EP 593: Google Opal: The Simplest Vibe Coding Ever? How to Use It

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life. I still think that vibe coding has barely caught on. Yes, it's one of the Trendiers words in AI in 2025 so far. Yet I think there's literally hundreds of millions of people that would probably vibe code a lot more if there was a simpler entry point. Sure, there's software like cursor and GitHub, co pilot and so many others that are actually very easy to build full stack apps. But what if you just need something simple or a way to dip your toe into the vibe coding waters? That's exactly what we're going to be going over today in Google's newly released Opal tool, and what I think is the simplest vibe coding tool ever. I mean, talk about no code. There's literally no code. So in today's show, we're going to go over different ways that you can use it and I'm going to show you live how to do just that. So on today's show, we're going to go over the basics of Google's free Opal vibe coding tool. We're going to compare it to other vibe coding platforms like Cursor, GitHub, Copilot, and Repl it, and show you live how it works. All right, I'm excited for today's show. I hope you are too. What's going on, y'? All? Welcome to Everyday AI. My name is Jordan Wilson and Everyday AI, it's for you. This is your daily livestream podcast and free daily newsletter helping everyday business leaders like you and me not just learn what's happening in the world of AI, but how we can leverage all of this to grow our companies and our careers. So if that sounds like you, what you're trying to do in it starts here with the unedited, unscripted live streaming podcast. But if you want to take it to the next level, make sure you go to our website at your everyday AI.com there you can sign up for our free daily newsletter. We're going to be recapping the highlights from today's show. So if there's something, maybe you're listening in the car, out on the treadmill, maybe there's something you hear and you're like, wait, what was that? You can always check in our newsletter as well as we keep you up to date with all the other news happening in the world of AI. So this is going to be one of those, that's a little bit better for our live stream audience watching the video. But if you're on the podcast, you can always go to our YouTube channel or our website. Like I said, your everyday AI.com and watch the video of this. This is going to be a little bit more visual, but I'm going to do my best to describe this to our podcast audience. And speaking of audience, when I say your everyday AI, it's yours. This is. You know, we started this new AI at work Wednesdays, showing you ways that, you know, we're using AI internally. And I said, what do you all want? And you voted for Google's new Opal tool. It was actually pretty close between a couple different tools there, but let's get straight into it. And also I did put together a list of 20 ready to go Opal apps. So maybe after today's show you're like, wait, that sounds interesting, but what are some ways I could use it? So go repost this show on LinkedIn and I will send you access to those. All right, so let's get into the basics. What the heck is Opal? Well, it's a free vibe coding tool released from Google, and it's much different than these other kind of vibe coding tools out there. Technically, Google has like five different ways you can Vibe code. And I'm going to be comparing Opal with some of the other more popular ones, mainly Jewels and some other very much more fully featured vibe coding tools like Cursor and Replit and others. But for the most part, it is a completely new way to build apps. Okay, so the way that Google says it, it says you can build, edit and share. Multi. Sorry, Build, edit and share many AI apps using natural language. So you know a lot of these vibe coding tools, when people look at them, right? Like Lovable and Bolt and Cursor and all these other ones. And it can be a little daunting because you're like, wait, I thought this was like a no code thing. Like, why do I have this entire repo And I'm having to, you know, make these MD files and like, like, what the heck is going on? If. If you've ever felt that and you just want to, like, experience something for the first time. I think Opal is great for that. It is free. If you have a Google account, that's all you need to get going. And you can literally, and I'm not exaggerating this, you can build a fully functioning app that you can share with others, and it works in, like, less than A minute. So I'm going to do something a little more complex, but let's dive into a little bit more an overview of exactly what Opal is and how it works. So like I said, right now it is free, but it is experimental inside Google Labs. So I believe the last I read it's only open in the beta only, so you can opt into it on Google Labs and right now it's only available for the US I will double check that because Google is literally shipping things like daily. So I will make sure to double check that before we put out today's newsletter. And essentially natural language, you just tell Google what you want it to build and it's going to build a visual multi step workflow that you can go in and edit later. And it chains together different prompts, model calls and Google Gemini tools. That's the great thing. Essentially anything that Google Gemini has released, you can use that inside of Google Opal. So if you've ever seen maybe, you know, the V3 video generator or the Imagine 4 or just any of Google's models, right? They have the top end Gemini 2.5 models and you're like, wow, I wish I could build something on that. Well, you can. And it takes literally less than a minute to build something extremely basic. Or you can spend a little bit more time modifying it so users can