I Made Games, Apps & Art Using Gemini 3 — Zero Code Needed

0:00:10 Welcome to the Next Wave Podcast. I’m Matt Wolf, and last week was a big one. We got some fresh
0:00:15 new models from Google. There was the official launch of Gemini 3, as well as the brand new
0:00:20 Nano Banana Pro model. And today I’m going to talk about both of them. And then I sit down with
0:00:25 someone who has been right in the center of all of this, Tulsi Doshi, the head of product for
0:00:31 Gemini. This episode is packed. We’re talking about what’s actually new, what surprised the Google
0:00:36 team as they were building these models, and how it changes the way normal people like creators,
0:00:41 entrepreneurs, and absolutely anyone using AI interact with these tools day to day.
0:00:47 And towards the end, Tulsi shares her own personal favorite ways to use the new models,
0:00:51 which genuinely opened my eyes to a few use cases I hadn’t even considered yet.
0:00:56 But before we jump into that interview, I want to quickly break down the biggest announcements,
0:01:00 show you some of the cool stuff I made with Gemini 3 and Nano Banana Pro,
0:01:05 and give you a sense of why these updates matter way beyond the tech headlines.
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0:01:42 All right, let’s start with Gemini 3, because this is a pretty substantial leap from the previous
0:01:48 generation. Google is pushing this one as their most capable, most general-purpose model yet,
0:01:52 and they’re not just saying that. They’re actually shipping a bunch of meaningful upgrades.
0:01:57 It had big jumps in reasoning and instruction following. This is the first Gemini model where
0:02:02 you can feel the internal steps happening. It doesn’t just give answers, it shows its work in a way
0:02:07 that feels much more intentional and transparent. There’s a huge difference when you’re doing
0:02:12 planning, research, or anything multi-step. It’s got way stronger media understanding. Images,
0:02:17 documents, charts, screenshots. Everything processes way faster and way more accurately,
0:02:24 especially in mixed-media situations. It’s noticeably more stable and aware than earlier Gemini
0:02:30 versions. It’s better at coding, better at math, better at multi-step tasks. This is the biggest jump
0:02:36 in the general-purpose AI assistant category. It recovers from the mistakes, sticks to constraints,
0:02:42 and handles longer, more complicated prompts without drifting. And the whole thing is available
0:02:48 inside the Gemini app on day one. So you don’t need any sort of special access or external tools,
0:02:53 it’s just there. And I need to give a quick thank you to DeepMind for the early access on all of it as
0:02:59 well. So what I want to do real quick, instead of just talking about this new model, is to show you a
0:03:04 couple cool things that I actually did with Gemini 3. Because once you actually put it to work,
0:03:10 that’s where the fun begins. It was able to look at my calendar and my email and my filming schedule
0:03:17 and help me map out a filming, editing, and publishing schedule based on criteria that I gave
0:03:22 it for how I want to release stuff. I got it to solve some pretty complex math problems and actually
0:03:28 show its work. I don’t know why this impressed me so much, but the way it presented the math as it did
0:03:35 it just like really blew my mind kind of. I got it to create a Minecraft clone here using only HTML,
0:03:43 CSS, and JavaScript. Then I took away the HTML, CSS, and JavaScript constraints and actually got it to create
0:03:51 an even better Minecraft world with more color blocks and even closer version to the reality of
0:03:58 what Minecraft actually is. With just one prompt, I got it to make a recreation of advanced wars that
0:04:06 actually works and you can see the AI actually thinks and makes moves as well. I got it to make
0:04:11 this happy birthday singing page with balloons and confetti that actually plays music.
0:04:28 You can’t say you’re not impressed. I made a game. That’s a clone of vampire survivor that actually
0:04:35 works. And on top of Gemini three, they also rolled out a new agent mode that has a cloud-based browser
0:04:41 that can actually take control of your screen and take actions on your behalf. Like this demo where I
0:04:47 got it to book dinner reservations for me. I’m just clicking through the screenshots, but it opened up this
0:04:54 browser here and step-by-step went and booked the restaurant for me. I think probably my favorite demo
0:05:00 was I gave it the PDF attention is all you need, which is a really complex technical paper about the
0:05:07 transformer architecture. And it put it into this visualizer that pretty accurately explains what the
0:05:14 paper is about. So yeah, Gemini three is pretty cool, pretty powerful. I actually made a full breakdown
0:05:19 video of it on my main channel. If you want to see all of the various tests that I did with it.
