I Built the SAME App in Claude Code vs Google Antigravity vs Base44

Ajouté : 2026-05-13

[00:00:00]Have you ever had a brilliant app idea, but then you thought that you needed years of coding experience to make it all happen? Now, I just proved that completely wrong by building the exact same app three times using three different AI tools. Claude Code, Google Anti-gravity, and B 44 without writing a single line of code myself. And most people think that you do need to be a tech genius or hire expensive developers to create an app. But nope, here's what the tech industry does not want you to know. AI tools now exist that can build real working apps for complete beginners in just hours, not months. So, in this video, I'm going to show to you and reveal which AI tool actually delivered a functioning app that real people wanted to keep using. I'm going to show you the shocking differences between what each tool produced, the one that completely failed, and the winner that had people asking where they could download it, too. Because, by the way, I am not a programmer. I'm just someone who wanted to test if these AI promises are actually real. And we're going to see real user reactions and discover which tool breaks under pressure. and I'm going to show you exactly which one you should use to turn your app idea into reality. So stick around until the end to see which coding tool takes the crown. And here's the exclusive part because I've created a free course showing you how to build apps and websites and even SAS products with the winning tool here completely without any code. This course normally costs $299 to join, but for the people watching this video, thank you very much. It is completely free. And this isn't just theory. You're going to learn to create real profitable applications using AI.

[00:01:45]And you can only access this course by watching until the very end. So, please don't skip ahead. Go ahead and check out that link in the description down below.

[00:01:52]Just can't wait. Okay. So, before we start comparing anything, we do need a system here that actually reflects how these platforms are used in real scenarios. So, for this test, we're putting three AI builders headto head to head. base 44 cloud code and anti-gravity to see how well they can build the same application under the very same conditions. And the app we're building here is a calorie tracker. And instead of doing it all at once, we're breaking the process down into a sort of sequence of prompts. And each prompt represents a specific stage of development from building the initial dashboard all the way to AI features, authentication, and payments. This allows us to see not just the final result, but also how each platform performs at every step as the app becomes more complex. Now, to keep things fair, every platform receives the exact same prompts. We're also not focusing on design preference here.

[00:02:46]Whatever the platform generates in terms of layout or styling, well, we leave it as it is. The goal here isn't to judge creativity. It's to evaluate how well each tool actually builds and then delivers the required features. Now, unlike a oneshot test, this process reflects a more realistic workflow here.

[00:03:05]If something breaks or doesn't work properly, we reprompt the platform to fix it. And that's where efficiency does come into play. Since some tools might get things right away immediately, while others will require multiple attempts to reach a working result and instead of ignoring those issues, we do track them as part of the evaluation. Each step is graded using four criteria. The first is speed, which measures how long it takes for the platform to generate each feature. Faster results, of course, means quicker iteration and just less waiting during development. Next is efficiency, which looks at how many attempts or reprompts were needed to get the feature working. A platform that gets it right on the first try scores a lot higher than one that requires constant back and forth and back and forth. And after that, we evaluate implementation. And this focuses on how well the platform actually follows the prompt and then delivers the feature as intended. So it's not just about whether something exists, but more like whether it works properly and aligns with the requirements. And finally, there's the verdict here, a summary of how the platform performed for that specific prompt based on everything that we've seen. So once all of those prompts are completed, we will zoom outward and evaluate each platform as a whole. And this includes the overall app building experience, how easy it is to publish the app, how the pricing works, and ultimately which platform delivers the most complete and practical experience.

[00:04:38]Okay, so now that the system is clear, let's go ahead and start building. So let's start with Google Anti-gravity and go through the app the same way it was actually built, one prompt at a time.