edit workflows via a conversational command or a visual editor. Then you can test and refine your apps and finish. Apps can be shared with others who also have an Opal account. So yeah, you can't just, you know, share it publicly. You do have to share it with anyone that has also signed up for Opal. But like I said, all you need is a Google account right now in the US Takes less than a minute to get up and running. So my biggest takeaways, I've been using it since it came out on some of my different accounts. If you think that you're going to build your next SaaS or your next iPhone app with this, that's not what it's for. I like to say this is great and the best for task apps, right? So I think a lot of times when people think about vibe coding something, they're running into an issue and then they think, okay, I need to build a fully functioning web app for this or oh, this could be an iPhone app, right? Whereas maybe first you need to validate it just quickly, like, can it be done? Is there a market for it? Or even maybe you're just like, let me just build this for myself. Maybe it doesn't need to be a full stack with a back end login, pricing users a dashboard, right? Maybe you don't need all that. Maybe you just need to build yourself something that's going to solve one of your problems immediately. And I think that's what Opal is great for. If you go back and listen to my 2025 AI prediction and roadmap series, I said non technical people are going to be building themselves apps just to solve their daily issues. And I think Opal and is the one for that because it is dead simple, right? In the same way that I think Notebook LM has changed the way a lot of people work, even non technical people. I think Opal might be that tool that does it for non technical people that you can just code task apps. That's what I call. These aren't full stack fully customizable, right, where you can, you know, change every pixel. It's not like that. These are more task apps. They're not highly customizable. But I, I think if you just reframe your brain into like, okay, do I just need, you know, an AI tool that can do something for me that's repetitive and I don't really care about how it looks, but I do have some customizable, the ability to customize how it functions. That's what Opal's for. And by far, I think this is the easiest way to vibe code away nagging problems for everyday people. And I'm going to give you an example of something that I've started to use it for and I'm going to continue to refine it for myself as well. So like I said, Opal is free. You don't need to pay for API usage or hosting. That's the other thing. A lot of times if you're trying to build an AI app, you have to connect it and pay for that AI usage, right? So if you're using OpenAI's API or Claude's API, right, you have to actually pay for it. You're not paying for it inside Google Gemini, right? I've been saying this since AI Google's AI Studio came out. It's like I feel like I'm robbing Google whenever I use it. Same thing with Opal. The fact that I can create an app that has Gemini embedded, I can use anything Google, essentially anything that Google Gemini has released. And it's free. Of course it is in beta. It's experimental, right? So that doesn't mean it's going to be around in five years. It Might get folded into other products. But right now, I mean, I've been talking for eight minutes. You could have already built multiple AI apps that solve it. And this is the most no code Vibe coder there is, mainly because there's literally no code. All right, so let's start live, shall we? All right, live stream audience, do me a favor, as always, let me know if you can see the screen. So this is. I started on one of my other accounts, so not littering your screen with a thousand things. So here's what we're going to do. When you go on to Opal, you'll see it's an experiment. You have to be logged in, so please follow along live for our live stream audience. Or, you know, if you're in the podcast, you're in your car, hit pause, jump back in in front of your computer. Let's vibe, code some stuff together. So when I log in, all I'm going to do is go click Create new. All right, and there's different ways that you can create a new app inside Opal. I'm going to show you one way and then I'm going to show you the other. And I'm not going to fully build the app out the first way. I'm just going to show you how it functions. So for our live stream audience, you'll see, and hopefully podcast audience, I can explain this easy enough. So right now there's two different panes. So the main pane, it's kind of like a canvas. And I can also chat with Opal in this Canvas view as well. And then on the right side, I can actually preview the app. It's going to build it and render it as we go, which is really cool. So if you're used to using, you know, Canvas mode in Gemini or Chat GPT or artifacts in Claude, it's the same thing. You kind of chat on the left side and then you can see things rendered on the right side. So a very familiar interface. However, the big difference here is now there's essentially like a canvas, a whiteboard that you can actually drag things and build. So essentially you have user inputs, you have Generate, which is the Google Gemini capabilities, and then you have an output. So then you can also add different assets. You can upload files, connect it to your Google Drive, YouTube, text drawing, etc. Right. So we're not going to get into assets. I just want to show you the two different ways that you can build an app. So one is you can do it more manually by just clicking these assets and Dragging them around the board. So as an example, I can click user input and our live stream audience will see. Now there's a bright yellow box and it just says user input, right? And then I can go in and select