EP 619: Nano Banana Uncovered: A practical guide from inside Google with Paige Bailey

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If you don't know anything about Nano Banana, seriously, where have you been? Let me drop a number here. 5 billion. That's how many images have been made inside the Gemini app with Nano Banana in less than a month. And maybe you're thinking, oh, okay, Jordan, like, you know, I'm not, like, a creative person. I'm not a technical person. Why would I use Nano Banana? Well, you're going to want to listen to today's show and stick around, trust me, because we're going to be uncovering some of the secrets and real power behind Nano Banana and how you can, even in natural language, create an app in a couple of minutes that is actually using Nano Banana to be wild. I'm excited for today's conversation. I hope you are, too. What's going on, y'?

All?

Welcome to Everyday AI. My name is Jordan Wilson and this is your daily live stream podcast and free daily newsletter helping everyday business leaders like you and me not just keep up with AI, but how we can make sense of it, get ahead and leverage it to grow our companies and our careers. That's what you're trying to do starts right here with the live stream podcast. But if you miss something or if you need more information, it's all going to be in our daily newsletter. We're going to be recapping today's show as well as all the other AI news you need to know to get ahead. So I'm excited for today's show. Have you not used Nano Banana? Don't worry if you haven't. Make sure if you're listening on the podcast. This is one of those more visual ones, so you might want to go to our website and watch the video if you're not going to catch it, you know, catch it on the YouTube stream or anything like that, but let's just get into it. I'm excited for today's guest, so please help me. Welcome to the show. We have Paige Bailey, the AI developer relations lead for Google DeepMind. Paige, thank you so much for joining the Everyday AI show.

Thank you so much for having me. And I can't wait to share more about what we've been building, especially for Nano Banana, the other AI features that we've been incorporating into Google Workspace and our other Google products.

Yeah, it's been great. And as someone that's been covering generative AI every day, for like three years there's been very few moments where I'm like oh my gosh, there's been like five and nanobanana has been one of them. But before we get into it, Paige, can you just tell me a little bit and our audience a little bit about, you know, what your role is at Google DeepMind and what it is that you know, you're kind of working on on a day to day.

Yeah, so I am the area tech lead for developer experience at Google DeepMind. You might have kind of been familiar with developer relations before. We've kind of expanded this out to include more around creator experience. You know, obviously people are starting to be able to build apps and to build digital artifacts with things that aren't so much code centric. So, so being able to kind of help people be successful there, to work with the product teams and the engineering teams as well as modeling teams to make sure that we're getting the right data into pre training, that we have the right evals for the models and really just trying to make sure that as you're building with Gemini, as you're building with Geo3, that you're able to everything that you prior to this. I started as kind of a machine learning Engineer back in 2009 Building models mostly in kind of the earth sciences. My background is geophysics and applied math. And then the world has certainly changed quite a bit since then.

It's changed a ton. Right. So I even remember, you know, I was at you know, Google Cloud next and you know, you and you know, Logan took the stage there and I'm like, I'm like remembering now like and just seeing the difference between, you know, what that was like April or May and where we are today, what's possible for both developers and non developers has completely changed. So just describe for our audience, you know, what are, you know, how capable, you know, are people now to build something that maybe if they didn't have the skills before, how has it changed?

Yeah, I think there's, it's truly remarkable what you can do with Nano Banana in particular. So you can think of it as kind of a natural language interface for images that allows you to do most of what you would be doing in Photoshop. So you can remove backgrounds, you can colorize images that might be historic images of your family, you can make them higher resolution, you can kind of take your Pinterest mood boards and turn them into actual like visualizations. Using your own home as kind of the background scaffolding, you can create really, really beautiful and Hyper grounded new kind of portrait photos, you can generate your own passport photos, like all sorts of things and it's been really remarkable to see what people have been doing there. And then another product that we're really excited about is called Build, which allows you to just describe an app in natural language, create it and even deploy it with a unique URL without having to ever write a single line of code yourself. So like the, there's even a feature with Gemini that if the, if in the process of writing the code for the app, the model encounters any errors, it will take the error, feed it back to Gemini and then kind of self heal the code and self heal the app to get the right outcome for what you've described.

