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Harnessing the Power of Artificial Intelligence with Claude Projects
In the fast-paced world of AI technology, business owners and decision-makers are continually seeking innovative solutions to streamline and enhance their operations. Among the most recent developments is Anthropomorphic Claude's latest feature, Claude Projects, an extension of the highly-capable, large language model Claude 3.5 SONNET touted as a potential frontrunner against OpenAI's GPT-4.
Introducing Claude Projects
Claude Projects has emerged as a game-changer for tech-forward businesses. Supporting the creation of customized versions of its large language model, the functionality allows for specific task-oriented usage resulting in increased efficiency and precision. In a world where customization is key, Claude Projects promises to deliver.
The Practical Implications
Businesses utilizing Claude Projects can better leverage the potential of language models. The feature creates an opportunity to generate powerful visualizations and insights at an affordable cost. Despite its lack of real-time Internet connectivity and limited usage in both free and paid plans, Claude Projects compensates with its ability to create SEO performance analyzers, business dashboards, apps, and more, becoming a preferred choice for power users and businesses.
The Comparative Analysis
While both Claude Projects and ChatGPT can successfully locate specific information and provide a summary of tech news ranging from AI announcements to EU restrictions, they fall short on the comprehensive coverage of May to June. Despite these similar limitations, each system shows promising capabilities in data analysis.
Businesses implementing large language models can greatly benefit from AI research assistants for business analysis, assisting with email campaign performance and subscriber growth tracking. It's worth noting that while GPT can create charts and data visualizations, it struggles with interactive dashboards - an area where Claude Projects excels.
The Business Implication
Utilizing AI technology allows businesses to transform their operations completely significantly. Powered by automation and large language models like Claude Projects, firms can overhaul their business intelligence systems, enhancing decision-making and strategy development. Ignoring the possibilities that AI provides could put business growth and longevity in jeopardy.
Preparing for the AI Future
The future of AI seems promising, with an increased focus on tailor-made applications like Claude Projects. As AI advancements continue to unfold, it's imperative for businesses to adapt and utilize these technologies in their operations for increased productivity and long-term success.
With AI's increasing presence and the potential it presents, it's crucial to remember that any process that is converted into data can be optimised through AI systems. If teams are spending large amounts of time on tasks that could be automated, business owners should consider implementing AI solutions to maximize efficiency.
Whether it's creating advanced market research analysis, developing sales strategies, or designing product development roadmaps, integrating generative AI and large language models into daily operations can drive success in an AI-centric world.
AI is no longer just a buzzword; it's a business necessity. With the advent of platforms like Claude Projects, the way businesses operate is set to change. From customizing large language models for specific tasks to creating powerful visualizations, the opportunities seem endless. As the technology continues to evolve, the extent to which AI can improve and streamline businesses will only continue to increase.
Topics Covered in This Episode
1. Overview of Claude Projects
2. Practical Use Cases of Projects
3. Claude Projects vs GPT-4
Podcast Transcript
Jordan Wilson [00:00:16]:
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 gonna be going over Claude projects today, what they are, and even building 1 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. 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 gonna be answering all of those questions and giving you, I think, some very practical and real use cases live on today's show.
Jordan Wilson [00:01:34]:
Alright. So before we get into that, we're gonna start as we do every day by going over the AI news. And if you are new here joining on the podcast or the livestream, 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 as well as more depth on today's episode. Alright. Let's take a look what's going on in the AI news.
Jordan Wilson [00:02:00]:
Alright. 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 Open AI 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 GPT 4. 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.
Jordan Wilson [00:02:58]:
Yeah. This one's interesting. More AI and even from OpenAI making its way to the defense industries. Alright. Next, French startup and a large language model maker, Mistral, has an has announced major updates hours ago, highlighting the introduction of customizable agents and advancements in La platform. Alright? I don't know if that's how you say it in 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 LeadChat or via the API to create custom behaviors and workflows.
Jordan Wilson [00:03:42]:
With the advanced capabilities of Mistral large 2, their newest and very capable model, developers can build increasingly complex work flows 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. Lot platform now supports the customization of flagship models like Mistral Large 2 and Code Stro, also from Mistral, obviously. And developers can tailor these models using base prompts, few shot prompting, or fine tuning even with personal datasets. So, yeah, very similar to what we're gonna 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.
Jordan Wilson [00:04:43]:
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. So, 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.
