Mindblowing o3 Prompts, OpenAI Models & More AI Use Cases
Igor covers OpenAI's O3 model, a new thinking model now in ChatGPT for paid users with full tool access. It outperforms previous models at research and image geolocation tasks, though it makes occasional errors. The episode also covers GPT-4.1, Notion Mail, Canva AI, and competing deep research features from Anthropic, Grok, and Gemini.
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This is the first week in AI where many people claim that we reached AGI. And look, that's a bold claim and it's definitely debatable. But the fact of the matter is, with the release of O3 from OpenAI, which we'll talk about today, you'll see in this very video chatgpt doing things that were unthinkable before. And the crazy thing is, even beyond that, this was an amazing week in AI, state of the art video generation, many more ChatGPT updates, multiple other models coming out of OpenAI and so much more. In this week's episode of AI News, you can use the show that rounds up all the AI releases from this week, filters by the one that you can put to work today and then we test them or have a first look at all the things that came out in Generative AI this week. As you might see, I'm still on the road my last week here in Japan. What a country. I don't want to waste too much of your time here. We're here to speak about Generative AI releases, but I will be using some pictures from my trip as a part of our testing here. All I'll say is if you're a tech enthusiast and you haven't been to Japan before, oh boy, does this country have a lot in store for you. And it's so peaceful. All right, let's get into this, starting with the OpenAI releases. So there's multiple things here and I'm sort of going to bundle this because we had two big announcements. One of them released multiple developer focused models that are not available in ChatGPT. This is the GPT 4.1 series. These are non thinking models. Again not in ChatGPT, only available through the API for builders. And the second announcement, and that is the big one this week is the release of O3, the full version, just O3. That's the brain that was powering deep research and the thing that made it so damn good. You can now access it through through ChatGPT on All Pro plus and Teams plans coming to the education and enterprise plans in about a week from now. Just to make it clear, this is not accessible on the free plans. With this O3 release they released also all four mini models. As you can see here on my Pro account, There's one called 04 Mini and one called O4 Mini High. But in this video I really want to focus on the O3 model because I think this is the one that most people care about. On this show. I always try to focus on the tools that really push push the bar on what's possible with AI. And while 04 and 04 mini high are impressive and perform better than 03 mini and O3 mini high before that naming is a mess. The joke has been made a thousand times. They'll fix it soon. Point is, the one model for most users that really makes a difference this week is O3 because it's head and shoulders above anything that we had access to before, except maybe deep research. But this thing is way faster and just different than certain behaviors. So before we get into some examples and use cases here, which I find absolutely fascinating. Fascinating. I can't wait to show you some of these. Let's just briefly outline some of the specs and which models you would care for based on what you're doing. So As I said, O3 is sort of the king here as they show in the blog post where you can see all the details of this. It leads on all the benchmarks and beats everything including this O4 mini model on both settings. So that's a really important takeaway if you want the highest intelligence in ChatGPT. As of now, you pick O3 on a paid plan. The other release this week which was GPT 4.1, GPT 4.1 mini and GPT 4.1 nano, those are developer focused models that are particularly good at generating code. These are only accessible for the API where you pay per request and you can't find these inside of ChatGPT. And again, these are developer focused models both in terms of usage and use cases. They're really good at generating code. And already a lot of tools are adopting GPT 4.1 under the hood. Like for example the sponsor of today's video, which we'll talk about later, which is Notion Mail, they instantly switched the model that helps you manage your emails to 4.1 because it just performs better and works better. So you can expect that increase in performance across all AI powered applications that used other GPT models before. But these are non thinking models. So let's not even even compare the benchmarks to these thinking models that are O3 and O4 mini because the thinking models are always going to crush non thinking models. So one more time, GPT series, non thinking models. There's a new 4.1 and smaller versions of it, O3 and O4 thinking models. O3 is the best one that you want to put to work today there. And if you wonder how they compare to some of the competition on the benchmarks, well, we're starting putting Together a table to show you all of that. But in the middle of creating this table, we found a website that already does this really well. Not affiliated. Just think this is a great resource. And as you can see, you can sort by the various benchmark benchmarks that have been published, for example, in GPQA. O3 leads above all the competition here. Anyway, let's get to the more interesting part here because this is a model that you'll be using and I want to focus on O3 here. I think we talked about the different alternatives and releases and benchmarks enough here. The thing that really matters to me and what I really try to make the point of this channel is how to democratize the access to all of these tools, techniques, workflows. And I haven't seen a tool since deep research that does it as well as O3. Now, because this is a