a name and a short description. Don't worry, this is just for, you know, internal purposes. All right, so what we're going to be doing for this one live, and then as long as I don't go too far over on time, I have another one pre built that maybe is a little bit more applicable. All right, so this is just going to be called AI News Assistant. Okay, so all we're going to do with this, I have more than a hundred pages of essentially AI news in our newsletter every single day. And I'm going to go ahead and just bring this up on screen so you can see this and if you didn't know, you can go back and read every single newsletter that we've ever written. However, every day we have in our newsletter, I'm scrolling down here on screen. We usually have five to seven big news stories here, right? So it's called bite sized news. So here we go. Yesterday there were six stories. So essentially I've gone through the last about four months and I've grabbed all of these different news stories. So there's hundreds of news stories, more than a hundred pages inside multiple documents. I broke the documents down by month. All right, so now let's go back into claude and I have this pre typed out. This is the wrong one. There we go. All right, so now I'm going to click Create Project. I gave it a description, a name, now I'm going to click Create Project. Okay, so there's a couple of things that you can do inside of this project builder. Like I said, it is much more limited than GPTs. However, the Claude 3.5 Sonnet model in Artifacts. Yeah, we're going to be getting to that. This is where I think the secret comes and why it's still worth using CLAUDE projects, even though I personally think custom GPTs are much more robust. All right, so let's start with this. So you'll see here we have a simple interface and right now I haven't done any custom instructions. So if I just start talking with this project, it's the exact same thing as if I'm just talking with the base model. So over here you'll see two different sections. I can add content, this is the project knowledge, and then I can set custom instructions. Okay, so first I'm going to set custom instructions and I'm gonna, I'm gonna read these for you. I don't know if I'm gonna read it all. I'm gonna read a high level overview because it's gonna take a while. So essentially I'm saying for this chat, assume the role of a helpful data analyst and business strategist for the company everyday AI. In short. Oh wait, let me see. Nope, this is the wrong one. That's why we do it. We do it live, y'. All. Sometimes I grab the wrong thing. All right, here we go. All right, for this chat, assume the role of an AI News Research assistant. You are tasked with helping answer the user's queries about different AI news items in your knowledge files. You will have more than 100 pages of news recaps for each user query. You will take your time, go step by step, research deeply and analyze all of your knowledge files in order to give the user a factual, an accurate and well researched answer. And then I essentially say, here's the documents, I label them. So as an example, AI news docs. May 20, 24, 33 pages of AI news recaps. So we have May, June, July and August. And we have all the different pages of these new recaps. And then I essentially say I'm giving Claude some information about, hey, these are in chronological order as well. All right, so now I'm just going to click save instructions. Okay, so now anytime I chat with this project, it is first going to go through those custom instructions and remind itself, oh, this is actually what's happening here. And I also, in the custom instructions obviously call out to the project files here. So I'm going to click add content. Okay. And upload from device. All right, so you pro, you can't see my prompt or, sorry, my window here, but all I'm doing is I'm selecting the files. You can't actually see that in the screen share, but all I'm doing is selecting these files. I, I had all these files in Google Docs, I exported them all as PDFs and now I am uploading them. All right, so now you'll see it's processing the content here. All right, and it is done. One thing I like about CLAUDE projects is it gives you the percentage of the knowledge base size that you, that you can use. So you'll see it's about. And this is not 100% accurate, but I think you can get just under about 500 pages of just documents. So actually pretty decent size of a knowledge base that you can work with. All right, and then from there I am now done. Okay. So now when I want to go use this project, all I have to do is just go inside of my projects. There it is, AI News Assistant. I click it and now I can just chat with it. I don't have to do literally anything else. Okay, so now let's go ahead. And I did build the exact same thing inside of Chat gbt. Literally it is verbatim the same. All right, so we, we're probably going to be toggling between the two. The only difference is I just changed the name. When I said, you know, project knowledge, I just said your knowledge files in ChatGPT, otherwise it is the exact same. So I just wanted to do some comparisons here so everyone can see, okay, are these Claude projects? Good? All right, so let's go ahead and get started. So I have some simple. Let's go ahead and bring the. There we go. I have some simple questions that we're going to ask. Okay. And we're going to start super simple. So I'm starting and I'm just saying please tell me the latest news on large language models. All right, sorry, had a little, little Internet hiccup there. All right, so I am