0:05:25 All right, now let’s switch gears to the one with the fun name, nano banana pro. So this one launched
0:05:32 on Thursday and it’s part of the new image generation system built on top of Gemini three pro image.
0:05:36 If you’ve used any of Google’s earlier imaging models, this is a pretty noticeable upgrade.
0:05:42 And the best way I can describe it is it’s fast, incredibly literal and shockingly good with fine
0:05:48 details. But here’s the biggest updates. Much better text rendering inside of images.
0:05:55 It can do menus, logos, posters, comic panels, all with accurate spelling. And it supports every
0:06:00 language the Gemini app supports. And that’s actually something that most image models are
0:06:05 still struggling with. It’s got more consistent characters and object control. If you need the
0:06:11 same character across multiple images or multiple angles, it sticks much better. This is huge for creators
0:06:16 making scenes, storyboards, thumbnails, or anything sequential. It’s got improved realism in materials.
0:06:23 So skin, fabric, glass, food textures, everything is cleaner, sharper, and less AI shiny than earlier
0:06:30 models. And it’s built directly into the Gemini app as nano banana pro. So you can generate, edit,
0:06:35 and iterate without switching a bunch of different tools. I was able to play with this one in early
0:06:42 image. And it made some genuinely wild images. So let me pull up a few of those and walk you through
0:06:49 them real quick. I was able to tell it to make this cafe menu that was bilingual with both English and
0:06:54 Japanese text. And then I also wanted it to make the images look like they were made out of clay. And it
0:07:00 nailed it. I mean, even the small text, usually when you have small text in these images, it becomes kind of
0:07:06 gibberish. Well, these are actually readable. Single shot of rich, concentrated coffee. And these look
0:07:12 like clay images, all with just one prompt. And it did exactly what I wanted it to. I had to create
0:07:18 three ultra clean modern logos that each spell the word wolf in different languages. So we got Russian
0:07:25 kanji and Italian, and they’re all different materials. We got metallic, we got fur, we got glass,
0:07:32 and it nailed it. Also, you can sketch on images. So I gave it this image of made on YouTube. And then I
0:07:38 sketched a stick figure and I wrote giant person on it right here. And check this out. It made this same
0:07:44 image, but put a giant person in the background, even match the colors that I drew them with. I redesigned
0:07:51 the Apple vision pro by putting ears on it and green eyes. And well, it gave me a new mockup with ears and
0:07:57 green eyes. You can have it combine up to six images. I gave it a dog, a surfboard, a city skyline,
0:08:04 a good vibe sign, and an empty road. And it generated this image with all of those. Here’s another one
0:08:09 where I gave it six different articles of clothing and told it to combine them into a capsule wardrobe
0:08:15 style sheet showing mix and match looks. And well, it did what it was supposed to. I mean, they’re not
0:08:20 good looks, but it did what it was supposed to. I had to create a pencil sketch drawing of a futuristic
0:08:25 gadget. And then once it was done, I said, all right, now let’s turn it into a realistic mockup.
0:08:32 And it did both perfectly. I built a brand mockup for a company called sky sip with colors and bottle
0:08:39 mockups and even fonts. It can resize photos into different aspect ratios without like compressing
0:08:45 screwing up the image. I gave it this image of me and Matthew Berman, which was a nine 16 vertical photo
0:08:54 and told it to make it a 16 nine wide photo. And it did it. You can transfer styles. I gave this image
0:09:03 of a watercolor along with this image of a group of us at GTC. And it took the watercolor and the image and
0:09:09 blended the watercolor style into the image. So it’s really, really capable, especially when it comes
0:09:14 to image editing. I’ve been pretty blown away by what it’s been able to do. This is another one
0:09:19 I’ve had early access to and was able to play with for a couple of weeks. So thanks again to deep mind
0:09:24 for that. And it’s also another one that I did a deeper dive video on, on my main channel. If it’s
0:09:33 something that you want to dive deeper into. All right. So you just saw what Gemini three can actually
0:09:38 do. It’s legitimately powerful, but here’s the thing. Most people watch these demos and then
0:09:45 they don’t actually use it. That’s why we’re giving you the Google Gemini at work guide. It has the exact
0:09:52 prompts and workflows that marketers are using right now to speed up research, create better content and
0:09:58 actually drive results. Scan the QR code or click the link in the description. Now let’s get back to the
0:10:06 show. All right. Now that we’ve gone through everything new and I’ve shown you some of the stuff that I’ve been
0:10:12 experimenting with. Let’s get into the conversation that I had with Tulsi. Today’s guest is Tulsi Doshi
0:10:17 from Google. Someone who has been deeply involved, not only in the launch of Gemini three, but also in
0:10:23 shaping how these models evolve, how they’re used and how they show up inside the products you and I get
0:10:30 to use. We talk about what’s actually different about Gemini three behind the scenes, the unexpected
0:10:35 discoveries the team made while building and testing the model, her personal favorite use cases, which
0:10:41 kind of surprised me and how Google thinks about making these models more helpful, more intuitive and
0:10:47 more aligned with real human workflows. It’s a super fun conversation and gives you way more insight into
0:10:53 how these models are born and why they behave the way they do. So let’s jump over and chat with Tulsi.