big point, O3 isn't just a thinking model. It's a thinking model that has access to tools. What kind of tools? Well, all tools that ChatGPT has before, we saw limited releases, okay? When O1 Preview came out, you couldn't upload images, you couldn't upload files, you couldn't access memories, it could not use the data analysis tool, and so on. All of these things work automatically inside of O3. Now, this is a massive deal and we'll see why in the examples that I have lined up here next for you. But before we do that, I want to share one more thing. Look, I made it my life to stay on top of all of this, test all of it, build organization around it, and we teach it at scale to individuals and organizations. At this point, not often does a release stop me in my tracks and maybe rethink the way I use these tools, the way I use technology, the way it integrates into my life. As I said in the opener, many people are stating that this is the first release that can be considered AGI. And I think that statement is debatable because if you ask a hundred people, you're going to get 100 definitions for AGI. And I'm making these videos to give you my personal take on this. And the conclusion sort of is this model makes me want to rerun every prompt I have ever tried because it has the ability to surprise, surprise me with its intelligence and level of insight. And while that might not be the case on every single thing you run, it did happen regularly to me in my first initial look here. So with that being said, let's have a look at some of the things that I Tried here right away. One of them just being this quick prompt from Matt Schumer on Twitter here, which says, do intensive research on Igor Pagani and give me a massive report on everything you find. Obviously you would replace my name with your name here and you could try this for yourself. And what happens here is just so different from anything we've seen, except maybe Deep Research. It goes ahead, it starts thinking, it starts making a plan. Then it uses the web search tool to pull up multip. It pulled up my Instagram, LinkedIn, my X. It found the company website, the company LinkedIn, we're now going to be posting twice a day on our company LinkedIn. Go check that out if you're on that platform. And it even found my SoundCloud, which I haven't used in years, but fair enough. Then it goes out and figures out that, hey, I even made a typo writing my own name. Embarrassing. And it proceeds to think about this, pull up more links, and it just finds like a dozen different LinkedIn posts and other websites, including some of my university, etc. And it comes up with something that Deep Research would always refuse, a profile on me with all the details. And it's quite comprehensive, while not being overwhelming, which sometimes is the case with Deep Research. So the guardrails on this are lower than what we saw with Deep Research, and it's quite impressive. It pulls together many, many relevant things. But let me tell you, there's like six or seven mistakes here that are just flat out wrong. And those are not mistakes that OFRI made, those are mistakes that websites that. This reference made, for example, it shows that my nationality is Austrian. That is actually not the fact. I lived in Austria for most of my life, but I still have a Slovakian passport. But the great thing is it gives it source right here. And this website here, I don't know much about, it's called Business abc. It just somehow assumes that my nationality is Austrian, and that's why O3 has it in its report. So it's not perfect, but clearly the big point here is that the guardrails are lower. With Deep Research, you could have never researched a person. And some of these other insights here actually made me lean back in my chair and kind of surprised me myself. It made a comprehensive list of all the different paid products we offer and it kind of extrapolated the signature content themes. That made me think like, yeah, we do. We did used to do a lot of AI workflow stacks and workflow videos and do less of these. These days we should do More of that, they get less views, but they seem to communicate more value. And then the overarching theme of the channel is productivity hacks. I mean, we're doing this in the form of generative AI tools and workflows, but that's what a lot of people here care about. So I thought that was great. And then it ran things like the SWOT analysis here in the end. And that's really one of the big points that I see here. It's not that none of this was possible before, but you had to bring in the context yourself. You had to know which prompts to use, and then you had to take the time to actually use them to wait for the results. And you think about it, re prompt things like that. This does all of it for you. You don't need to engage a search function. You don't need to give it specific websites that you wanted to consider. You don't need to prompt for it to come up with a structure like this. That really makes sense here. It just figures all that out for you with a combination of thinking models and tool usage. So obviously, you could take this little prompt, replace your name, run this on yourself or on anybody else. Maybe they'll restrict this down the line. But as of now, this works. And in deep research, this never used to work. This was one of the most interesting things that I wanted to try. Now it works in here, but. And the second use case that I want to share here, it really shows the depth of the capability. And this one was inspired by Matthew Berman on Twitter here, doing something that many people on the Internet are trying right now, which is giving it a picture and saying, figure out exactly where this person is. I did it on this wholesome picture of myself from the recent Japan trip. And O3 went off and started thinking about it. What it did is it cropped into the different parts and started analyzing them in detail, translating the Japanese language