Jordan Wilson [00:05:41]:
Alright. So keep an eye out. If it does break today, we'll obviously be covering it here on OpenAI, or or sorry, here on EverydayAI. But, you know, it's it's it's hard to tell with these things. However, OpenAI did actually tease and release its last model, GPT 4 o, 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 1,000,000,000 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.
Jordan Wilson [00:06:30]:
Alright. 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.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 gonna be looking at how these new Claude projects compare to, GPTs from OpenAI.
Jordan Wilson [00:06:53]:
Alright. So here's what we're gonna be going over. We're gonna be going over, anthropic claud overview. So if you are brand new to anthropic claud, we're gonna be giving you the high level. Don't worry. Because yeah. We talk about Chad GPT a lot, on the show, but if you don't know clog, don't worry. We're gonna go over an overview of that.
Jordan Wilson [00:07:10]:
We're gonna go over a projects overview, and then we're gonna say why your, you or your company might want to use, Enthropic Claude's new projects feature. We're gonna 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 OpenAI. We're gonna briefly go over the pros and the cons as well as look at some of the outputs. Alright.
Jordan Wilson [00:07:43]:
Livestream audience, thanks for tuning in. Appreciate y'all as always. So, Kathleen joining us. Thank you. Jennifer, good to see you back. Zane and Jason and Gordon, everyone else. Big Bogeyface joining on YouTube. Cecilia, Tara, let me all let me note.
Jordan Wilson [00:08:01]:
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. Alright. 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.
Jordan Wilson [00:08:25]:
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 ChatGPT and the GPT technology. However, Anthropic was founded by former, OpenAI executives. We talked 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 GPT 4 o.
Jordan Wilson [00:09:07]:
I would say not so much. I still personally think GPT 4 o is better, but that's up for, personal taste. Right? When you look at benchmarks for the most part, OpenAI's GPT 4 o is still leading, at least 4 products that are available for consumers on the front end. Alright. So that's a super high level overview of anthropic Claude. It is backed by, Amazon in a huge way. I believe a $4,000,000,000 investment. And I do think that they are recruiting, anthropic is some of the top talent in the world.
Jordan Wilson [00:09:39]:
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.
Jordan Wilson [00:10:44]:
At least when you compare them to open AI and ChatGPT, 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.
Jordan Wilson [00:11:17]:
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, studies from, you know, McKinsey Digital that say, you know, 60 to 70% of manual knowledge work is gonna 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 cloud 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 4, 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.
Jordan Wilson [00:12:26]:
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 anthropic overview or sorry, an anthropic clawed overview, it's important to point out it doesn't have access, right, to the Internet, in real time.
Jordan Wilson [00:13:24]:
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 dotai. Right? That's the, the the address. If if you wanna 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. Alright. Yeah. Michael, I I I agree with what Michael said here, in the comments.
Jordan Wilson [00:13:57]:
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 I believe it's it's 30 or so messages every, 5 hours. I could be wrong on that.
Jordan Wilson [00:14:21]:
It might be 50, but the limits are so low. Obviously, on ChatGPT, we have a team's 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. Alright. Yeah. A lot of people a lot of people on, on the livestream here loving Claude. You know, Darcy says she loves it.
Jordan Wilson [00:14:46]:
Zane says, loving it. Gordon, same thing. Yeah. But the limits the limits are bad. So, hey, if someone from Infropic 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 2 of the main reasons you shouldn't be using it, at least if you're trying to use it as a front end user.
Jordan Wilson [00:15:10]:
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 wanna get started by logging on to the front end of, you know, a claw.aiorachatgbt.comorageminI. 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. Alright. So now that we have our Claude overview out of the way, let's talk, basics on what these new cloud cloud projects are. Okay.
Jordan Wilson [00:15:50]:
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 task 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 chat g b t or Claude. So, we're gonna 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.
Jordan Wilson [00:16:32]:
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.
Jordan Wilson [00:17:12]:
Right? You can essentially it's 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 on 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 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.
Jordan Wilson [00:17:59]:
Don't put any of that into a large language model unless, you you know, someone in charge of your company says, yes, we can do this. We've checked with, you know, legal compliance, whatever. You know, all all laws, right? Laws in the EU, etcetera, 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'd probably wanna be using the most powerful version, which is Claude 3.5 SONNET until, you know, they release a 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. Alright. And, hey, let me know live stream audience.