[00:04:47]Starting with the first prompt, this is where we build the foundation of the application. And the goal here is to create the dashboard layout to set up the main sections like food tracking, stats, and settings and include all the required widgets as placeholders. Now, in terms of speed, anti-gravity completed this initial build in around 7 minutes. It's not particularly slow, but compared to what we'll see later on with other platforms, it does feel a bit on the longer side for a foundation level prompt. So looking at efficiency, this wasn't a clean one attempt process either on the first run. Anti-gravity defaulted to a basic HTML and CSS setup, which doesn't really align with a modern web approach. So because of that, the process had to be interrupted and then redirected to use a more suitable stack like React with Fight. Now once that adjustment was made, it was able to continue and complete the build without further issues. But again, that initial correction still counts as an extra step. For implementation, anti-gravity does manage to cover most of what was asked. The dashboard includes the correct sections and widgets, and everything is structured in a way that matches the prompt as well. It also applies a consistent dark blue theme across the app, which gives it a nice cohesive look. And that said, there are a few noticeable issues. There's a lot of redundant log food button that overlaps with the existing quick action widget and it also introduces additional actions like log water and workout which were part of the original request. So these aren't major problems but they do show that the platform does not always stick strictly to the prompt. Overall the result is solid but not perfect.

[00:06:38]Anti-gravity successfully builds the foundation of the app with the correct structure in place, but it takes a bit longer than expected and requires an early adjustment to get on the right track. The extra features it adds also slightly drift away from the original instructions, which affects how clean the output feels. Now, moving on to the second prompt, this is where we start building the actual core functionality of the app. specifically the food logging system and this includes removing placeholder data and allowing users to log food with details like calories and macros and images through a modal. In terms of speed, anti-gravity handled this pretty quickly. The entire feature was built in just around 2 minutes, which is a noticeable improvement, especially when compared to the first prop we had earlier. And when it comes to efficiency, this was a clean oneprompt result. There were no reprompts needed and the feature worked immediately after generation, which is definitely a good sign at this stage.

[00:07:43]For implementation, the feature does technically work. From the footage here, you can see that the food logging functions as expected and users are able to input data without major issues.

[00:07:56]However though, this is where some weaknesses start to show up. The model itself feels very basic with little attention to detail. There are also UI issues, particularly with the drop- down options for selecting meal types, which don't feel polished. More importantly, the image handling isn't implemented properly. Rather than allowing direct image uploads, anti-gravity relies on image URLs, which isn't ideal for a real user experience. But overall, while the feature was delivered quickly enough and worked on the first attempt, the lack of polish is kind of hard to ignore here.

[00:08:31]The UI issues in the absence of a proper image upload system just make the implementation feel incomplete, even though the core functionality is technically there. For this third prop, the goal is to replace the remaining placeholder data and fully build out the stats and insights section, including functional widgets and the ability for users to modify their targets through a modal. So, here the app shifts from basic functionality into, well, something that actually helps users track progress over time. Speed-wise, anti-gravity stays consistent and finishes the entire feature in about 2 minutes. It isn't slow down even as the app begins to require more functional depth. Now for efficiency, this is another clean run here. There are no reprompts needed and everything is generated in a single attempt without any kind of interruption. Now looking at implementation, anti-gravity handles most of the important parts pretty well.

[00:09:30]The stats section is functional and users can view and edit their calorie and macro targets directly, which adds meaningful interactivity to the app.

[00:09:40]It's clear then that the core idea of the feature is there and working, but however, not everything is fully completed. One of the key widgets, the 7-in day caloric intake, still uses placeholder data instead of reflecting real user inputs. So, while the section does look complete at a glance, it doesn't fully deliver on all of the requirements. Overall, this is a good result with a noticeable limitation. An anti-gravity keeps things fast and efficient, but the incomplete widget prevents the feature from being fully finished. For the fourth prompt here, the focus is on adding an AI chatbot that can assist users with fitness related questions and even log food directly through, of course, conversation. And this is one of the more advanced features in the app since it does require both interaction and integration with the existing system.

[00:10:33]Now, anti-gravity doesn't get it right immediately. It takes a couple of tries before the chatbot reaches a usable state. So, there's already a bit of a back and forth compared to the earlier prompts. It's not excessive, but it is noticeable. And once it does work though, the result is surprisingly solid. The chatbot responds properly to general fitness related questions. And more importantly here, it can log food directly from the conversation. So that part is important because it shows the feature isn't just isolated. It's actually connecting with the rest of the app. And in terms of speed, then the whole process lands at around say 4 minutes. Slightly longer than the previous prompts, but that is expected given the added complexity of it all. So overall, this is one of those cases where the final result is just good, but the process to get there isn't completely smooth. The feature works the way it should, but it does take a bit of extra effort before it gets there. So now we're pushing the AI side even further, and this prompt is supposed to expand the app's capabilities by allowing users to scan food images and instantly get results and then add that directly into their food log. And just like the chatbot, this isn't something NTG gravity gets perfectly on the first try. Takes a couple of reprompts before the feature actually works. So there is again some back and forth involved before reaching a more usable result.