and interpreting some of these signs. Like, for example, here it says the crest has 16 petals. Although it looks a bit stylized, it zooms in onto different parts. And based on the caption and the text, it comes up with the conclusion that this is clearly the Meiji Jingu Shinto shrine. I hope I'm pronouncing that correctly. That is located in Shibuyaku, Tokyo, a park in the middle of Tokyo. And here's the clues it followed. And it even states that this is the inner precinct exhibition area, which is exactly right. Then I was like, wow, okay, so it didn't just get the location right. So it Even got the exact area within the quite small shrine. Right? Impressive. But it did have several clues here, like the text and the logo. So I wanted to give it something a bit harder. I took a second picture from my script, which had no text, no logos. Matter of fact, it's night and I figured there's no real clues on this one. And I asked the same thing, figure out exactly where this person is. And after about two and a half minutes of thinking where it started by cropping into the various parts, coming up with some assumptions and various locations. This could be maybe Kamakura's Hokokuji temple or the Kodaji temple in Kyoto. It started running some Internet searches to find more images that it could then double check with. And in typical O3 fashion, as you might know from deep research, it didn't just find a website or two, it found 11 ranging from TripAdvisor to specific travel blogs, to Reddit to YouTube. Like, yeah, it pulled up a YouTube video in the process to cross reference these snippets of this image. And it noticed things like, hey, it only shows one horizontal rail, while the other shrine that I was considering typically has two. But that could be a trick of the vantage point, so I want to verify it. And then it went ahead and looked for that specific trail that it assumed and again pulled in a bunch of different websites and and analyzed images on there to cross reference this. And eventually shows me multiple images that match mine and says that, no, this is not my first assumption, but it's a small hillside bamboo walk inside Kodai Ji temple in Kyoto's Higashiyama district, which is 100% correct. This is not some obvious spot. The main temple is at the bottom. You kind of have to walk up into the gardens. And then this is a small passage tucked away in the woods behind the smaller shrine that is behind the big temple. And it even says the person is standing roughly halfway along the illuminated bamboo trail that leads visitors back towards the main hall of kodaij. This is exactly correct. And it even gives you the exact coordinates. At this point, I was just blown away by the accuracy of this thing and by the fact that it looked up dozens of websites, looked at the images there, and cross referenced them, and could identify this. And I told myself, okay, okay, okay, gotta give it something even harder. How about something without a person and without a trail that people walk? So I looked at my camera roll and found this little snap of this beautiful little bird in a river here. And I just figured, how could it know this? I Mean, it's just a river with some rocks and a generic looking Japanese building in the back. Well, long story short, it got this right. What? But I thought it was really interesting that initially it pulled up Python and wanted to extract the metadata from the image, which I thought of already. And all of these images didn't include that. I screenshotted the original image to make sure that there's no GPS coordinates embedded in this to make this actually challenging. So that didn't end up working. So then again, it did what it did before. It made some assumptions, ran some searches, looked at all the websites, cross referenced this, and after only 43 seconds, it concluded that this elegant gray heron is standing in the Shirakawa Canal. And then again, it gave the exact coordinates and a description of where you can find this exact spots with other reference images that look similar to this. And yep, that's the exact location where I took this picture. God damn. How is it this good? At this point, I felt a combination of excitement, but also I was kind of pissed at the tool. I just figured like, how can it be this good? How can I break this thing? So I just took out my phone at this coffee shop where I was preparing this video earlier today. And I looked out and I was in a beautiful coffee shop overlooking the river of Osaka. And when I looked closely on the other side of the river, there were these rocks that were tied up in a net with some turtles sitting on it. I shot a little video of you so you can visualize what I'm talking about right now. And funnily enough, when I shot the video, a little raven landed there and all the turtles that were sunbathing kind of hopped off the rock. That was kind of a random moment. Anyway, with the telephoto lens on my iPhone, I snapped a picture of this and I just wanted to break it at this point, I just wanted to give it something that it cannot figure out. And yeah, when I ran the prompt after two and a half minutes, it actually got it wrong. It figured that this is in central Tokyo, which is not right, this is in Osaka. So then when I followed up and said it wrong, not in Tokyo, it did several weird things like running Python code on top of the images. And look, it found these signs and it tried to run some filters on top of it to extract the contrast to read what's on them, but the image was just too low resolution for it to read what's on the sign. Side note, if they plug in some of the top AI upscalers right now into this, it might have gotten this right, but after a total of 10 minutes of thinking, it actually figured out that these are the sandbags that line the inner mode of Osaka. Joe, the Osaka Castle in Osaka, Japan. Which is correct. I mean it's not exactly next to the castle, it's like a 25 minute walk down the canal. But