entering this into Claude. So now what's happening is it is going through those custom instructions and presumably it is reading through the document. So it says, thinking deeply, standby. All right, so the thing I like by default, again, this is with zero prompt engineering, right? This is just a very quick, quick and dirty example, so to speak. So I like that Claude tells me exactly what it's doing. So it says first I'll check the Most recent document, AI News docs August 2024, as it's likely to have the most up to date information. All right, I'm going to run this exact same prompt inside my inside GPT that does the exact same thing. All right, so first high level overview. I mean, let's see. So in inside Claude it's saying Gemini 1.5 Pro tops. Chatbot Arena. That's good. Meta Llama 3.1 release good. Mistral's new model Large 2. Good. All right, so did a good job. Let's look at chat GPT. Let's see. Same thing. So it's talking about Meta's, llama 3, Mr. Large 2, OpenAI and copyright issues. Microsoft's new AI model Mai1, that's actually a little bit older, so not super recent. But again, I didn't give it context. Recent could mean anything, right? When I'm just saying, please tell me the latest news. All right, so I think both models there did a pretty good job. All right, now let's. Let's kind of test its reasoning. So this is where project and project files really can, I think, help you? Because what I'm asking, what I'm asking now, it's not tricky necessarily. Right? Let me go ahead and get rid of this little banner here at the bottom so you can see. So I'm saying what happened with Apple in their AI? Again, I'm not being super specific. I'm not giving it prompt engineering right here. I am actually testing CLAUDE Project's ability to not reason, but to think. Think rationally. Because if you know anything about Apple and their AI, from May to August, it's been wild. There was all these rumors in May about what they're doing, what they're not doing. Then they announced it in June and then in, you know, July and August, there's all these setbacks. It's been delayed. They might not be able to use Apple Intelligence, which is what it's called in the eu. So there's been a lot of development. So now I am just openly asking CLAUDE projects what happened with Apple in their AI. Give me a comprehensive overview, but keep it short and factual, please. Do not waste words. Right? I don't want to spend an hour going through a huge product project here. All right, so now I'm sending this to Claude Projects and I'm seeing. Because in theory, I would want it to go through every single month, right? May, June, July, August, we'd have four months of AI news. And I wanted to hopefully go through and like a human would go through and understand what's actually going on. Full disclosure. If a human had no idea about this and to give me a good answer, it would take them probably a couple hours, right? 100 pages. 100 plus pages of AI news is a lot to take in. Claude obviously got the job done in five seconds, right? It's done. So let's see how it did here. So it's starting by talking about Apple Intelligence announcement June 2024 unveiled. So it's Giving me some bullet point headings. So the first heading is Apple Intelligence announcement was in June 2024 integration with a OpenAI, which it says is July 2024. Technically that was in June. So is it right? Yes. Is it wrong? Yes as well. So okay, now we're talking about, you know, Apple gaining a seat on OpenAI's board. That's true. Then in it's talking, the third headline is talking about partnerships from July to August, potential plans to integrate other third party models, correct EU restrictions. There we go. In August 2024 did a good job. So overall, overall it did a really good job. The only thing that I don't think Claude projects did a really good job on is, is really that May to June, which was pretty important because there's a lot of uncertainty, rumors going all over the place with what was going on with Apple intelligence. So overall Claude I think did a really good job. Again, this would take something a human that's uninitiated to what Apple is doing. This would take a long time. Even if you follow, you know, AI and Apple, you probably wouldn't know this even if you read our newsletter every day or listen to the podcast every day. That might be difficult. All right, let's run the exact same thing inside of Chat GBT. Again this is literally verbatim. The exact same knowledge base, the exact same instructions. So let's see how chat GPT and GPTs do again. Custom instructions, custom knowledge base and it's going through. All right, so let's see. So first one it says Apple, Apple Intelligence. Okay, so that's good again. But starting off with the keynote in June 2020, then it's talking about AI partnerships. Same thing you know about potentially Apple working with Meta and Google in the future in addition to OpenAI so nailed that privacy and safety. Talking about some things with the Biden administration, new AI features for Apple intelligence. Let's see here. Yep, this is all correct. So it's talking about new iOS 18 features due to the AI. Then it's also talking about AI chip development. So Chat GPT went a slightly different route I would say. If I was just comparing head to head again, this is with a terrible prompt, y'. All. This is open ended, right? If I had something a little more pointed in the questions that I was asking, I think I could do a real legitimate like Apples to Apples comparison. And hey, live stream audience, let me know if, if you can still, still hear