[00:12:03]Now time-wise, it lands at around 4 minutes, which is consistent with the previous AI related prompt. So speed isn't really the issue here. It's more about how cleanly the feature comes together. And this is where things start to fall short. Yes, the scanning technically works. It's able to recognize something like say a chicken breast and then return results, but the experience is incomplete. There's no image preview and more importantly, it still relies on image URLs instead of allowing proper uploads. And that same limitation from earlier carries over which makes the feature feel less practical. So, while the feature does function, it doesn't feel polished or fully thought through. It requires extra prompts just to get it working and even then the implementation does not really improve the user experience in a more meaningful way. So now we're getting into one of the more critical parts of any real app handling users. And this prompt focuses on setting up login and signup authentication and connecting everything to a database which is essential if the app is meant to go beyond just a local demo. So this is where the process starts to slow down because the full implementation takes around say 9 minutes making it one of the longer prompts so far. And it's not just because of complexity. The back and forth plays a role here too. And unlike earlier prompts, this one isn't smooth.

[00:13:28]The login system requires multiple revisions before it works properly, which adds extra time and effort to the build. And it's clear here that anti-gravity struggles a bit more when it comes to handling authentication cleanly. And in the end, the feature does indeed work, but with some important limitation. Now, authentication is set up locally, so users can log in and use the app, but there's no real native database integration behind it. And to make this fully usable in a real scenario, you'd need to connect it to something like Google Cloud or another external database. So, that adds an extra layer of setup that wasn't really part of the original prompt. So, while anti-gravity technically delivers the feature, yes, the reliance on external services does make the process more complicated than it should be, especially for beginners who are expecting something more integrated. And for the final prompt here, everything comes together with payments and feature gating. And the goal here is to integrate Stripe, create a subscription flow, and lock certain features like AI scanning and chatbot logging behind a premium plan.

[00:14:37]Anti-gravity finishes the build and say around 5 minutes here, which is fairly reasonable given the complexity of setting up payments and of course more subscriptions. But similar to some of the earlier advanced features, this isn't a smooth one pass result. The Stripe integration takes multiple reprompts before it starts working properly. There's a noticeable amount of back and forth just to get the payment flow and feature locking aligned with what was expected. And then once everything is sorted out, the implementation does the work. The checkout flow is functional, subscriptions go through, and the feature locks behave correctly. Now the app is able to restrict access and unlock features after subscribing which is exactly what the prompt requires. So overall the end result is there but the process to get there is a bit rough. It takes several attempts before everything works as intended which brings down the efficiency even though the final outcome is indeed functional. So after going through all seven prompts, this gives us a clearer picture of what it's actually like to build a full application using Google Anti-gravity, not just individual features in isolation. So looking at the overall app building experience here, anti-gravity is capable, but it's not always consistent. Most of the core features do end up working, which is a good sign. But getting there isn't always so smooth. Several parts of the process require reprompts and adjustments, especially as the app becomes more complex. There's also a noticeable gap in usability here. This isn't a platform that you can just kind of jump into without some kind of background knowledge. So, having a basic understanding of development concepts does make a big difference here, which also can be a barrier, especially for beginners. When it comes to publishing, things become even more involved.

[00:16:32]Anti-gravity doesn't offer a straightforward built-in way to deploy your app. And instead, the recommended approach here is through a Google Cloud project, which does introduce additional setup and configuration. And on top of that, since the project runs locally, you'll still need to rely on external hosting platforms to actually make the app accessible. Compared to platforms with direct publishing, this one adds just more steps and complexity to the whole process. Pricing is also less straightforward. At first glance, anti-gravity can feel like a pretty good option with free, but in reality, it's tied to Google's ecosystem. Costs depend on how much you use services like Google Cloud or Firebase, which then means expenses can vary and they aren't always predictable. For someone new, this can be confusing to manage, especially when compared to platforms that offer clear all-in-one pricing. Overall, anti-gravity is indeed capable of building a working application, but then the experience comes with a few trade-offs. It often requires multiple attempts to get features right. Some implementations also lack polish and the reliance on external services, especially for database and hosting.