So, so obviously you, you have a technical background, right? I don't. And what's like, what's weird is like everything that you just said there, it all made sense to me, whereas maybe three to five years ago some of that would have went over my head. Let me ask you this, is, is everyone, or can everyone be a developer now at least like, you know, go in and get their feet wet? Right. I've, I've used Jules, I've used Opal, I've used, you know, AI Studio to, to build apps, Canvas and, and Gemini. Right. Can anyone do this or do you still need some level of expertise?

Absolutely, anybody can do this. And I think it's so exciting in the sense that we have hackathons every weekend in the Bay Area. It feels like we do. And it's been really magical to see that the attendees for hackathons have moved away from just being engineers to being product managers, to being folks from maybe a sales background, a business development background who are really excited about software solving problems and who are able to describe really articulately like the kinds of problems that need to be solved. And now there's no hurdle, like there's no big chasm of software engineering ability that needs to stand between them and getting the work done of building an app that can really meet their customers needs. So a lot of the time in hackathons, the teams that we see winning and the teams that we see kind of potentially even building out, companies aren't really necessarily the ones with all engineers on staff. It's kind of like the singleton PMs who have a hobby project or the sales people who have spent 10,000 hours with customers and who deeply know what they need and who just want to build a tool that can help address those concerns. So I am very jazzed and I can't wait to see things like build and things like these models be adopted more and more in the science community as well. Because I think we have, you know, a ton of hyper talented people across so many different domains who haven't had the time to spend 10 years learning how to be a software engineer, but who know precisely what needs to be built. And now they finally have AI democratized in a way such that they can just let loose and create all of the things that they've dreamed about.

All right, well, well, let's, let's do this. Let's, let's let loose a little bit. So for our podcast audience, I'm going to have Paige share her screen here and we're going to walk through a couple of different use cases. 1 I think great for, for non technical people, beginners. If, if you spend a ton of time in Google Sheets like me, I think this first example will be really, really good. This is something that I love using. But Paige, why don't you just kind of walk and talk us through kind of what we can do here inside of Google Sheets. Let's go ahead and bring it up there. So there we go. So we have your Google Sheet showing. Just walk us through what's going on here.

Yeah. So I have a couple of tabs that I've created in this Google Sheets document to help showcase some of the new capabilities of our AI function, which is baking Gemini directly into Google Sheets. This first one is kind of near and dear to my heart. I am a super soccer fan. I used to play it, used to play it back in school. And so we have a lot of teams from the uk, specifically from England, their stadium names and their locations. And what I'm going to do is in this fourth column, I'm just going to type in equals AI. So that's the function name. I'm going to open parentheses and then I'm going to describe in natural language something that I would like to have as kind of a supplement to this data. So I'm going to take this stadium names column. I'm going to say something like return the address for the location. I'm going to add a comma, I'm going to add the kind of link to that cell. So this is all very familiar. If you've ever used sheets before, if you've ever used Excel before, I'm going to hit enter. And immediately behind the scenes, Gemini kind of returns the location of the stadium name in the United Kingdom. And then even better, kind of similar per user experience to all of Those, all of those other functions that have you, you might have used before. It kind of fills in and populates each one of the, each one of the cells in the column based on, based on that stadium name and location. So it's, it's pretty cool to, to be able to see this happen in real time and to be able to understand that, you know, you can start generating data, analyzing data, doing things like sentiment analysis just via natural language and the AI function.

Like, let me be honest, like where, like where was this like 10 years ago? Right? Because this one simple thing. So, you know, Paige walked us through. There's team name, column A, stadium name, column B, location, you know, column C. And then in column D, she just automatically pulled the address live. For these 20 top England soccer teams, this would have taken, I don't know, at least for me, two hours because I would have got distracted. I would have went on the team's Twitter.

Right.

Like, like that is such a timesaver. So even sometimes when you're thinking like, oh, what would I use, you know, Gemini and sheets for? There you go. Saved a ton of time.