and see. I, I, I think I'm having some Internet issues here, so hopefully you can But I think they both did a good, good enough job. Right? I'm not going to say one failed, one's better than the other. If I had to choose, I would say claude. The CLAUDE project is slightly better. But again, this is an open ended question. Not great prompt engineering. I'm not asking something super specific. All right, now we're just going to do a slight kind of needle in a haystack test. Okay, what that means is, let's see in one of the files here. All right, so this file is foreign. Page, let's see, 20 of page 31 for this month. It's talking about OpenAI's new CFO and CPO. So essentially this, I'm asking one very specific question that is buried in this project knowledge. So more than 100 pages of documents. I'm asking for an answer that can only be found one place. I'm not telling it where to look. This is another thing. You should always be doing needle in the haystack tests. When you're using, whether it's CLAUDE projects, whether you're using GPTs and chat GPT, you always need to be testing it because you need to write trust and transparency when working with models is important. You should always be doing ongoing testing to make sure these models are correctly pulling in information. All right, so let's go ahead and test this out in CLAUDE project. So a very specific question and I'm not telling it where to find. So it's saying to answer this question accurately, I'll search through the documents chronologically, focusing on news about OpenAI's executive appointments. All right, so it got to it pretty quickly, right? So there's the answer. CFO Sarah Fryer, CPO Kevin Whale. All right, so pretty good job, Claude. CLAUDE found it pretty quickly. All right, so now let's ask the same thing again. Doing a little needle in the haystack test with the custom GPT. So you'll see right away it's saying searching my knowledge. I asked the exact same question and very quickly, Chat GBT found it as well. Said appointed Sarah Fryer as the CFO and Kevin Whale as the cpo. All right, so there you go. There is a very quick overview at least of basics. Now I want to do something, I want to do something a little more in depth, something that is maybe a little more relevant for you or your business because you might be thinking, all right, why would I want to use this as a research assistant? Right? I could just do that on the Internet. I could just use Perplexity etc. Okay, cool. Well, that was just an easy way to look at it. All right, so now I also created GPTs. Well, actually, let, let me make sure. I think I created a GPT for this. Or maybe I just. Okay, I did. Cool. All right, so now we're doing the exact same thing, except instead of an AI research assistant, here's what we're doing. I uploaded all of everyday AI's stats into, into this new project. We're not going to build this one live. It's already built. All right, so essentially what we have going on here, I'm looking at the custom instructions and again, it's the same for Claude and ChatGPT. But I'm saying for this chat, assume the role of a helpful data analyst and business strategist for the company. Everyday AI. I give a little bit of information about Everyday AI. We're a daily newsletter, podcast, live stream, helping people grow their career. Then I'm saying, in your project knowledge, you have a set of CSV files. These files are exported from everyday AIs different platforms. And then I'm essentially telling it, here's the data that you have. You have Google Search Council data. So this is essentially how people are finding everyday AI on the web and what pages they're going to email data, which is a big part of what we do here@everyday AI. Yeah, maybe you listen on the podcast. I think our newsletter, if I'm being honest, that's probably our best platform. It's written by me, a human. It is. You know, there's dozens or hundreds of AI newsletters out there. We're the only one that brings you every day exclusive insights that you can't find anywhere else. Because we recap our podcast every day. And our podcast is unique and we bring on experts from across the world. So right here I'm giving the chat, or sorry, I'm giving Claude projects access to all of our email data. Not your email addresses. Don't worry, we didn't put that in there. This is open rates, click through rates, trends, etc, Right. We have a lot of data. So this is our data from Beehive, that is our email service provider. And then podcast data. So I'm putting in there. So I'm saying these are stats from our podcast. We use buzzsprout to distribute our podcasts on all match your major platforms, you know, Apple, Spotify, all these other places. So now essentially I'm saying for this task, you will answer the user's queries by always first analyzing the correct file. So this is another thing, right? I'M telling Chat GP or, sorry, I'm telling Claude projects. Hey, depending on what the user asks you, you need to find the correct file. You know the information you have. You have some website and Google data, you have some email newsletter data, and you have some podcast newsletter data. So if the user is asking about one of those platforms in particular, you need to go and make sure you look at the right data. All right? And then, and then I'm also saying when the user asks you to use artifacts, always use the artifacts feature to try and best visualize the data that the user asks for. More on that in a minute. And I did the exact same thing inside of Chat GPT, but instead of artifacts, I said advanced data analysis. All