just based on this low resolution image with one follow up prompt, it got it. I mean this is just crazy. So I hope this example shows you the capabilities of this thing. It's not just the fact that the thinking model is just better than anything we've seen before or free, but it's also the fact that it has access to Every tool in ChatGPT. And while before even some power users might not have took full advantage of the tooling in ChatGPT just because you don't think of it or often because one is too lazy. I mean, if you run a search, you kind of have to wait for it and then you need to take that context re prompt on top of it. Use the data analysis tool. Again, that took some time. It does all of that for you now and just presents you with the result. So this works really well if you do something that I refer to as goal based prompting rather than instruction based prompting. So you just tell it where you want to go and it figures out how to get it. That was always a strength of these thinking models. Yet any AI skill that you might have required over the past few years is still relevant here. Because if you know how to use this individual modalities, you can open up this thought process and re prompt it to do specific things for you if the results are not what you want. And there's still value to prompt engineering because you can do more intricate things like I started trying afterwards here, but I'm still playing and I don't want to spend an hour showing you every single use case that is interesting here. If I find enough, I'll do a separate video as per usual. But let me just say that many of these business use cases which I now teach in workshops like this meeting analyzer, just work better than anything before. In some cases they're just slightly better, in other cases they're stunning. And in other cases they're just the same as you would have got with a deep research or proper prompting. But one thing remains constant and that's that the bar to get some of these advanced results that even in combination with AI would have took quite a bit of effort to achieve. You can now get from a simple one line prompt and that the guardrails on things like searching for specific people were lowered on this release just unlocks a whole new world of possibilities. If that's AGI yet or not is up for you to decide, but it's certainly impressive. So that's my first look at O3 and then O4 mini and O4 mini high are just versions of it that are faster and then cheaper when you use them for the API. But for most people, O3 will be the one that you want to be really playing with right now. And then for developers, the three different variations of the 4.1 models are excellent at generating code. And if you need a non thinking model, that's the new go to. They will be depreciating the 4.5 in the API. By the way, that's also kind of interesting. Okay, so there's two more things out of OpenAI here. One of them is a mobile release which I'll just quickly cover. There's a new tab called Library where it just collects all of your images. So if you're generating a lot of images, they created this interface that is sort of more focused on that. That's really nice. But more importantly, there was one more OpenAI release this week which is OpenAI codecs. And I thought about spending more time on this, but at decided to focus this video on O3 because I think most people will get more value out of that. Plus this new release of Codec CLI that they by the way, fully open sourced, is a agentic product that works in the terminal and does pretty much exactly what Claude code does, which was released by Anthropic about two months ago. And I've been using that tool regularly since its release. It's really great. It basically runs in your terminal. So I guess you do need the very, very basics of development. But these are the things that with ChatGPT assistance you can figure out in like an hour. And then you can basically do things like drag and drop images into your terminal and let it build little applications for on demand on full autopilot. It's obviously the same thing that Anthropic did with Claude code. And I think it's amazing that OpenAI decided to open source this entire program and they said there will be more coming, but as of now, if there's one thing you should really check out this week, it's all free. Okay, let's see what's next. All right, so this next one is exciting for me because if you've been following the channel for a while, you'll know that I love using Notion. We use it for our prompt databases and the entire operating system of the AI advantage, whether that is the content production, the entire organization or the the community. Come to think of it, we actually cannot use it for absolutely everything. I try to avoid other apps at all costs. So we have one source of truth and also one source of context for our various AI workflows. And as they reached out and wanted to partner on a video, I was like, yes please. Because the new feature they're releasing is actually something we're interested in ourselves, and that is Notion Mail. It's a brand new email platform that launched earlier this week. And there's a couple of features that I want to highlight to you because you might just want to consider this for your workflow. The basic concept is simple. You just connect your Gmail to Notion Mail and it upgrades your regular inbox with a ton of AI powered organization and writing tools. When it comes to organization, they have the Auto Label system, which is my personal favorite feature in the Notion Mail release. And it's one of the things, when you start using it, you wonder how you were doing it before. Basically you just tell the AI that you want a new label and the AI does all the work of the labeling. Or you could also call it categorizing. And then once the AI labels all your emails, you can review the decision and tell it if it did well, basically fine tuning its decision making so you can rely on it