Exactly. And, and it's also really useful. Like I'm sure if anybody has ever done data pre processing or data cleaning before, you've experienced the pain that is, you know, people's names spelled slightly differently or capitalized slightly differently in the same column, state names, city names slightly misspelled. Like, with this function, what you can do is you can kind of go through and for those fuzzy problems that didn't even have like exact functions to help you tackle this AI feature can kind of go through and make data pre processing so, so much easier. I have been using it all the time, honestly, and, and I, I think it unlocks kind of these more complicated data analysis workflows for people who previously would have had to learn something like Python or R to do those automations.

Yeah, such, such like a great, I think use case for anyone out there, you know, whether you don't want to have to go through and manually do it or just enrich your spreadsheet. So, all right, what are you showing us here with?

Yes, so, so this is a. Or at least you've probably done it before. So it's. But I haven't shown you this tab. I just grabbed some comments from different social media platforms. So full disclosure, some of the comments might be a little bit spicy because they come from social media, but one of the cool things that you can do with Gemini as well is you can ask for sentiment analysis. So we have Some gems that are baked in. You can also just ask in natural language to classify based on whatever categories that you would like. But it, but you can kind of give, you can kind of give the kind of table views for customer sentiment. You can summarize the customer sentiment of the support tickets. You can kind of analyze at scale how customers feel about a product. Or kind of similar to what we just did before, you could say like, classify the sentiment for the statement into positive, negative or neutral. And, and then just say like, all right, well, B2. And then it will go through and kind of do the, the classification for you.

I've, I've spent again all these things. I'm like, like laughing in like face palming just hours I've spent and the money I've spent in years past to pay for tools that would monitor sentiment analysis on social media. And Paige just showed us. Oh, you can just click a button now in Gemini and sheets.

So.

All right, I want to get into the second use case. But before we do, a quick, very quick word from our sponsors. Great timing, Google. NotebookLM. This podcast is supported by Google. Hey, folks, Stephen Johnson here, co founder of NotebookLM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built NotebookLM as an AI first tool for anyone trying to make sense of complex information. Upload your documents and NotebookLM instantly becomes your personal expert, uncovering insights and helping you brainstorm. Try it@notebooklm.google.com all right, let's get. I, I, like, I actually literally forgot until just now. If any of you are listening to this and you're like, wait, I've heard this page sounds so familiar. I think it's a previous partnership we had with Google. I think we had Paige talking about Gemini 2.5 flash. So great timing, amazing.

And also, I have to say, NotebookLM and Steven Johnson, he's just such a sweetheart too. They have been rolling out features like gangbusters. It's been so awesome to use audio overviews and all of the analysis features in NotebookLM as well.

Oh, it's been, yeah. And, and I do have to shout out like, Josh, like Josh Woodward. I'll like, you know, DM him something. I'll be like, hey, this isn't working. And then like a day later he's like, fixed it. I'm like, this is crazy.

I don't think he sleeps like he is he. And he does that for all of the products. But within labs, within DeepMind is just kind of like the most helpful person I have worked with in my entire career. So, like, if y' all don't follow Josh Woodward on all of the social media, I strongly, strongly recommend it.

All right, so, Paige, let's get into the next one. Let's show people how you can literally build an app in natural language with Nano Banana with the viral AI image generator and editor. Let's see it. Walk us through it.

Absolutely. So right now I'm in AI Studio. AI Studio is kind of the first place to go to get access to DeepMind's models as soon as they're released. You can select different models here, off to the right, including Nano Banana, and see kind of information about them behind the scenes. You can also use many of these models in our products, like the Gemini app, but this just kind of gives you an under the hood kind of view for a lot of the models and their capabilities. But the feature that we're talking about today is this thing called Built. It's this little puzzle piece guy that you might see here off to the left. When you click on it, you're kind of put into this gallery of a whole bunch of apps that. That the team has created to kind of inspire you. Or you can just describe an app that you would like to create in natural language and have it generated for you. So today I am going to. You know, we talked a little bit about Dungeons and Dragons before, so I'm going to just, you know, create an app that allows all of my friends to generate, like, hyper personalized characters using Nano Banana. Does that sound good? Or.