0:11:00 Hey, Tulsi. How’s it going? Good. How are you? Good, good. I’m sure it’s been busy couple weeks for you
0:11:05 guys. Yeah, but I think, you know, what’s exciting right now is I feel like it’s just busy always.
0:11:11 Yeah, that is very true. It seems like there’s never a dull moment lately. So I’ve had access to Gemini
0:11:17 for four days now or so and had a chance to play with it. Nice. Honestly, I’m actually pretty blown
0:11:25 away. Like I got it to like, I got it to like one shot, like a mini, like Minecraft clone and like
0:11:31 all sorts of crazy stuff. And I asked it to generate the happy birthday song for me and it generated the
0:11:37 song, but it built like this whole user interface with like floating balloons and the music playing and
0:11:44 like a, a sing along karaoke from a single prompt of make the happy birthday song for me, which was
0:11:48 just, I don’t know. That was crazy to me. It’s been really fun just to see. Like, I think for me,
0:11:54 it’s just like the visual strength of the model is so good in terms of what you can do, like zero shot
0:11:59 that it’s like, yeah, it’s just awesome. Yeah. Yeah. Yeah. So I do have a handful of questions. I have
0:12:04 six questions total, so I won’t waste too much of your time. I’ll just kind of run through the
0:12:09 questions and we’ll take it from there. Yeah, let’s do it. Cool. So my first question is with
0:12:16 Gemini 3, was there something that changed with how the model was trained or like the training data on
0:12:22 the model? Like what made Gemini 3 such a big leap over previous models? I mean, I think honestly,
0:12:26 Matt, it’s a combination of multiple things. And I think that’s actually what makes it so exciting
0:12:32 is like, it really does feel like it’s a team effort here in terms of both architectural changes on the
0:12:38 pre-training side, but also translating those then downstream on the post-training side for what we
0:12:44 were pushing in terms of tool use improvements, in terms of multimodal improvements. I think the other
0:12:50 thing is with Gemini 3, it was really an exercise in us seeing and learning from the feedback from 2.5
0:12:55 in terms of what were people really loving about the model, but also where were people potentially
0:13:00 giving us feedback about where the model wasn’t as usable or wasn’t as exciting. And then how do we
0:13:04 actually take the combination of those two things and try to build something awesome? And I think it’s
0:13:06 been like the culmination of all of that work.
0:13:10 Gotcha. Gotcha. So was it like a fine-tuning thing? Like you were kind of constantly fine-tuning it
0:13:16 towards the feedback that was given? Yeah. So we were doing that as well as then building on kind
0:13:20 of the strengths of the model itself in terms of the work that our pre-training teams have done. So
0:13:25 it’s really been a combination of both of those things. Gotcha. Now I know you personally, you’ve had
0:13:31 a big focus on safety and that sort of thing from my understanding. Has Google and your approach to
0:13:35 safety changed at all over the last sort of several Gemini iterations?
0:13:40 I mean, I think what’s been for me, at least important is I think safety has been kind of
0:13:45 central to the model development process from day one. And that was true for Gemini 2, 2.5,
0:13:53 1.5, 3. So I don’t, in that sense, no, because it’s been a consistent focus and a consistent
0:14:00 prioritization across our models. I think with Gemini 3 and with any model that is the most capable model
0:14:06 we release or the next generation of models, we spend a lot of time on frontier safety.