[00:17:48]Those add extra layers of complexity. I guess a job done, yes, but it's not the most straightforward or beginnerfriendly option among the platforms tested. Let's move on to Claude Code and go through the same stepby-step build process. And just like before, each prop adds a new layer to the app. So, it is easier to see where things go right and where the platform starts to just kind of drift from the original instructions. So for our first prompt with Claw, the goal is to build the initial dashboard structure with placeholders including the main sections and widgets, of course, without adding real functionality. Not yet. And Claude Code completes this in around 5 minutes or so, which is a pretty solid start and slightly faster than what we saw earlier. Now, at first glance here, as you can see, it seems efficient, but the process isn't entirely clean cuz right from the start, Claw defaults to basic HTML and CSS setup. And while it does technically build the layout, it doesn't match the expectation of using a modern stack. And because of that, an additional prompt is needed to switch it over before continuing. And once that adjustment is finally made, it does proceed without further issues. After the fix, the dashboard is generated. But this is where things start to feel a bit off because Cloud Code doesn't strictly follow the structure from my prompt.

[00:19:07]Instead, it kind of expands the app by splitting the main sections into multiple subsections. And while this might seem like an improvement in some cases, it does go beyond what was originally asked for, and it also changes the intended layout. There are also some noticeable design issues here.

[00:19:25]The layout lacks polish, and there's a large amount of empty space here on the right side, as you can see, which makes the entire dashboard feel to me unbalanced and slightly pushed to the left. So, while Claude Co does manage to build the foundation, it requires an a little bit of early adjustment and doesn't fully stick to the prompt. The added structure and layout issues do affect the overall results, making it feel less aligned with the original requirements. Now that the layout is out of the way, we're now going to try to make the app well actually do something specifically. building the core calorie logging feature and removing all of the placeholder content from earlier. Cloud code handles this part much more cleanly this time. The feature is completed in around 4 minutes which is a pretty steady pace and it fits well for this level of functionality. More importantly, there is no friction this time. The entire feature works on the first attempt with no need for additional prompts or fixes. Compared to the first prompt, this is a noticeable improvement in efficiency because looking at the implementation here, everything is indeed set up properly.

[00:20:34]The food logging system works as expected and users can input food data along with calories and macros without any issues. The structure is clean and the feature behaves the way that it is supposed to. Overall, this is a strong result from clot code. It delivers the calorie tracking feature correctly on the first try with no issues during the process. It is a straightforward build and pretty much everything works as intended without any extra effort on our part. Next is the stats and insight section where the app starts turning log data into something more useful, allowing our users to view progress and adjust their targets. Now, Claude Code handles this step very quickly, finishing the entire feature in around just 3 minutes. It's one of the faster builds so far, and the pace doesn't seem to affect the quality either. And just like the previous prompt, there's no need for any reprompts here. Everything is generated in one go, which keeps the process smooth and uninterrupted. What's interesting about the implementation, however, is that the earlier decision to split the app into multiple subsections actually works in its favor this time.

[00:21:48]Because of that structure, Clawed Code is able to organize and deliver functionality across different parts of the stats section a lot more effectively. The features aren't just present, they're actually usable and properly connected. Again, this is another strong result. The stats and the insight section works as expected right from the first output and the feature is delivered cleanly without any extra steps. So this is where the build starts to get more demanding with the addition of an AI chatbot that can answer questions and directly through chat. Now compared to earlier prompts this one takes a little bit noticeably longer.

[00:22:27]Cloud code completes it in say around 11 minutes which is a significant jump in time. It also doesn't come together in one go because the chatbot requires several reprompts before it reaches a usable state. So there is a fair amount of back and forth involved during this part of the process. And once everything is sorted out though, the implementation is quite solid. The chatbot responds properly to questions and is able to handle food logging requests as expected. The structure does feel clean and it integrates well with the rest of the app instead of feeling like a separate feature. The final result works well here, but getting there did take a bit more time than expected and effort compared to earlier prompts, too. Next is expanding the AI capabilities by adding imagebased food scanning, where users should be able to upload an image and then log it directly into the app.