Let's. Let's. Let's do it. And literally, like y', all, she's. There's. There's no editing. You know, if you're listening to the podcast, she's doing this live. She's typing this out inside of AI Studio in the build feature. So anyone can go do this in natural language. But, yeah, maybe. Why don't you kind of literally just dictate even what you're typing here, Paige, just so our audience can. Can. Can imagine it with you.

Yeah, absolutely. So I'm just going to type. Create an app that uses the webcam to take an image of the user. The app should then use Nano Banana to modify the image of the user into a Dungeons and Dragons character, unique Dungeons and Dragons character. Make sure that the app also includes character stats. So, like, strength and dexterity and all that good stuff, and that the app is well designed. So let's do control enter and see what we get. That was obviously, like a very simple Prompt. I'm not the best at prompting. I should have probably used Gemini 2.5 Pro to help me like rewrite my prompts behind the scenes.

It's, it's better, it's. It's better just to go with, you know, your gut, something simple. So, like, as this is building, so it's telling us exactly what it's doing, right?

Yeah, it absolutely is. If you've ever played SimCity 2000 and you've seen like the loading screen for like reticulating splines, it looks very, very similar. It's basically defining everything that it would need to do in order to build out this app with all of the kind of the, the stack associated with building a really nice web application. If it needs to generate prompts behind the scenes for any of these models, it's, it's kind of defining those prompts as well. We're kind of put into this thing that looks a little bit like a development environment. So you can see the code getting generated. Off to the right, this really nice directory structure in the center. If you're a developer, you can kind of, one button click, save this as a public or a private GitHub repo. And if Gemini encounters any errors along the way as it's building this app, what it will do is it will take that error, it will put it back into the model, and then it will use all of that to kind of, to kind of self heal and like fix itself before, before the app exists. But it looks like we've already got the app. It's done. Yeah, it's done. So let's test it out. I'm going to begin this quest. It says DND character forge, which sounds very, which sounds very, very cool. I'm going to take a picture. I'm going to forge my character. I used the webcam to take a picture. So that looks like it was pulled up correctly. It's consulting the Elder Scrolls and forging arcane artifacts. This is very cute. So it's got my before image, it's got my after image. That's super cool. I am for anybody. I'm a wood elf ranger and I'm chaotic good. And it's got like the, the same, it's got kind of like the same peace sign. It's got the same facial structure. My hair is a little bit purple, so maybe that's inspiration that I make a, a lifestyle choice change for like dying.

Predicting the future, it seems as well.

Exactly. And then, and then it's got strength, dexterity, wisdom, charisma, all that good stuff as well as a backstory. So Lyra grew up in a secluded elven forest, learning the ways of nature and the bow from an early age. This is awesome. I love it.

This is nuts. This is. And, and yes, she literally did this live in real time. Just said Nano Banana. It obviously knew to use Gemini 2.5 flash and she used her webcam right there. Like just vibe coding in the airport. So now how can you actually use this and share this? Walk us.

Yeah, well, of course I want you to join my Dungeons and Dragons campaign. So, so like, and all of the folks who are listening today. So I'm going to go ahead and click this little rocket symbol that we have on the right that allows us to deploy the app automatically to Google Cloud. I'm going to select a cloud project, I'm going to click to deploy the app, and then it will kind of generate a unique URL behind the scenes. So this URL you can share with friends, family, coworkers, and it will also hook up everything else that you need from a cloud project perspective. So storage, logging, cloud run like it hooks up all of the, all of the infrastructure so you don't have to worry about any of it. It's doing it in a secure way because it's using Google Cloud. And then if I click, this kind of cloud project will also give me insight into logs. So all of the things that were required to build this app and then also billing. So if this happens to go viral, I can kind of see. And also no judgment. This is my personal, my personal GCP account. It shows me all of the services and all of the costs associated for the different products that have been used. So it's, it's really, really nice to have all of this handy. And we were able, like, this is effectively a hackathon project using Nano Banana deployed with the unique RL that took us less than like two minutes to build. It's kind of bonkers.

Amazing. Yeah, amazing. Like, I, even as, you know, Paige, Paige was talking there, I, I, I already used it. I already have my own character. Not as much strength and dexterity as, as Paige, but, you know, I got to start somewhere.