right, so that's it. So again, think of your company, right? Think of your company and how you could use something like this. Again, I'm fine giving all of this data to a large language model. I have paid plans of Claude and Chat GPT. I turn off model training, all right? So I'm not too worried about it. Don't, like I said, don't dump in a bunch of pii phi confidential things unless the person in charge at your company is like, oh, yeah, we're on an enterprise plan. We can do that. All right? But I've uploaded all of this data. There's a good amount of data. I would say we have hundreds of thousands of data points, all right? And we've used 73% of our knowledge file here in Claude, so actually a ton of data. All right? So think, think. Maybe you work in data or you work as a business analyst. This is a good chunk of what you would be doing at your job, right? Conversely, let's say maybe you don't have a dedicated data analyst. You don't have someone working in business intelligence. But you would love, right? You know, you have access to all this data and you would love to do something with it. Here's a great example. Dumping all of your analytics, all of your data into a single project or a single GPT, and then just being able to have a conversation, right? So this is literally as if you were to hire, as an example, a data consulting company, a business intelligence company, a digital consulting company, right? Because you're like, man, we have mountains of data and we have no clue what it means. This is what you used to have to do. Guess what? Large language models are fantastic at this. They are so, so good, especially with artifacts and advanced data analysis. So I'm, I'm excited for this one. Enough chit Chat Y', all, let's, let's jump straight into it. All right, so let's go ahead and run some general prompts here. Okay, so what I'm saying, and I'm going to zoom in just a little bit. I'm saying tell me the 10 most impactful trends that I need to know in order to grow everyday AI. I am intentionally being vague in general about this. I always tell people when you start with large language models, when you're having a conversation, right? The other thing about this is I'm going to be chatting with just this one project. So as I have a conversation with the project, the same thing applies for the GPT. All of the conversation and all of the insights that we unearth, they're going to be in the context window of the conversation. Right? Just like a human, when you sit down and ask a human questions, presumably the human is going to be able to remember what you're talking about. Unlike if you're using like a Siri or an Alexa and you ask one question and then you ask a follow up and it fails. That's not how at least AI smart assistants, voice assistants work. Large language models work iteratively. Right? So you can ask general questions and then ask follow up questions without the need to clarify. Right? So again, think of you are chatting with a team full of of status statisticians and business analysts, right? So I like to start general and I like to see how good is the model at finding some of these things on its own without me pointing it in the right direction. So I'm not even saying go look at the podcast, go look at the newsletter, go look at the website. I'm just saying find me trends. Okay, so there we go. And then I'm going to run this exact same prompt inside chat GPT. So if you join on the podcast, I'm just doing a bunch of window flipping right now. All right, so Claude projects right now says pondering standby. This one might take a little bit of time because it can go in a number of different directions. As an example, CLAUDE just might give me a bullet point list or it actually might start building graphs using the artifacts feature. It will go different way each time. All right, so right now it's saying, yeah, so you'll see. Now it's using the artifacts feature. So if you are brand new, I love the artifacts feature. Right. If it wasn't for the artifacts feature in Claude, I wouldn't use Claude if I'm being honest, because I think the GPT4O model is better chat GPT has Internet connectivity. GPTs are more flexible and robust. But Claude has artifacts, right? So essentially, to talk very plainly, right, it can not just run code, but it can execute it. So it can run and execute HTML, css, Python, right? So in theory, with the artifacts feature, you can literally build applications and not have to go, you know, interactive applications as well, right? And not have to go into a third party platform or, you know, have your own web hosting. You can literally build business dashboards, interactive things inside the artifacts feature with GPTs, it has advanced data analysis and you can do interactive graphs and charts, but not like dashboards. Okay, so all that used artifacts for right here was to essentially just give me a list, right? So it didn't actually, you know, run any code or anything like that. So another thing is I can add this to the current project, right? So I can click that. And now what this just created is now in that knowledge base, right? But if I keep talking with this particular chat, it will stay there. However, I can start a new chat with this same project. And now that information that Claude just gave me will stay in the project knowledge, which I like that. All right, so I'm just going to go over there very, I'm going to go over these very, very quickly. All right, so Claude says, number one, mobile Optim. Mobile optimization is crucial. It's giving me some mobile versus desktop as well as action. Right? So maybe I'll just go through the first one and then I'll just read off some of the rest. So essentially it's saying mobile devices accounted for a significant portion of engagement. 