moving forward. And sure, you have this feature in other email clients, but I found this AI integration to work particularly well as they're a new product and they don't have a million existing features that the new one has to comply with. And in practice it just works better. Next up is a feature that you've probably seen in other platforms too, but it's an important one. And that's when AI drafts your email responses. But Notion Mail has one twist that sets it apart from the competition here. And that's a fact that usually the AI writing your email drafts or the Agentic app that you kind of plug into, it lacks the context of, well, anything that matters. And guess what? As this is Notion, you can pull the context from your Notion pages into the email client really simply. So, for example, if you want to draft something and say, hey, you can find all the relevant information about the sponsorship and this page, you just type at. Pick the page. That's it. The AI has all the context from that page. Now obviously there's way more ways to add context here, but if you already work in Notion, this is the smoothest thing ever. Obviously, so those are two, but there's many more ways. Notion mail will make your email management smoother and it's completely free to use, so why not give it a try today? All you need to do is click the link at the top of the video description, link your Gmail account, and get started. Thanks again to Notion for sponsoring this video. And now let's get back to the next piece of AI news that you can use. Next up, we have some major updates from Canva with all of their AI integrations. And I always like covering Canva because I feel like they take a lot of the trends and things that have proven to work out there and integrate them into their suite of products, which is, I understand it as taking complex tools for visual communications and packaging them and then making them easy to use. And they already were doing a ton with AI, but now during their Canva Create conference, they launched several new features which I would like to show you, and I believe a few of these deserve our attention here. So we tried everything that is available now, and I think most significantly, this new updated Canva AI is really close to something that one might conceptualize as a visual AI tool that creates social media content for you. As you can see, we just took the example of a online dog product subscription brand trying to create social media content, and Canva AI assisted us in the process. Here, here. And what I like here is that gives you various options, right? They have a lot of these templates already, and this AI integration just helps you navigate between those and it really creates an experience that is very smooth for somebody who might not be involved in this at all. I mean, can you get higher quality results by dialing in every single detail? Sure. But is this one of the quickest workflows out there? Yeah, probably. I mean, look at that. You pick one of these presets and then you can edit all the text in there, hit export, and you're done. And honestly, it's kind of funny, it's getting to a point where for some of these smaller brands that just need to get their message out there, it's really hard to argue for some of these fully manual workflows where you pick your templates, fonts, maybe do photo shoots for custom imagery, put it all together in Photoshop, and then iterate on top of that, you just say what you want to get a bunch of presets, you click one, change the text and you're done. It's kind of great. And I think this is an easy recommendation for a lot of small businesses that just need content, right? But they went even further. They integrated some of these AI trends that we've seen across all different applications. I mean, the one that most people were surprised by is Canva code. Like Canva served the opposite of a technical audience, but now it can write code and create the landing pages and so on. We tested it again on our imaginary Doc subscription box business here, and hey, it built the interface with our onboarding flow with surveys and everything and canvas. So I found that interesting. But me personally, when I looked through all of this, one thing really caught my attention and that is the fact that Canva has now their own type of sheets integration. We can upload data and it works with it. Now, unfortunately, this is one of the features that isn't available as of now, but I thought this looked really interesting because you can add a sheet with data into it and then quickly process everything in there, making bulk workflows really simple. So from what they showed is you could upload the list of all the products or campaigns or pretty much anything, and then you can use Canva AI at scale to generate multiple assets and just get it done instantly. And there's also data analysis features and things like that. But I just thought that looked quite promising, especially for social media agency owners. So if you do that, have a look at these Canva features. Now let's move on to the next story. Okay, next up, we have some major updates to AI video generators, namely, the one where most people agree right now is probably the best one. Google's VO2 is, for a short amount of time, freely accessible for Google's AI studio. That's their developer interface. And with new releases, they put their products in there for a short amount of time for developers to try out. And as of the week that I'm recording This video in, VO2 is available in there. So you can just go ahead and generate a few videos for yourself if you haven't tried this before. This model is head and shoulders above many of the competitors, as you can see by some of the examples that are on screen right now. But now you could only access it directly through the API where you paid per usage and in their experimental WISC app that we talked about recently. But now you can just try it. So that's kind of nice. And