0:14:11 And one of the things that we really invested in with this model is making sure to do kind of robust
0:14:16 external safety testing to really invest in what are our kind of understanding and protections of this
0:14:22 model. How do we get that right on day one? And so I think that’s been really important. One thing
0:14:27 also that’s related to safety that we spent a lot of time on with this model is also things like
0:14:33 reducing sycophancy and trying to make sure that the model has a more balanced approach to how it
0:14:37 responds. And I think that is also like, it’s not quite safety, but it’s also in the vein of
0:14:40 how do we build a really good experience for users?
0:14:47 Yeah, it totally makes sense. So I’m curious, is there anything that’s not in Gemini 3 that you
0:14:49 expect to be in future Gemini models?
0:14:56 Ooh, good question. I mean, Gemini 3 is a huge step up in a lot of ways. I think there’s two areas I’m
0:15:03 excited about continuing to push. One is, you know, right now, Gemini 3, one of the things you saw this
0:15:09 morning is we showed a lot of really rich visual outputs, really interactive outputs. And a lot of
0:15:14 those are very code driven, right? One of the things we want to continue to push on with Gemini is
0:15:20 pulling these modalities together. Right now, Gemini is multimodal in, but it still outputs text,
0:15:26 right? How do we also like really bring that rich output modality to that kind of mainline model?
0:15:31 So that’s one area that I’m like really excited to continue to see us push on and push forward.
0:15:37 Another area I’m really excited for us to like continue to push on is really how we continue to
0:15:44 iterate on multi-turn tool use, kind of long journeys. This model does an amazing job at a lot of those
0:15:50 things. And you can see that in some of the evals like vending bench or sweet bench or tau 2. But I
0:15:55 think that’s an area where we know that’s where the world is going in terms of kind of complex actions
0:15:59 and investments. And I think that’s where we want to continue to help climb, I think.
0:16:04 Yeah, yeah. One of the really cool things that I saw when I was playing with Gemini was I gave it like
0:16:09 seven tasks in a row and said, go through and do all seven of these tasks. And then the output was
0:16:13 like, all right, here you go. Here’s one done. Here’s number two done. Here’s number three done.
0:16:18 And it just did all of these tasks one after another from a single prompt, just one initial
0:16:23 prompt at all, which was really cool. The other thing I’m really excited about, but haven’t tested
0:16:29 with Gemini 3 is the longer context with video inputs. Like I think there was a very, very brief
0:16:34 mention of like, you can actually give it like up to like hour long videos now, and it will actually
0:16:39 understand the context, which is something I’m super excited to test as somebody who makes YouTube
0:16:43 videos, you know? Yeah, actually, like my husband also makes YouTube videos. And so this is something
0:16:48 that like I’ve been watching more closely. And actually there’s a demo we pulled together. He’s a huge
0:16:54 pickleball player where you can actually like have the model critique your form in, in many of these
0:16:58 kinds of cases. And I think that’s the kind of thing, like there’s both the sheer high quality of
0:17:04 video understanding at like, you know, very specific FPS. So you can actually like capture this kind of
0:17:09 nuance and then actually have the model reason over it, which is awesome. Yeah. Yeah, absolutely. I mean,
0:17:13 I used to actually take golf lessons and that was one of the things when I was taking golf lessons is
0:17:17 they’d take me into a room, show me a video of my swing. And then the instructor would be like,
0:17:21 see right here, the way you kind of move your arm there. If you just adjust your elbow up a little
0:17:27 bit, you know? So that’ll be really cool to have that sort of built into like, I don’t need the
0:17:32 trader anymore to show me how to do that. I can analyze my own stuff. That’s really cool. Was there
0:17:38 anything that Gemini three was capable of doing that you guys didn’t expect it would be doing like any
0:17:42 sort of like emergent capabilities? We’re like, Oh, wow, that’s cool. We didn’t see that one
0:17:47 coming. It’s interesting because I feel like we’ve been iterating on Gemini three for a while. So
0:17:51 in some ways we were able to take some of the early emergent capabilities where we were like, wow,
0:17:56 this model is actually like surprisingly good in this area and then build on top of it. Right.