24% of clicks came from mobile devices. It has a higher click through rate compared to desktop. All right, so essentially it's saying make sure the action is giving me is ensure all your content is mobile friendly, including emails and website designs. Because it's telling me the majority of your engagement right now for everyday AI is coming from mobile devices. All right, so then it's also talking about email marketing effectiveness, geographic expansion opportunities, podcast growth potential, right? So it's giving me a lot of good information that I'm going to read later. All right, let's jump over into Chat GPT. So one thing I love about Chat GPT is the new Advanced Data Analysis V2 is crazy. It is so good. And they actually just two days ago updated even the V2 of advanced data Analysis. So again, it can't do everything that Claude Artifacts can do in terms of, like I said, you know, it can render code, right? It can render HTML, it could build A literal website and show you how the website works. ChatGPT can, can write code, but it can't render the code unless it is, you know, spreadsheets or you know, essentially interactive tables, datas, data visualization. Right, but it can't create like websites or business dashboards or apps. Right, you can literally create apps, web apps, actual apps, games, inside of Claude artifacts without even having to take the information anywhere. So one thing I love about GPTs, that I think it does a better job than CLAUDE is for the most part it shows you its work. Right. So right here I'm getting a breakdown of exactly what it's doing. It's telling me step by step what it's doing. It says, I'm loading, you know, I'm loading and analyzing this file and then it's telling me what it's looking at. It's, it's giving me the Python code. So I can actually go and see, you know, I can read basic Python, I'm not great at writing it, but I can literally see what it's doing step by step under the hood. This is fantastic. All right, so let's go ahead. I'm going to do the same thing. I mean, look at this from chat GPT, it's, I love, I, I love the way that GPTs and advanced data analysis show you the work. All right, so I'm gonna, I'm gonna read the number one and then just kind of go through some of the rest. So number one, this is in the GPTs again, we're just doing some comparisons. It says high open and click rates in emails. So it says certain email subjects, like, you know, this is actually the one from Yesterday. Amazon's Titan V2 image model is here. Show exceptionally high open and click rates. And then it gives me action. So interesting. It actually gave me GPT and Claude projects, gave me a very similar format. It essentially gave me an insight and then an action, even though I didn't ask it to. Right. So the action here continue to create engaging and intriguing subject lines and content similar to these successful emails to maintain high engagement rates. All right, so there you'll see, chat GBT gave me, you know, number two top performing URLs and email three effective subscriber acquisition sources, four popular search queries drawing traffic. Right. High performing pages. Right. Just looking at these eyeball test, I actually like the, I like the insights from the Chat GPT a little bit more. Right. So let's just say as an example, right, so number four and five, it's a little more Specific from the GPT. So it's giving me popular search queries, driving traffic and high performing pages. So it's giving me very specific and actionable insights around SEO. Right? Whereas in Claude projects it's a little more vague. It's just saying like search engine optimization focus. Right? So it's giving me kind of broad, which is okay because I didn't ask for specifics, but I, I like a model's ability to. Because if I'm asking it for impactful trends, just telling me to focus on SEO, not great, not very impactful. All right, let's go ahead. We're going to run a couple other prompts here, all right, and we're going to go through these pretty quickly because I know this, this episode is running a little long, but I wanted to give everyone really a good idea of Claude projects, but also to compare it to chat GPT. All right, so now for this one, I'm saying please give me 10 areas where I'm trending in the right direction and 10 areas where I'm trending downward. Right? Focus on big wins or potential big losses. Be creative and strategic in your approach, be ultra specific in your result, but do not waste words and be succinct, factual and creative. All right, so again, similar, I'm not asking for specific, oh, give me this from the podcast, give me this from Google Search, but I'm saying give me 10 areas where things are going very well and give me 10 areas where things are trending in the wrong direction. So presumably what this requires of Claude projects and also GPTs is to do some reasoning, right? To do some logic work. Because like I said, there are hundreds of thousands of rows or, sorry, hundreds of thousands of cells of data in this and that's a lot of work, right? Again, if I were to hand this off to even a company that specializes in this, number one, it's going to be expensive, very minimum to play to play ball with these type of companies. Five, six figures easily, right? Just to be able to start working and, or start engaging. Look at this. This is good stuff. This is good stuff. And this is