it in the editor. So then on the right side I can click the at key. And then I have different options, right? I wouldn't do these for the user inputs because for the most part, user. User input is going to be a text command from me, the user. So keep that in mind when I'm building this. The user input is what the person using it, which is probably going to be you or your team, is ultimately going to put in. So I can type in different commands there. I'm going to leave this blank. I'm just showing everyone an example of how this works. All right? And then I have generate. So here's where the magic happens. All right, so this is all the different capabilities inside Google Gemini. So I have different models here. So I can choose different models. I can do Gemini 2.5 flash pro. You can, you can do in depth research. You can plan and execute different tasks. Here's imagine for Google's state of the art AI image generating model. There's audio LM. You can generate speech from text. There's VO. I don't. I'm not actually sure if this is VO 3 or 2. I will have to reach out to my friends at Google and double check that. And then you have Lyria 2, which can create instrumentals from text. So talk about a literal creative sandbox here, right? So think if you've ever been using a large language model and you're like, man, I wish when I typed something in, I could get video out or an image out or, you know, an instrumental out, right? Now you can, and it's literally just on a dropdown, all right? And then essentially what you would do to connect these things is you drag an arrow from your user input to your generate, right? I have to actually connect that. There we go. All right. And there we go. Sorry, didn't get a little, little backwards there. Okay. So we have our user input that sends the user inputs to the generate tab. I choose what I want it to do, all right? And then I can, you know, type in instructions, right? So know, I could say, you know, take the. Take the user input and that's a selectable item and use, you know, Gemini 2.5 flash to blah, blah, blah, blah, blah, right? And then all I have to do is click output, all right? And then I drag the generate to the Output. So it's simple. Podcast audience think. I have three little things up on a whiteboard. User input. It gets sent over to generate. Inside generate. I can choose what Google capabilities I want and that gets sent over to an output. And so I essentially have an app. Not. None of this would work yet. I'm just showing you the two different ways that you can build it. So that's way one, but I say do it the other way. It's way easier. You just type something and describe what you want to build. Okay, so I actually have a little prompt here and I'm going to put it in and we're going to get it going. So here's what I said and we're going to watch because it's going to be built live, right? So now I'm telling Google how I want this to be built and actually it might take a minute or two. Actually, no, I lied. It's done. My gosh, that was fast. All right, so it's very, it's very impressive. All right. I could even explain what I wanted to be built because it's already built. So you'll see now I have a user input, I have a multiple step generation process and I'll explain exactly what's going on. So there's some, some simple conditional logic here. All right, there's four different processes, four different generates, and then it gives me an outcome and I literally have an app ready. All right, so now in the right hand side, it built an app. Like I said, these aren't apps that you're going to be building and, and you know, moving things around, pixel for pixel for the most part. I mean, you have a little bit c, like a little bit customization options, right? You can go in and create a certain theme again with AI. All right, so I'm just going to go in and. All right, let's see here. All right, I'm running into an issue. I think it's because when I was clicking around. All right, so actually what I'm going to do, when I was clicking around, I accidentally disconnected one of the nodes. So I'm just going to redo that app. Bam. And it's going to cook here in a second. But in the second, let me actually take a quick pause. Halftime break, 32nd halftime break. Word for word from our sponsors. This podcast is supported by Google. Hey everyone, David here, one of the product leads for Google Gemini. If you dream it and describe it, VO3 in Gemini can help you bring it to life as a video. Now with incredible sound effects, background Noise and even dialogue. Try it with a Google AI Pro plan or get the highest access with the ultra plan. Sign up at Gemini Google to get started and show us what you create. All right. And yeah, in that 30 seconds, obviously opal rebuilt the app that I accidentally screwed up there. All right, let me just go ahead and tell everyone exactly what I told Opal to do. This is putting AI to work at Wednesdays. This is something I'm actually using, and I like it. So I said I want to create a simple app called a podcast episode outline generator for my podcast Everyday AI give the app either. I want to be able to give the app either a basic or a specific topic, and it will do very specific research for me that is timely and relevant. It should first start by searching Google for the topic I want on today's date, then this week, then this month, then prior month. So I'm giving it directions on how I need it to search. Because if I'm using this to help me plan and research episodes, I need to make sure I'm researching today's news first, then this week, because I don't want to be researching stuff from months ago. It's very old. Then I say being fresh and timely with information is paramount. Essentially, I'll give the app a topic, then it will give me three different episode topic ideas and outline