Jordan Wilson [00:18:44]:
We're gonna get we're gonna get building here. But I wanna know from from you all, are you using quad 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 Aphori, but not in their interface. Yep. 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.
Jordan Wilson [00:19:14]:
We're doing this live. Douglas, we're doing this live. Alright. 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 Cloud, using, OpenAI's GPT technology, Microsoft Copilot, Google Gemini, etcetera. 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.
Jordan Wilson [00:20:18]:
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 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 that 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.
Jordan Wilson [00:21:00]:
That is a recipe 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, I e nonexistent. There are really no real rules. Right? If you're in the EU, you know, there's many more hoops they have to jump through and 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 US, if you are a a small even a small company, 100 employees.
Jordan Wilson [00:21:43]:
If you're a medium size, you know, enterprise with a couple 1,000 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 gonna be screwed. Yeah. Let me say that again. If you haven't already, if you have 100 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 cloud projects. But let's talk about, some some simple use cases.
Jordan Wilson [00:22:29]:
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. Alright. 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.
Jordan Wilson [00:23:00]:
You can do that inside of clogged 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 gonna be doing that live here in a minute. It's not just PDFs. Right? You can upload documents of all kinds. Employee training programs.
Jordan Wilson [00:23:22]:
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.
Jordan Wilson [00:23:51]:
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, etcetera. And then you can use all of those and build projects out of them. Right? An unlimited number of projects inside of Cloud. 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? Alright? Should we do one live, y'all? Hey, Michelle. Don't worry if you jumped in late.
Jordan Wilson [00:24:33]:
We're gonna do this live. Are you guys ready? Yeah. Jason Jason says quote of the day screwed. Alright. And, hey, if you are on the podcast, I'm gonna do my best livestream audience. So 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.
Jordan Wilson [00:24:50]:
But podcast audience, I'm gonna 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. Alright? So let's go ahead and build one of these things live, Shall we?
Jordan Wilson [00:25:51]:
Alright. Let's do this thing. So I'm gonna try to zoom in. Let me know, livestream audience.
Jordan Wilson [00:26:15]:
Can you see, can you see my screen? Alright. 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.
Jordan Wilson [00:26:38]:
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 gonna be building a project, and you do even need to be on a professional plan to use any project. Alright. So I am logged into Claude now. I you can go to the side panel here. You click projects and then we're gonna click create project. Okay. So what we're gonna do here is we need to give the project a name in a short description.
Jordan Wilson [00:27:10]:
Don't worry. This is just for, you know, internal purposes. Alright. So what we're gonna be doing for this one live and then as long as I don't go too far over on time, I have another one prebuilt that maybe is a little bit more applicable. Alright. So this is just gonna be called, AI News Assistant. Okay. So all we're gonna do with this, I have more than a 100 pages, of essentially AI news in our newsletter every single day.
Jordan Wilson [00:27:37]:
And I'm gonna 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 5 to 7 big news stories here. Right? So it's called bite sized news. So here we go. Yesterday, there were 6 stories. So, essentially, I've gone through the last about 4 months and I've grabbed all of these different news stories. So there's hundreds of news stories, more than a 100 pages, inside multiple documents.
Jordan Wilson [00:28:10]:
I broke the documents down by month. Alright. So now let's go back into Claude and I have this pre typed out. This is the wrong one. There we go. Alright. So now I'm gonna click create project. I gave it a description, a name.
Jordan Wilson [00:28:25]:
Now I'm gonna 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 gonna be getting to that. This is where I think the the the secret comes and why it's still worth, using Claude projects even though I personally think, custom GBTs are much more robust. Alright.
Jordan Wilson [00:28:52]:
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 2 different sections. I can add content. This is the project knowledge and then I can set custom instructions. Okay? So first, I'm gonna set custom instructions, and, I'm gonna I'm gonna read these for you.
Jordan Wilson [00:29:18]:
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 wait. Let me see. Nope. This is the wrong one. This is why we do it we do it live y'all.
Jordan Wilson [00:29:36]:
Sometimes I grab the wrong thing. Alright. Here we go. Alright. 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 a 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 and accurate and well researched answer.
Jordan Wilson [00:30:04]:
And then I essentially say, here's the documents. I label them. So as an example, AI news docs May 2024, 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 I'm giving Claude some information about, hey. These are in chronological order as well. Alright. So now I'm just gonna click save instructions.
Jordan Wilson [00:30:28]:
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 gonna click add content, okay, and upload from device. Alright. So you you can't see my prompt or or sorry my window here, but all I'm doing is I'm selecting the files. 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.