0:18:02 Kind of take that further. I think we knew the model was going to be great in multimodal understanding,
0:18:07 but I think just how versatile it is across all of the different aspects of that, I think has been
0:18:12 kind of a pleasant surprise. I think for me, another one that I actually would say a lot of folks in the
0:18:17 building won’t necessarily tell you is a surprise, but one that I am like very excited about actually
0:18:23 is it’s multilingual performance. Like the model is very, very strong across languages. And then you
0:18:28 can think about these intersections, right? So like the intersection of multimodal with multilingual
0:18:33 means you could, for example, like there’s someone who created a demo of like taking handwritten
0:18:39 Korean and then translating that into a web app. Right. So there’s a lot you can do now across the kind
0:18:43 of rich combination of these things that I think is actually really cool. And then leads to a bunch
0:18:48 of emergent properties that is actually really awesome. Awesome. When it comes to benchmarks,
0:18:54 I’m curious if there’s like any specific benchmark, you guys put a little bit more weight on or trying
0:18:59 to like optimize for like, what is the benchmark that inside of Google people are like, let’s see
0:19:04 how we can push this one. Yeah. I mean, I think part of what we’re actually really proud of is I think
0:19:08 one of the things that we hold really steadfast as a goal is we really want a well-rounded model.
0:19:14 And I think that really means two things. One, it means not just focusing on a single benchmark,
0:19:19 but actually looking across a slate. And so when you see the benchmark table that we’ll share,
0:19:23 it basically like has a set of benchmarks that are more like academic reasoning benchmarks,
0:19:29 a set of benchmarks that are multimodal, a set of benchmarks around code and then tool use.
0:19:33 And so what we really do is try to say like, hey, there’s a set of capabilities we really want this
0:19:39 model to excel at. And what is a range of evaluations that will help us kind of do that?
0:19:43 And then the thing is, is it’s also important actually, not just to look at those benchmarks,
0:19:48 the kinds that you publish externally, but then internally, we also have evaluations for model
0:19:54 behavior, for style, for instruction, following these types of kind of core competencies of the
0:19:59 model that are actually just as important. Right. And then you also have, you know, sources like
0:20:04 LM arena that allow you to get live feedback. And so I think the combination of all three of those
0:20:09 things allows us hopefully to have a model that doesn’t feel like it’s just really jacked up at
0:20:14 one thing, but that it actually like can kind of meet you where you are, which I think is super
0:20:19 important. Right. Right. Yeah. I’ve been personally trying to find like the best benchmark to hone in on
0:20:26 for like, is this model better for general consumers? Right. Like, so my audience on my channel
0:20:32 is mostly focused on general consumers who are just, they kind of want to stay looped in on it,
0:20:35 but they may not be coders or the most technical people in the world. They’re just kind of
0:20:41 wanting to be looped in. And for me, I mostly look at the LM arena because I feel like this is what the
0:20:46 people are saying. Like anybody who types a prompt, this is what they’re saying they like the best,
0:20:51 but I’m still trying to find like that benchmark to hone in on and be like, this is what you guys should
0:20:56 be paying attention to. Yeah. I mean, I think one area that we benefit from when we think about
0:21:01 consumer value is also that we have these amazing consumer experiences. And so one of the things,
0:21:06 you know, we try to do in partnership with, for example, the Gemini app is run live experiments,
0:21:11 get user feedback, you know, on AI studio, we again, run experiments. We allow people to side
0:21:16 by side vote as they’re building in experiences in AI studio or chatting with the model in AI studio.
0:21:20 And so I think actually it’s, it’s interesting when you talk about like, what is just a good model
0:21:27 for consumers? The best signal in my view is actually to just get feedback from consumers in the experiences
0:21:33 that they’re trying the model in. And so for me, that’s the big thing we’re going to continue to invest
0:21:37 in is actually just trying to get as much feedback from our users as possible about what they love and
0:21:42 don’t love about the model. And then using that to improve the model, you know, rather than a benchmark,
0:21:43 it’s the most kind of direct flywheel.
0:21:51 Absolutely. So this is my last question. What is your personal, you, Tulsi, what’s your favorite
0:21:56 use case of Gemini 3 or use cases? Like what are some of the ways you’re using it right now that
0:21:58 you’ve found really impressive?