what should be covered. The the three ideas should be different from each other. The outline should include a suggested title, five major topics to cover in each episode, and factual bullet points of each of these topics. It should also use Google's Imagine to create a visual for each suggested episode. The app should be easy to use and interactive. All right, so it's obviously done. So essentially now I'm back on the kind of whiteboard version, and I have my podcast planner on the right side because I'm in editor mode, but I can click app mode, and that essentially launches this very basic app full screen. But let's go back into editor mode and explain a little bit of what happened. So it took my prompt and it created this app in this kind of like whiteboard version, right? And I can go in here and I can edit things and refine them a little bit as well. But essentially, I can go in. Let's zoom in a little bit here to see exactly what's going on. So on the user. Sorry, I'm not used to using this on my. It's a scroll thing. I wish I could click and drag, but it's a little different. So essentially, on topic, this is the user Input. So the user enters the podcast topic, then it takes that topic and it carries that topic over to the three different generate tabs. So it's doing multiple steps of research and it's passing it on to each of the generate tabs. And then in the end, it's creating an interactive web application. So again, the input is the podcast topic, the generate using Google Gemini. It does three different steps of researching and passes the information on to each different step. And then last but not least, the output is an interactive web application. Pretty cool, right? Let's see if it works. All right, so I have something already typed up, so let's use the app mode and I'm going to click Start. Okay, so all it is it now there's no, no fancy visuals, no animations. This is straightforward. That's why I call this more of a task app builder. And it's not a traditional, fully featured, know, full stack, you know, agentic coding tool. All right, so all I'm doing is now I'm putting in a simple prompt and what it's showing me right now, it's conducting deep Research. And I'm actually going to skip over to editor mode because it's a little more fun to watch here because you can literally watch as it takes my query from step to step. So it's in the first step right now. So the first of those three generate tabs is highlighted and I can see it says, conduct conducting deep Research. So on those different modules, one of them was doing a version of Google's Deep Research. This is great. This is the normal process I do. The way I normally research a topic is I will manually start typing out my ideas, my bullet points, you know, facts from the newsletter, et cetera, and then I'll send those to Google's deep research. OpenAI's deep research. Claude's Deep Research. Right. And then I take all of that information and then I'll start organizing it first myself. I'll say, okay, based on all this, I'll start browsing it and I'll start outlining it. And then I'll send my initial outline and all of this deep research over to different AI tools to help me start outlining my shows. Right. It's a very collaborative process. I usually ultimately use OpenAI's Canvas or Gemini Canvas, so then I can do a more interactive element with the, the AI. So now already it's moving on to the second step. So you'll see right away you might be wondering, okay, why is this taking so long? Well, it's not because it's literally doing multiple steps of deep research. All right, we're already. Now it's creating images, so now I can see the third of the three generate icons is highlighted. So I know, I know right now it already did a bunch of research. It made those podcast ideas and I assume right now what's happening, because I can see it's highlighted. It's using Imagine 4 to create visuals as well. And now it's on the last step in the output and it's creating an interactive kind of web app just based on the prompt that I sent and the prompt that I sent I all my topic was quarter three, 2025, trends of smaller language models like Google's Gemma three two seven, which just came out this week, and OpenAI's GPT OSS 20B and the Rise of Edge AI. So I do want to do an updated show sometime soon on kind of this rise of the smaller language models. They're getting much more powerful, much more robust. So, so I literally just built an app, customized it with natural language, and it went out, did, did a bunch of research, put it together how I wanted to. It should give me three different episode ideas based on my topic. I gave it. It should bullet point factual information on those different episode ideas. And it should also give me some, some image art. Right? So it's probably not image art that I would actually use. You know, on a zero shot, I would probably go in after the fact and use it a little bit. All right, so it's done. It's done. That was pretty impressive. All right, so now I'm going to drag. I'm actually going to go in full app mode, so hopefully our live stream audience can see this. This is pretty. This is pretty cool. All right, so I have the three different ideas here that this, this, this is really, really cool. I'm just cheesing a podcast audience because, man, I like this. This technology is really, really impressive. Okay, so I had the three different episode ideas. So one it says edge of innovation, how smaller AI models are reshaping quarter three, 2025. The second one it says Gemma 3 270, Google's tiny titanium of on device AI. And then the third one it says OpenAI goes open the GPT OSS 20B and the future of accessible AI. So it looks like I gave it kind of two examples. So it built out episode ideas on the two examples and then it made more of a general one. So right now it's. I have this kind of interactive, almost like a little miniature website. The Three episode ideas. When I hover over them, I can tell there's more information. So when I click on one. Okay, let's see if I can click on them all. Okay, cool. I can't click on them all at the same time, which is pretty nice. So each of these, it should have at least five bullet points. 