Jordan Wilson [00:31:02]:
I exported them all as PDFs, and now I am uploading them. Alright. So now you'll see it's processing the content here. Alright. And it is done. One thing I like about quad 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 a 100% accurate, but I think you can get just under about 500 pages of just documents. So actually pretty decent size of of of a knowledge base that you can work with.
Jordan Wilson [00:31:38]:
Alright. 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.
Jordan Wilson [00:32:06]:
Literally, it is verbatim the same. Alright. So we we're we're probably gonna be toggling between the 2. 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 clogged projects good? Alright. So let's go ahead and get started.
Jordan Wilson [00:32:33]:
So I have some simple let's go ahead and bring the there we go. I have some simple questions that we're gonna ask. Okay? And we're gonna start super simple. So I'm starting and I'm just saying, please tell me the latest news on large language models. Alright. Sorry. Had a little, little Internet hiccup there. Alright.
Jordan Wilson [00:32:57]:
So I am entering this into Quad. 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, stand by. Alright. So the thing I like by default, again, this is with 0 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.
Jordan Wilson [00:33:32]:
Alright. I'm gonna run this exact same prompt, inside my, GPT that does the exact same thing. Alright. So, 1st, 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.
Jordan Wilson [00:33:54]:
Good. Mistral's new model. Large 2. Good. Alright. So did a good job. Let's look at ChatGPT. Let's see.
Jordan Wilson [00:34:02]:
Same thing. So talking about Meta's llama 3, Mistral large 2, OpenAI and copyright issue issues, Microsoft's new AI model, m a I one. 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. Alright. So I think both models there did a pretty good job.
Jordan Wilson [00:34:24]:
Alright. 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 no. It's 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 Cloud Project's ability to not reason, but to think rationally.
Jordan Wilson [00:35:02]:
Because if you know anything about Apple and their AI, from May to August, it's been wild. There's 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 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.
Jordan Wilson [00:35:33]:
Please do not waste words. Right? I don't wanna spend an hour going through a huge product, project here. Alright. 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 4 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.
Jordan Wilson [00:36:02]:
Right? A 100 pages a 100 plus pages of AI news is a lot to take in. Claude obviously got the job done in 5 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.
Jordan Wilson [00:36:28]:
Technically, that was in June. So is it right? Yes. Is it wrong? Yes as well. So okay. Now we're talking about, you 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.
Jordan Wilson [00:36:51]:
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 to to what Apple is is doing. This would take a long time. Even if you follow, you know, AI and and Apple, you probably wouldn't know this.
Jordan Wilson [00:37:25]:
Even if you read our newsletter every day or listen to the podcast every day, that might be difficult. Alright. 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 ChatGPT and GPTs do. Again, custom instructions, custom knowledge base, and it's going through. Alright. So let's see.
Jordan Wilson [00:37:50]:
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 you know, about potentially, Apple working with Meta and Google in the future, in addition to OpenAI, so nail that. Privacy and safety, talking about some things with the Biden administration. New AI features, for Apple Intelligence. Let's see here.
Jordan Wilson [00:38:19]:
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, ChatGPT 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.
Jordan Wilson [00:38:46]:
And, hey, livestream audience, let me know if if you can still, still hear and see. I 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 gonna say one failed. One's better than the other. If if 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.
Jordan Wilson [00:39:13]:
I'm not asking something super specific. Alright. Now we're just gonna do a slight kind of needle in a haystack test. Okay? What that means is, let's see. In one of the files here alright? So this file is June 2024 on 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.
Jordan Wilson [00:39:48]:
So more than a 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 test when you're using whether it's Claude projects, whether you're using GPTs and ChatGPT. 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. Alright. So let's go ahead and test this out, in Claude projects.
Jordan Wilson [00:40:21]:
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. Alright. So, it got to it pretty quickly. Right? So there's the answer. CFO Sarah Friar, CPO Kevin Whale. Alright. So pretty good job.
Jordan Wilson [00:40:42]:
Claude Claude found it pretty quickly. Alright. 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 ChatGPT found it as well. Said appointed Sarah Friar as the CFO and Kevin Whale as the CPO. Alright.
Jordan Wilson [00:41:05]:
So there you go. There is a very quick overview, at least, of basics. Now I wanna do something, I wanna 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, alright. Why would I wanna use this as a research assistant? Right? I could just do that on the Internet. I could just use perplexity, etcetera. Okay. Cool.