I got to start somewhere.

All right, so, so Paige, you gave us a great, you know, Gemini use case for non technical people. We dove into and talked about some different Nano Banana use cases. You built something literally live in in like a minute. So I want to ask you this as, as a third thing. What's been your personal, you know, favorite? Either, either use case of Nano Banana, your favorite project you've seen maybe someone else build what's kind of that, that one thing that you always go to, you're like, this is such a great use case. Because I feel there's like unlimited use cases.

There's, there are so many use cases. One of my, one of my favorites has really been because often I don't have time for professional, like, you know, the kind of professional headshots that you might put on LinkedIn for myself and I know that my coworkers don't either. You can ask for Nano Banana to create professional headshots. I've been using it to colorize old family photos, which has been like, really exciting for my mom and for all of my like family members to see. There's also a really great use case where you can give it an aerial satellite image of a place and then have it turn into an isometric building that you could use for a game. So it looks like kind of a pixelated, a pixelated scene from a, from a video game. You can have it. Imagine what a street view light looks like for a given place that you might have on satellite images. But one that everybody on the call can try out today. That's, that's part of our kind of Gemini app showcase is actually called Past Forward. It was created by my colleague Amar. And what you can do is you can generate yourself through the decades, which is quite cool. So if I was to kind of upload a photo. So I'm just going to select an old photo that I have kind of hanging out on my, hanging out on my, my laptop. I'm going to click Generate and then this app behind the scenes and I'm going to zoom out a little bit so folks can see it creates different images from the 1970s, from the 2000s and kind of allows you to kind of see yourself in all of those different. I love the aviator glasses from the 70s, like holy moly. But it. And also the big pair from the 1980s. Like, I would absolutely rock that. Like, this is, this is really, really cool and is something that you could do today with your friends, your family and kind of have, have displayed along the way as well.

The, the possibilities are, are literally mind boggling. Right?

Like, absolutely.

Paige, I could have you show us hundreds of demos every day and I still don't think we could honestly scratch the surface of what's possible in Nano Banana. But you know, as we, as we wrap up today's show page, what would you say is your one, you know, most, you know, practical or tactical, you know, piece of advice that you have for people to start going in and maybe either using Nano Banano on the front end in AI Studio or building with it. What's that one piece?

Yeah. So I would strongly, strongly recommend. I know there's a lot coming out. DeepMind. It feels like we're releasing new models and new features every four or five days. So it's been a hard thing to keep up even for all of the folks working on the team. But really, the best way to kind of understand what these models are capable of is to test them out yourself. So roll up your sleeves, Slate. Google's made a lot of these models available to use for free via the Gemini app, via Notebook, via AI Studio. So if you have something that you've been curious about, if you have like a. A workflow that's really frustrating that you would love to automate, just go in, ask Gemini like to. To help you. To help you write a prompt. If you don't know how to prompt yourself or if you're just getting started and just test it out. And I guarantee you, if you try it, if you keep iterating, if it doesn't work the first time, I think that you will be surprised to see how far these models have come over the last six months. And given that this is a new space, given that this is a complete reimagining of everything, machine learning and AI over this past couple of years, you're getting started at pretty much the same place that everybody else getting started. So really, there's still time, there's still room to get in on the ground floor. Don't be afraid. Like, just test things out.

I don't. I don't know what's more impressive, you know, what we've seen in Nano Banana or what Paige Bailey just delivered in 27 minutes in terms of value, My gosh. So if you miss anything, if you're listening, make sure you go check out our newsletter. We're going to be recapping it all. But, Paige, thank you so much for taking time out of your day to join the everyday AI show. We really appreciate it.

Thank you. Thank you for everything that you're doing to kind of bring AI to everyone and to make it approachable and accessible. We appreciate you and can't wait to listen to your next podcast.

All right, well, hey, every day, so you can't. It's. It's hard to miss it. It's hard to miss it. All right, so thank you, everyone, for tuning in. Like I said, if you haven't already, please go to your Everyday AI dot com. Sign up for that Freed Line newsletter. We'll see you back later for more Everyday AI. Thanks y'.

All. Awesome. 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.