1, 2, 3, 4, 5, 6. It gave me six and that's fine. Oh no. Oh gosh. Okay. It gave me a ton, a ton more information than I even asked for. So it looks like it gave me different categories. So let's just walk through the edge of innovation. The first one how smaller AI models are reshaping. Quarter 3, 20, 25. So gave me five different categories and then in each of those kind of categories it bullet pointed additional information. So it says first the rise of small language models, second edge AI. Third the convergence small language models and edge AI synergies. The fourth, industry transformation in real time impact. And then privacy, efficiency and accessibility. So pretty good. So I'm actually going to look now at the GPT OSS version because this is the only one I've done a dedicated podcast on and I want to look at the quality of research here. So it says The GPT OSS 20B model was released on August 5th. That's correct. It's available under the permissive Apache 2.0 license. Correct. Allowing free building, customizing and deployment without restrictive copyright. It marks OpenAI's first open way language model release send GPT2 correct signifies a strategic recollabration towards an open source strategy responding to growing open source AI community and competition. Correct. CEO Sam Altman indicated consideration of a different open source strategy. And yeah, this is all really, really good. So this right here is something I'm going to be using all the time now because this is something, this multi step process is something that I've been doing now manually, right? I would obviously go in and refine this. I essentially just zero shotted this thing, right? Or one shot of this thing. I gave it a very simple prompt, no going back and forth and it did a pretty good job. And then it also gave me these different visual concepts down here at the bottom. None of them are breathtaking, right? But it just pulled together some different visuals based on the content. So nothing that I would probably use use for the most part. I mean there's a couple of these I might use like in an infographic or on a blog post. Nothing's blowing me away per se. But again, all I did is say make me some images, right? If I was more descriptive on the type of images, the style, what I would be using it for. I would assume that these visuals would be a lot better. But here you go. I essentially, for each and every podcast idea that I have, I can go get multiple different angles that I can tackle this from some of my base research has already done for me. And although these visuals right now might not be great, eventually they might. Right. I can spend some more time, be a little bit more descriptive, and then I can go in and edit the app as well. So as an example, now I'm going back into the app editor and I'm looking at what it built. So again, all I did natural language, but I can go back in, click any of these modules and edit them as well. So as an example, the generate podcast images, I can go in and see exactly what's happening. So I can go over here on the right hand side and it looks like it used Gemini 2.0 flash image generation, but maybe I want to use Imagine 4. All right, so I can do that and then I could rerun it And I'm guessing those images are going to be much, much better now. Right? So it was using a much older version, an actually underappreciated and underutilized model in Gemini 2.0 flash because it does text and images in the same model, but the image quality is, is nothing near what imagine 4 is. So if I ran this again, I'm guessing it would be much better. All right, and it saves automatically. Right? So that's really, really cool. So let me show you one or two other things quickly in Opal and then I'm going to go over some comparisons against other tools. So one of the things is Opal has a gallery which is really cool. So essentially there's different pre built apps that you can go in and modify. So one is learning with YouTube. Right? So I can click that again. The app is already built and I can click this remix. Okay. So what the Remix does is it essentially creates a version or a copy of the app. And now I can go in and you know, do this for my own purposes. So I can just say, you know, update this to make it a dedicated learning tool that explains complex AI topics for everyday non technical people. All right, so in a couple of seconds I can take something that was pre built by the Google Opal team, go in, put some simple natural language instructions and refine this and use it for my own purposes. So you can edit things in natural language essentially in a chat box. Chat box. Or I can Go up in this more kind of sticky note whiteboard layout in these modules, click them and change anything in natural language or with the dropdown. So not only can you build something from scratch with the modules, you can number two, build something with natural language. Or the third way that you can do it is you can go and find in Google Opal's pre built library, go in, remix something and it's done. Right. So this one is literally already done. So in that 30 seconds, I went in and kind of fine tuned a version of this web app for myself. The other thing is, you know, the themes you don't have, like I said, this isn't something where you're going to build something fully customizable. Oh, I want an app, you know, that's interactive in this way. It's really just for completing basic tasks with anything Google's AI has