first, very first question. I could do 10 follow ups and really drill down. All right, so Claude did some great work here. So give me 10 positive trends. So trending positively, podcast growth, email open rates, mobile engagement, inter, you know, international expansion. Good stuff. Areas of concern, tablet engagement. Okay? Geographic gaps. So, okay, it's like, all right, saying you're not getting engagement from Japan and South Korea. Podcast download consistency, that's normal. Email click rates all right, so it's giving me some essentially pros and cons. There's, let's look at chat GPT, the custom GPT here. So things that are trending in the right direction. High email open rates, top URL click throughs, subscriber growth from specific sources. All right, some of the downsides here, let's look. Stagnant open rates on some emails, declining click through rates, underperforming subscriber sources. All right, so I, again, I think both of them pass the test here. All right, so I think CLAUDE projects and custom GPTs did a great job, right? And again, I would want to have much more information and much more depth, but I specifically said, yo, smart AI models give me high level stuff here, right? Because then presumably I would then be having a conversation based on what they say and really drilling down into those topics. All right, we're going to do about one and a half more things here. All right, so now here's where we're really going to start separating what's currently capable in Claude projects versus GPTs. All right, so now what I'm asking is, I'm saying based on the data that I have, please, please give me an idea of five data visualizations or dashboards that you could build for everyday AI. That would be the most helpful. Yeah, here's where we're going to start to shine. I saved the good stuff for last. Right? If you're here after 55 plus minutes, this is where we put the gems, y'. All. All right, so what, what CLAUDE is actually doing right now for our podcast audience, it didn't. Which it's like, okay, can I really be mad at Claude for doing this? So I asked specifically for ideas of data visualizations and CLAUDE just essentially said, yeah, you know what, I could give you ideas. So it gave me five ideas, but it went ahead and just built one of them. Right? It built me. Let me zoom out a little bit here. So it built me literally an interactive dashboard for podcast performance tracker. I love this. So it's giving me episode numbers and then it's giving me the number of downloads for our last couple of episodes, for our most, some of our most recent episodes hosts, y'. All. That's, that's pretty, that's pretty amazing, right? Again, this is an interactive dashboard. Simple, right? And all I did was say, hey, give me five ideas. Right? And you can really build inside CLAUDE projects with the artifacts feature. Some very impressive data visualizations and interactive dashboards. And here's the thing with CLAUDE projects, you can then Share these with your team. I didn't even get to that. I wish Claude would change their rules a little bit more because you have to have minimum, you have to pay for five seats essentially for their team plan to then be able to share these with your team. So you're talking about at $30 a month or maybe it's $20 a month, it might be more for the team plan. Yeah, I think it's $30 a month. So I think you're thinking It's a minimum, $150 a month in order to be able to do this and share this with your team. Where with ChatGPT the minimum is just two users for a team. Right. And then again, you can share GPTs though with even free users. But this is pretty impressive. All right. This podcast performance tracker that Claude just literally went ahead and built. Very impressive. All right, so what I'm going to do and I'm going to do this for each of them, I'm going to have them essentially try to build me one in more depth and detail. So I'm, I'm for quad projects asking. I like this idea that it said SEO performance analyzer. So I'm just going to say please build number four in artifacts and I'm going to say making it more in depth, detailed and useful. All right, so very, very broad, you know, instructions there. So let's see actually what it does. Does. All right. Then we're going to jump over real quick to Chat GPT. So again, it gave me five different ideas for things that it could visualize. So advanced data analysis or code interpreter as it's sometimes called Inside ChatGPT is a little different. It can visualize data, it can plot things, create charts. It can't create interactive dashboards. Right. It can't create apps, it can't create websites and render them. It can write the code, but you can't actually see them. So I'm going to go through here and I'm going to say, okay, same thing. So for number one, it said email campaign performance dashboard. I don't think Chat GPT is going to be able to build that the best. So it's giving me a lot of dashboards. Right. Which is fine because that's technically what I asked for. So first I'm going to do a follow up questions and I'm going to say, I'm going to say which one of these would be best as an interactive chart or data visualization? Okay. And then what I'm going to do is I'm going to choose One of these just like I chose one in Claude projects. And I'm going to have chat GPT via advanced data analysis code interpreter, build one of these. Okay, so it's telling me subscriber growth and acquisition dashboard. So it's still saying a dashboard, which I didn't necessarily want. So I'm gonna, I'm gonna say I don't think this is going to work