then beyond that, we have cling 2.0, and cling was one of the models that was competing for this top rank with VO2. And let me tell you this 2.0 model is incredible. It's probably the best text to image model that we have right now. Even better than VO2. It's debatable. Both have their strengths and weaknesses, sure, but the point here is AI video has definitely not plateaued and I wanted to show you a few comparisons of this cling 2.0 model and some of the prompts that we usually like to run on top of them for the show to give you a feeling of how this stuff is evolving. Before we do this though, I do want to say it's ridiculously expensive, Whereas right now a five second video costs 100 credits. And here's the pricing for the credits only. So getting one clip that needs a few iterations might just cost you five to ten dollars, which is just crazy for most people. But as you can see from some of the comparisons on screen right now, the quality is incredible. Particularly this little text to video example of a sloth hanging out an inflatable donut really impressed me. The water physics are near perfect here. Even the way his foot is submerged in the water kind of work works. I mean AI video has become indistinguishable in many cases, but with releases like this and VO2 now it's just pushing that high bar further and further. Interesting, interesting. Now let's move on to the next one. So here we have a few announcements and I'm going to bullock these together. I would call these competitors catching up to OpenAI and if using either Anthropics, Claude or Grok then great. There's a bunch of new features that you should know about. Namely I'll start with Anthropics Research feature which is essentially Deep research now integrated into and from topics Claude. Many people love Claude for good reasons. The tone is really unique. The coding abilities are state of the art, although now Gemini 2.5 Pro and GPT 4.1 are competing for that slot. Which one is the best there really? That depends on who you ask and what exactly you're doing. But the point is they added this research feature which has one integration that none of the competitors have as far as I know. And when I say competitors I mean Google's Gemini Advance, ChatGPT and Grok and that's their ability to connect to external documents within the Deep Research. So you could do something like connect it to your Gmail account, Google Calendar, Google Docs, and it will consider those files while conducting its deep research here. As of now it's available in beta for Max Team and enterprise users in select countries. So yeah, this is a Paid feature. And no, the standard plus plan does not have this as of now. And then we have updates from Grok releasing Grok Studio and there's things like Data Analysis as we know it where it can generate documents, code and then visualize them and then something like a canvas where you can collaborate on the content content with the AI here. They're just clearly playing catch up with ChatGPT features that have been here for, well, almost two years in the case of Data Analysis Code Interpreter as it was called back then. And they also shipped the Memories feature similar to the one in ChatGPT, but it doesn't reference your past chats. We talked about that extensively last week. If you haven't heard that discussion, I think it's an important one. Consider checking that segment from last week's video out point here being Grok now has basic memories and adding some of these essential features that I still love using all the time in ChatGPT. Okay, on to the next one, which is updated to Gemini's Deep Research feature. Now it uses Gemini 2.5 Pro under the hood, which has enjoyed massive popularity since its release. Now if I may, I'll give you a quick recap of the history of the Deep Research feature. It first was released within Gemini Studio. They called it Deep Research, but it was very basic. Then OpenAI came along and implemented their O3 model under the hood of their Deep Research product, which blew everybody's mind. I think still today that is the most useful AI thing out there and they just adopted that descriptive name. And now as we just covered everybody's following suit with pretty much everybody having a Deep Research feature now, including Anthropic, Kroc and even more specialized players like Perplexity. So what happened actually last week I'm featuring it just now is that Google updated the model under the hood of their Deep Research with their best model, Gemini 2.5 Pro. And I was really excited to see this because I love me a good Deep Research and I was hoping that this could be some competition for OpenAI. But long story short, I genuinely do think that OpenAI is still better. I ran a few test prompts here and just consistently I found the OpenAI results still be better. We'll put up our results here on screen right now from our test. Basically the gist of it is that OpenAI reports are more pointed towards what you exactly prompt. So one might say that the prompt adherence is better or in more human terms it just listens to you and understands what you actually mean. Gets you exactly those results. Now, don't get me wrong, still a great product. Just at this point in time. OpenAI's version is still superior. Oh, one more note. The deep research results, they're more structured like a research paper. If that's what you need, then I would definitely recommend Gemini's implementation of deep research. And if you use Gemini, the advanced perfectly fine. I'm just saying, if you start nitpicking, OpenAI's results just more relevant. And I find some of the insights from those reports to be truly novel, whereas I don't have that experience with pretty much all the competing products. All right. So that's really everything I have for this week. Next week, I'll be back in my studio. If you enjoyed this, make sure to subscribe. I do the show every single Friday. My name is Igor, and I will see you very soon.