Jordan Wilson [00:41:27]:
Well, that was just an easy way to look at it. Alright. 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.
Jordan Wilson [00:41:42]:
Alright. 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 gonna build this one live. It's already built. Alright. So, essentially, what we have going on here, I'm I'm looking at the custom instructions. And, again, it's the same for Claude and ChatGPT.
Jordan Wilson [00:42:07]:
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, livestream, 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 at everyday AI.
Jordan Wilson [00:42:42]:
Yeah. Maybe you listen to 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 or sorry.
Jordan Wilson [00:43:08]:
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, etcetera. Right? We have a lot of data. So this is our data from Beehive. That is our email service provider. And then podcast data.
Jordan Wilson [00:43:25]:
So I'm putting in there. So, I'm saying these are stats from our podcast. We use Buzzsprout, to distribute our podcast on all major major platforms, you know, Apple, Spotify, all these other places. So then 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 g or sorry. I'm telling, Claude Projects, hey. Depending on what the user asks you, you need to find the correct file.
Jordan Wilson [00:43:56]:
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. Alright. 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 asked for. More on that in a minute. And I did the exact same thing inside of ChatGPT, but instead of artifacts, I said advanced data analysis.
Jordan Wilson [00:44:28]:
Alright. 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 Chad GPT. I turn off model training. Alright.
Jordan Wilson [00:44:48]:
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. Alright. But I've uploaded all of this data. There's a good amount of data. I would say we have 100 of 1,000 of data points.
Jordan Wilson [00:45:09]:
Alright? And we've used 73% of our knowledge file here in Claude. So actually a ton of data. Alright. 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.
Jordan Wilson [00:45:39]:
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 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.
Jordan Wilson [00:46:15]:
Enough chitchat y'all. Let's let's jump straight into it. Alright. So let's go ahead and run some general prompts here. Okay? So what I'm saying and I'm gonna 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.
Jordan Wilson [00:46:50]:
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.
Jordan Wilson [00:47:37]:
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 gonna run this exact same prompt inside ChatGPT. So if you join on the podcast, I'm just doing a bunch of window flipping right now. Alright. So, Claude Projects right now says pondering.
Jordan Wilson [00:48:04]:
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. Alright. 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.
Jordan Wilson [00:48:27]:
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 GPT 4 o model is better. G ChatGPT has Internet connectivity. GPTs are more flexible and robust, but Claude has artifacts. Right? So, essentially, to 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 3rd party, platform or, you know, have your own web hosting.
Jordan Wilson [00:49:13]:
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 use 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.
Jordan Wilson [00:50:04]:
Alright. So I'm just gonna go over there very I'm gonna go over these very, very quickly. Alright. So Claude says number 1, mobile opt 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.
Jordan Wilson [00:50:28]:
It has a higher click through rate compared to desktop. Alright. So, essentially, it's saying make sure the action it's 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. Alright. 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 gonna read later. Alright.
Jordan Wilson [00:50:56]:
Let's jump over into, ChatGPT. So one thing I love about ChatGPT is the new advanced data analysis v 2 is crazy. It is so good. And they actually just 2 days ago, updated even the v 2 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. Chat gbt 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.
Jordan Wilson [00:51:37]:
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 and then it's telling me what it's looking at. It's it's getting 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.
Jordan Wilson [00:52:22]:
This is fantastic. Alright. So let's go ahead. I'm gonna do the same thing. I mean, look at this from ChatGPT. It's I love I I I love the way that GPTs and and, advanced data analysis show you the work. Alright. So I'm gonna I'm gonna read the number 1 and then just kinda go through some of the rest.
Jordan Wilson [00:52:38]:
So number 1, 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 Cloud Projects gave me a very similar format.
Jordan Wilson [00:53:03]:
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. Alright. So there you'll see, ChatGPT give me, you know, number 2, top performing URLs in email. 3, effective subscriber acquisition sources. 4, popular search queries drawing traffic. Right? High performing pages. Right? Just looking at these eyeball tests, I actually like the, I like the insights from ChatGPT a little bit more.
Jordan Wilson [00:53:37]:
Right? So let's just say as an example. Right? So number 4 and 5. It's a little more specific from the gbt. So it's 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 clawed 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.