very well. I'm gonna say, please, I'm going to say using advanced data analysis. I hate typing live, please build this interactive. I'm going to say chart, because it cannot technically build a dashboard. It can build interactive data visualizations. So I'm going to say, please build this interactive chart or data visualization. I'm going to say, if you're not able to, please reformat this idea in a way that you can visualize the data using advanced data analysis. All right. I hate typing live because with my setup here, I got cramped dinosaurs arms. All right, so let's see. And this is, this is the last thing we're doing, y'. All. I know this has been a longer episode. I hope it's been helpful though. All right, so now let's go ahead and jump in. Let's see how Claude did. Wow, this is impressive. Okay, so again, as a reminder, I asked it to build. It gave me five suggestions. I like this one of an SEO performance analyzer. So I said, hey, go build that and you'll see using Claude projects. It did it. So if you're here on the live stream, y', all, this is, this is pretty impressive here. So again, it's giving me different terms, right? So it's giving me different terms that have brought in a high number of clicks and a high number of impressions. So it's a pretty simple data visualization. Right? But the good thing is. Oh, wait, it built actually five of them. Interesting. Okay, so it built me a series of different interactive graphs. Man, cloud artifacts is great. So here we have a simple bar chart for top performing queries. Again, I can hover over them and it's giving me more information. Now it's doing a little scatter plot for click through rate versus average position. This is super helpful, y'. All. This is some stuff I didn't even know. So I can, let's see if I can hover over. Okay, so it's not naming everything. So the first shot on this, not super helpful. But I would probably go through and iteratively say, hey, I'm not getting, I'm not getting labels on these, you know, average Positions and click through rates. That's okay because that's an easy fix. So now it's saying device performance comparison. So showing me, let's see clicks and click through rate. So okay, I didn't even know this. So as an example, okay, this is interesting. Much, much higher click through rate on mobile than we do on desktop. So we have more, way more overall clicks on desktop for our Google search console, but a way higher click through rate. So that's, that's pretty interesting. There's. Geez, I mean y' all live stream audience. Are you guys impressed with what again, this is inside of a project so I could share this with my team, but it's using the artifacts feature. This is now. Geez, this is good. Okay, an interactive chart here. It's a little line graph and I can hover over and it's showing me impressions and clicks by day. Very, very cool here. So it's giving me the number of clicks or impressions by day. Very cool. Now it's going over to top performing pages here again. So Claude literally just built me five interactive graphs and dashboards, right? And in theory, if I wanted to, I could click this code button, I could copy this and I could put this in a live production website where I didn't even need someone to be logged into. Claude, that's the other thing. It is both giving me the code if I wanted to do something and build something with it, but then also I can just grab it. All right, so let's see, all right, let's see how Chat GPT did. This is a lot, right? Showing me its work. A lot of Python. Here it is gobbling up data. So let's see what it actually built. So again I asked it, let, let me go back up here real quick. So I said, please build this interactive chart. And the one that it gave me was the one for subscriber growth and acquisition Dashboard. All right, so scroll down here. Let's see what it did using Advanced Data Analysis and Code Interpreter. Lot of data here. A lot of data. Okay, so you'll see right here it gave me way too much data, right? But it's also extremely impressive that it brought all this in. So here's what I'm going to say. I'm going to take a screenshot of this, right? I'm going to drop this screenshot into Chat GBT and I'm going to say this is too much data and I can't read the labels and I'm going to say please maybe only focus on 10 so I can Easily read all of the data, right? So that's another thing. So these models can see so I don't have to go through and be like, huh, let's say you're using this to build a business, a dashboard for your team, or an interactive graph that you can share with someone. And you're like, how am I going to troubleshoot this? There's, it looks like hundreds or maybe thousands of different data points on this chart. How can I work? Well, upload a screenshot, be like, yo, this is all jumbled. It's going to see, it's going to understand. Oh yeah, I put way too much information on that chart and you can't make it, you can't make use of it, right? So now it's going through and it's recap. And now look, now we have a, a pretty, pretty beautiful, nice little chart here inside of Chat gbt. And then the, the thing that I love is a lot of the different graphics within Chat GPT and data analysis. You can just chat with certain things. So it's showing me top new subscribers by top 10 acquisition sources for our newsletter. So now I could continue. And it's a nice looking graph. So it's not inner, it's not as interactive as the one from CLAUDE projects. But again, y', all, I just showed you in live, in real time how powerful cloud CLAUDE projects are, right? Again, that