0:22:02 Yeah. So, I mean, I’ve been using Gemini 3 for the last few weeks. I’ve been like lucky to kind
0:22:07 of get to try a bunch of things. I think similar to you, the ones that really pop out as like wow
0:22:12 moments are when you’re trying to create some of these like just amazing web app experiences that
0:22:17 are like super fun. So my family speaks Gujarati, which is a language from the Western part of India.
0:22:24 And I’ve been actually really enjoying trying to take Gujarati phrases, poetry, stories, and then
0:22:29 actually like not only translate them, but have the model build on them. And so the models like
0:22:34 writing capabilities, multilingual capabilities, and like that’s something that I haven’t been able
0:22:39 to do as much because Gujarati is not a language that is super common on the web. And so it’s
0:22:44 actually one where it’s harder to capture that nuance. And I find that Gemini 3 has been able to
0:22:48 do that much better than I think I would have expected, which is pretty cool. I’ve also just been
0:22:53 honestly starting to use it in my daily life, which is pretty awesome for a wide range of things,
0:22:59 including creating like cool invitations that are more interactive for like events and just
0:23:00 it’s been really fun.
0:23:04 Awesome. Well, thank you so much. I added a few bonus questions in there because I had some
0:23:10 follow-ups, but that was my list of questions that I had ready for you. Is there anything else that,
0:23:12 you know, maybe we should touch on that we didn’t touch on?
0:23:18 We touched on a lot of things in that original brief. So I feel like we covered most of the ground.
0:23:22 One thing that you and I, I think, didn’t talk as much about, but we kind of touched on briefly is
0:23:27 this idea of like Gemini 3 should be able to bring anything to life and help you bring anything to life.
0:23:32 I think one of the things that’s really powerful is this kind of transitioning between formats,
0:23:37 right? So one of the things that I think Robbie talked about is this kind of generative layouts
0:23:42 and the model being able to help you kind of visualize concepts and actually like
0:23:47 help them be more interactive as you engage with them. I think one of the things we’re really excited
0:23:53 about is how we can actually leverage Gemini to build these like richer experiences for users and for
0:23:59 consumers. I think the other is that Gemini 3 is super widely available. And I think that’s
0:24:02 something we’re really excited about. Like we’re really excited about how do we bring the power of
0:24:07 Gemini 3 everywhere? So it’s not just a model that is great for developers. It’s also a model that brings
0:24:11 a ton of value to you in the app. It’s a model that brings a ton of value to you in search.
0:24:16 And I think the idea of the model should meet you where you are and then help improve your
0:24:19 experience. I think that’s means to me, hopefully more than anything.
0:24:23 Totally. Yeah. You just reminded me of something that I tested that I thought was really cool.
0:24:28 I actually took the attention is all you need paper and fed it in and said, generate an animated
0:24:34 visual that explains this paper. And it actually did make an animation that was really cool that
0:24:40 explained like, you know, if a sentence has it in it, it looks back through the sentence to find out
0:24:45 what it means. And it created this whole like visual explainer of the transformer architecture.
0:24:49 And I was like, this is wild. So yeah. And that’s amazing. Right. Like just like,
0:24:54 and I think about it as like, if you go back to kind of Google’s mission of like make information
0:24:59 universally accessible and useful, like I think that kind of example where you can take information
0:25:04 that is super dense and super complex and then actually translate it to a format that is like
0:25:07 really rich and understandable. I mean, that’s awesome.
0:25:12 Yeah, absolutely. Absolutely. Well, Tulsi, thank you so much. This has been awesome. And I really,
0:25:16 really appreciate you taking the time out of your day. I know there’s a lot going on over there right
0:25:20 now. Of course. Thank you. And I’m excited to see the content. Please keep giving us feedback by the
0:25:23 way, as you’re playing with the models. That’s awesome. Absolutely. Yeah. Thanks again. Really
0:25:29 appreciate it. Thanks. Thank you so much for listening and a huge thank you to Tulsi Doshi for
0:25:35 joining us and giving such an inside look at what’s happening with Gemini 3. If you enjoyed this episode,
0:25:39 hit that like button, subscribe to the show and share it with someone who’s curious about where AI is
0:25:45 headed. It helps the podcast grow and lets us bring on more amazing guests. Thanks again. I really
0:25:48 appreciate you being here and hopefully we’ll see you in the next one.