Jordan Wilson [00:54:13]:
Alright. Let's go ahead. We're gonna run a couple other, prompts here. Alright. And we're gonna 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 ChatGPT. Alright. 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.
Jordan Wilson [00:54:43]:
Be creative and strategic in your approach. Be ultra specific in your result, but do not waste words and be succinct, factual, and creative. Alright. 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 Quad Projects and also GPTs is to do some reasoning, right, to do some logic work.
Jordan Wilson [00:55:15]:
Because like I said, there are 100 of thousands of rows or sorry, 100 of thousands of 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 1, it's gonna be expensive. Very minimum to to play, to play ball with these type of companies, 5, 6 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.
Jordan Wilson [00:55:42]:
And this is first very first question. I could do 10 follow ups and really drill down. Alright. 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.
Jordan Wilson [00:56:06]:
Geographic gaps. So okay. It's like, alright. It's saying you're not getting engagement from Japan and South Korea. Podcast download consistency, that's normal. Email click rates. Alright. So it's giving me some essentially pros and cons there.
Jordan Wilson [00:56:21]:
Let's look at ChatGPT, 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. Alright. Some of the downsides here. Let's look. Stagnant open rates on some emails, declining click through rates, underperforming subscriber sources. Alright. So, I, again, I think both of them passed the test here.
Jordan Wilson [00:56:46]:
Alright. So I think Quad 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. Alright. We're gonna do about one and a half more things here. Alright.
Jordan Wilson [00:57:11]:
So now here's where we're really gonna start separating what's currently capable in quad projects versus GPTs. Alright. So now what I'm asking is I'm saying based on the data that I have, please please give me an idea of 5 data visualizations or dashboards that you could build for everyday AI that would be the most helpful. Yeah. Here's where we're gonna 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. Alright.
Jordan Wilson [00:57:45]:
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 5 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.
Jordan Wilson [00:58:22]:
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. Y'all, that's that's pretty that's pretty amazing. Right? Again, this is an interactive dashboard. Features. 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.
Jordan Wilson [00:58:57]:
I wish Claude would change their rules a little bit more because you have to have minimum you have to pay for 5 seats essentially for their team plan to then be able to share these with your team. So you're talking about it 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 2 users for a team. Right? And then again, you can share GPTs though with even free users.
Jordan Wilson [00:59:33]:
But this is pretty impressive. Alright? This podcast performance tracker that claw just literally went ahead and built, Very impressive. Alright. So what I'm gonna do, and I'm gonna do this for each of them. I'm gonna have them essentially try to build me one in more depth and detail. So I'm I'm for quad projects, I'm asking, I like this idea that it said SEO performance analyzer. So I'm just gonna say, please build number 4 in artifacts, and I'm gonna say making it more in-depth, detailed, and useful. Alright? So very, very, broad, you know, instructions there.
Jordan Wilson [01:00:17]:
So let's see actually what it does. Alright. Now we're gonna jump over real quick to ChatGPT. So again, it gave me 5 different ideas for things that it could visualize. So advanced data analysis, or code interpreter as it's sometimes called inside chat gbt is a little different. It can visualize data. It can plot things, create charts. It can't create interactive dashboards.
Jordan Wilson [01:00:39]:
Right? It can't create apps. It can't create, websites and and render them. It can write the code, but you can't actually see them. So I'm gonna go through here and I'm going to say, okay, same thing. So for number 1, it said, email campaign performance dashboard. I don't think, ChatGpT 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.
Jordan Wilson [01:01:03]:
So first, I'm gonna do a follow-up questions, and I'm gonna say, I'm gonna say which one of these would be best as an interactive, chart or data visualization. Okay. And then what I'm gonna do is I'm gonna choose one of these just like I chose 1 in Claude Projects, and I'm gonna have, ChatGPT 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 gonna work very well. I'm gonna say, please, I'm gonna say using advanced data analysis.
Jordan Wilson [01:01:46]:
I hate typing live. Please build this interactive I'm gonna say chart, because it cannot technically build a dashboard. It can build interactive data visualizations. So I'm gonna say, please build this interactive chart or data visualization. I'm gonna say, if you're not able to, please reformat this idea in a way that you can visualize the data using advanced data analysis. Alright. I hate typing live because with my setup here, I got cramped dinosaurs arms. Alright.
Jordan Wilson [01:02:26]:
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. Alright. So now let's go ahead and jump in. Let's see how Claude did. Wow. This is impressive.