data, those visualizations, those dashboards, y', all, especially with CLAUDE artifacts. This was the fact that this is capable and available to you if you pay for a, you know, 20amonth Claude Pro subscription. That's wild. Right? Again, to either to get this type of data, to get these type of insights, to get these types of visualizations and dashboards. A year ago it would take you a lot of money, a lot of time. And now look, y', all, I did it live on a podcast with very little effort, right? You saw that we didn't have to put in super structured prompts and go through all this prompt engineering, y'. All. If this didn't just show you what large language models are capable of, I feel sorry for your company. I feel sorry for your career if I'm being honest, right? Think of all the publicly accessible data that you have, right? Here's the thing, y', all, you don't even need permission to use data that's publicly available. So many companies don't even know how much data they have publicly available, right? Companies that are public, they have to put out their 10Ks. Think of all these interactive dashboards, all of these insights. As an example, here's a company's, you know, 10k financial reporting for the last five years. You know, give me detailed breakdowns and trends in a dashboard that's easy to use. Bam. Think of all the different applications for this and how easy it is, y'. All. This is both. Well, if I, let me be honest here, I think there's equal amounts of excitement and there's equal amounts of like, okay, doom, Let me explain what I mean. If you are not excited by what I just showed you, you gotta, you gotta check your pulse. If I'm being honest, right? This level of business intelligence, of automation, of AI doing the grunt work, this is truly mind boggling. I say this because I've spent, right, I've spent, as an example, probably 20 hours going through pre generative AI and putting together a very rudimentary version of what you just saw, right? I would go through, I would read long PDF reports, grab all this data, try to put it into a little graph. It would look very ugly and it would take me like 20 hours. Do this in, in 20 seconds. This changes. So from a, from an opportunity. If this, whether you're an entrepreneur, whether you're leading your department, if this just didn't make you think that I should change the way that I'm working, that our company should, should really take large language models seriously. On the flip side of that is doom if you're not. I'm being honest. If your company is not already building on top of these APIs for large language models, if you're not already using them in some way, shape or form, if you are not personally going in here, even paying, right? Oh, my company won't pay for it because we don't. Okay, well, pay for it yourself. Pay for it yourself and use publicly available data and information. This changes what humans are capable of, it changes how business operates and it completely rewrites the business future. If you are not using large language models, if you're not using cloud projects, if you're not using GPTs, you are putting your own career, you're putting your own company at risk. You shouldn't do it. All right, y', all, I hope this was helpful. If you're still listening, share this episode. I got a little something special for those of you that do share this. So if you're on Twitter, yeah, I think you can like retweet it or whatever. If you're on LinkedIn, click that repost button. All right, I have, I. I'm going to record a video on Some even more specific examples and use cases that I think are great for Claude projects and for GPTs. All right, so go ahead if this is helpful. If you're still listening, click Repost on this. If you are on the podcast and you're still listening to this, go check your show notes. I always leave a link to the LinkedIn kind of live stream for this. Go click that. Repost. I'm going to be creating a video going over some more specific use cases that I think really have the potential for explosive growth. So I'm going to be going over some of my favorite use cases for Claude projects and for chat GPTs. GPTs. All right, so repost this. If this was helpful, I'll get that over to you probably early next week. I got got a little stuff going on the rest of today, so give yourself some time. Go repost this. But if you're listening now, do it live. Also, if you're listening now, you need to go sign up for our Thanks a Million giveaway. If Everyday AI is helpful for you, all you have to do is sign up. It'll be in the newsletter. It's on our website. If you, I think, I'm pretty sure if you refer like three friends, you're in first place. So you know, it's essentially a referral giveaway. You just sign up, you get a link, you send that to your friends. If you're. If your friends sign up for the Everyday AI newsletter, you earn points. And like I said, I think if you refer three friends, you're in first place. So we're giving away a year of Chat GPT or a year of Claude Anthropic. Right? So a personal paid subscription for a year to your favorite large language model. And we're going to be talking about a lot of other prizes. One of them will be a one on one consult with me. So if you find Everyday AI helpful, if you want to, maybe what you saw today, you are wowed by it. But you don't really want to pay 20 or $30 a month. That's cool. We will pay it for you. So go sign up and refer friends to our Thanks a Million giveaway and we'll see you back tomorrow for more Everyday AI. Thanks y'. All.

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