to offer, which is a ton. Right. But I can, you know, change the theme, right? So I could just say, you know, basketball themed, click enter and it's going to change the design. So you don't have a ton of customization. But I think that's the whole point. Right. And another thing is you'll see there's not a bunch of code flashing up on my screen. It is literally no code, which I love. All right, so let's get back. We learned a little bit live. Let's do some comparisons, quick. Let me compare these to some other popular Vibe coding platforms, even Google's own, right? And you're like, wait, Google Opal? What about Google Jewels? So Google Opal, the target audience is general creators and knowledge workers. So these are no code users. And the biggest standout differentiator for Opal is it's no code AI chaining. So the ability to use multiple Google AI features back to back to back, passing the input along without knowing any code. Google Jules is another tool from Google. I would say this is one of their more popular kind of AI vibe coding tools. There's actually so many Google has, I would say easily 5. But Jules is another popular one. The target audience for this one is more professional developers and teams wanting more autonomous help. And the standout differentiator here is Async Autonomy and it functions more like a coding colleague that works in the background. So you know, different, you know, vibe coding tools, different pros and cons. Let's look at some other popular ones. Cursor. Target audience for cursor developers seeking a deeply integrated AI first coding environment. The Differentiator for Cursor. It's an AI first IDE and it's a fork of VS code that's supercharged with AI offering code base, aware chat and predictive multi line completion. So that's usually if you're working with a pretty big code base. Cursor is a great tool for that. GitHub GitHub co pilot this is the target audience I would be for dedicated software developers all the way up to enterprise. And the standout differentiator would be the ecosystem in the integration. So it's literally everywhere developers already are. So you know mainly GitHub, different command line editors, IDEs and this is backed and supported by Microsoft. That's actually their product replit. So the target audience there are coders from learners to pros, educators and small teams. And replit is fully featured top to bottom. It is full stack. It is all in one development, hosting. It combines coding, running, debugging and deploying with a self healing also AI agent in one platform. Replit. Very impressive but a pretty steep learning curve. Lovable. It's been kind of trending a lot recently, I would say so for more for developers, startup founders and product teams. So if you're trying to push an app, you know, maybe a startup idea, a software as a service, you know, maybe lovable or replit is something you might be looking at. So this is a standout differentiator I would say for rapid MVPs generates production grade full stack code, backend database, etc. From a high level conversation. But in terms of Opal, like I said, it is the fastest, easiest to use. Literally no code, you don't even see code. And I think to use multiple pieces of Google Gemini AI, string the queries together, share the context. You know, from a deep research to a Gemini 2.5 to an imagine 4 to a VO3 and passing that information all along is a no brainer to start using this immediately to just solve nagging problems. So I also have kind of a core features showdown here for our live stream audience. I'll probably include this in the newsletter as well. But the by far the standout feature for Opal is just no code. Literally no code. There's no code, right. So if you want to go in there and write the code, this isn't for you Opal. It's the one of the simplest there is. All right, so that is a wrap y'. All. So I will say this, I hope this was helpful and I did create a literal starter pack, all right. Of 20 different kind of ready to go apps that you can go in and Modify them for yourself. All right, so I'm going to share them on my screen here. So they're, they're ready to go. So you know the meeting debrief and action plan, the 360 degree article analyzer and these just have placeholders ready. So these have been crafted specifically for Opal. All right. And I think out of these 20, I, I really spent a good amount of time trying to make them for the everyday non technical user. Just some of the most common problems that people run into on a day to day basis. So if you've been wanting to, you know, start vibe coding something or you're like, there has to be a different way or you're like, hey, I'm using, you know, different AIs, but I'm always having to copy and paste and use multiple ones. This is a great way to start stringing these together in Opal. And I have some great different use cases that I've already put together. It's very easy to visually see you can copy and paste these. There's some placeholders you put in your information. It's a great starting point to maybe, I mean maybe one of these is just going to work for you. You right away you copy and paste and this could take a task that you're spending five, six, seven hours a week on and turn it into five, six, seven minutes a week literally. So if you do want access to this, these ready to go Opal apps, just make sure you go share this on LinkedIn. All right, so if you're listening on the podcast we always put the link to the LinkedIn live stream as well as we put that link on our website. So go repost this and I will send you this full list of Opal apps. I hope this is helpful. If it was, go to your everydayai.com Sign up for the free daily newsletter. We're going to be recapping today's show and a whole lot more. Thanks for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.