Jordan Wilson [01:02:38]:
Okay. So again, as a reminder, I asked it to build. It gave me 5 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 livestream, y'all, this is this is pretty impressive here.
Jordan Wilson [01:02:56]:
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 5 of them. Interesting. Okay. So it built me a series, of different interactive graphs.
Jordan Wilson [01:03:18]:
Man, Claude 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't let's see if I can hover over. Okay.
Jordan Wilson [01:03:38]:
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 it's showing me, let's see, clicks and click through rate. So okay. I didn't even know this.
Jordan Wilson [01:04:04]:
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 council, but a way higher click through rate. So that's that's pretty interesting there. Jeez. I mean, y'all livestream audience.
Jordan Wilson [01:04:24]:
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 jeez. 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.
Jordan Wilson [01:04:51]:
Very cool. Now it's going over to top performing pages here again. So Claude literally just built me 5 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. Alright.
Jordan Wilson [01:05:22]:
So let's see. Alright. Let's see how chat gbt did. This is a lot. Right? Showing me its work. A lot of Python here 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.
Jordan Wilson [01:05:39]:
So I said please build this interactive chart, and the one that it gave me, was the one for, subscriber growth and acquisition dashboard. Alright. So scroll down here. Let's see what it did using advanced data analysis and code interpreter. Lot of data here. Lot of data. Okay. So you'll see right here, it gave me way too much data.
Jordan Wilson [01:06:03]:
Right? But it's also extremely impressive that it brought all this in. So here's what I'm gonna say. I'm gonna take a screenshot of this. Right? I'm gonna drop this screenshot into chat gbt, and I'm gonna say this is too much data, and I can't read the labels. And I'm gonna 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, Let's say you're using this to build a a a dashboard for your team or an interactive graph that you can share with someone, and you're like, how am I gonna troubleshoot this? There's, it looks like 100 or maybe thousands of different data points on this chart.
Jordan Wilson [01:06:47]:
How can I work? Well, upload a screenshot and be like, yo, this is all jumbled. It's gonna see. It's gonna 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 gbt 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.
Jordan Wilson [01:07:28]:
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, you know, $20 a month Claude, pro subscription, that's wild. Right? Again, to 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.
Jordan Wilson [01:08:27]:
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.
Jordan Wilson [01:09:00]:
Right? Companies that are public, they have to put out their 10 k's. Think of all these interactive dashboards, all of these insights. As an example, here's the company's, you know, 10 k financial reporting for the last 5 years. You 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. You know? 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.
Jordan Wilson [01:09:35]:
Let me explain what I mean. If you are not excited by what I just showed you, you gotta you gotta check your pulse from being honest. Right? This level of business intelligence, of automation, of of AI doing 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, you know, read long PDF reports, grab all this, data, try to put it into little graph. It would look very ugly and it would take me, like, 20 hours. Do this in in 20 seconds. This changes.
Jordan Wilson [01:10:18]:
So from a from an opportunity, if this whether you're an an 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.
Jordan Wilson [01:11:04]:
If you are not using large language models, if you're not using Claude Projects, if you're not using GPTs, you're putting your own career, you're putting your own company at risk. You shouldn't do it. Alright 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.
Jordan Wilson [01:11:33]:
Alright? I have I, I'm gonna record a video on some even more specific examples and use cases that I think are great, for Claude projects and for GPTs. Alright. 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.
Jordan Wilson [01:12:08]:
So I'm gonna be going over some of my favorite use cases for cloud projects and for, ChatGPT's GPTs. Alright. 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.
Jordan Wilson [01:12:29]:
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, 3 friends, you're in 1st place. So, you know, it's essentially a referral giveaway. You just sign up, You get a link. You send that to your friends.
Jordan Wilson [01:12:49]:
If your if your friends sign up for the Everyday AI newsletter, you earn points. And like I said, I think if you refer 3 friends, you're in 1st place. So we're giving away a year of ChatGPT or a year of Claude Anthropic. Right? So a personal, paid subscription for a year to your favorite large language model, and we're gonna be talking about a lot of other prizes. One of them will be a 1 on 1 consult with me. So if you find everyday AI helpful, if if you want to maybe what you saw today, you are wowed by it, but you don't really wanna pay 20 or $30 a month. That's cool. We will pay it for you.
Jordan Wilson [01:13:24]:
So go sign up and, refer friends to our thanks a 1000000 giveaway, and we'll see you back tomorrow for more everyday AI. Thanks all.