[00:23:19]Cloud Code completes this in around 4 minutes, which actually brings the speed back down compared to the chatbot build previously. However, this is not a perfect one pass result either. The feature needs a few adjustments before it starts working properly. So, there's still some minor back and forth involved. And where things become a bit more noticeable is in the implementation. As you can see, while the scanning feature does work, a cloud code places it inside the AI chatbot instead of integrating it into the main logging flow. So, this changes how the feature is accessed and it doesn't match what was originally intended in the prompt. So even though the functionality exists, it's not positioned where it should be. The feature works, yes, but the way that it's being implemented does not fully align with the expected behavior, which does then affect how intuitive it is to use. Now, we're introducing user accounts and persistent data, which is a pretty big step towards making the app usable beyond just a local session. This includes login and sign up authentication and connecting everything to a database. Cloud Code accomplishes this feature in around just 5 minutes, which is still relatively quick considering the scope of what's being added here. However, again, it's not entirely smooth. The login and authentication system does require a few adjustments before it finally works properly. So, there is still some back and forth as well involved during this part of the process. In terms of implementation, the feature does work but with some limitations.

[00:24:55]Authentication is handled locally and there's no built-in database support to make the app function properly. An external service like Superbase would normally be needed and in this case we had to guide cloud code to use a local database setup instead. So, while the login system is functional, the lack of native database integration adds extra steps and really just kind of makes the process a lot less straightforward than it should be. So, to wrap everything up now, this prompt introduces Stripe integration and feature gating where again certain parts of the app like say the AI scanning and the chatbot logging are locked behind a subscription. Claude Code did this in around 5 minutes, which is again relatively fast considering it involves both payments and access control. However, similar to some of the earlier advanced prompts, again, this is not a one-pass result. The Stripe integration, for example, requires a few adjustments before everything works properly. There's some back and forth involved to get the checkout flow and feature locking aligned with the intended behavior. But once everything is in place, the implementation does work as expected. The payment flow goes through successfully. Subscriptions can be completed and the locked features do behave correctly as well. After subscribing, then access is properly unlocked, which shows that the integration is functioning end to end.

[00:26:28]And while the final outcome is solid, the process itself does take some extra effort and the need for multiple prompts to stabilize the feature just brings down efficiency even though the end result still delivers what was required.

[00:26:42]So looking at cloud code as a whole, the experience is generally solid, but not without a few trade-offs once everything is put together. Starting with the app building experience, Claude Code performs well overall and most features are built cleanly and once they are working well then they tend to be stable. Compared to the other platforms, there are fewer outright errors which helps keep things predictable here.

[00:27:08]However, it's not always consistent when it comes to following instructions.

[00:27:12]There are moments where it seems like Claude changes structure or placement, like splitting sections differently or placing features in unexpected areas.

[00:27:22]These aren't always bad decisions, per se, but they don't fully match my prompts, which then means extra adjustments are sometimes needed. And when it comes to publishing, this is also where things do become less straightforward because there is no built-in deployment system. So everything depends on external hosting platforms. And that means for us is setting up your own environment and choosing where to deploy and handling configurations all by yourself. So for someone with experience, then maybe this is manageable. But for us beginners, it might just add an extra layer of complexity that is not immediately obvious. Pricing also follows a similar pattern. Cloud Code offers multiple tiers, starting with a free plan, then a pro plan at around $20 per month, and scaling up to higher tiers that can reach $100 to $200 depending on usage.

[00:28:17]But the main thing to consider here is that this does not cover everything.

[00:28:21]Since there is no built-in database or hosting, you're also going to need external services like Superbase and deployment platforms, which overall will add additional costs. And because of that, pricing isn't centralized, then it can become harder to actually track how much you're actually spending. Taking everything into account, clouded code is indeed very capable and reliable when it comes to building features. It produces clean implementations and avoids major issues most of the time. However, the need for external tools combined with occasional inconsistencies and following some prompts makes the overall experience less streamlined. It works well, really well, but it requires more setup and involvement compared to platforms that pretty much offer everything in one place. Now, moving on to base 44. And right away, the approach feels noticeably different. We're still following the same stepby-step process, but this is where we start to see how a more integrated platform handles the exact same set of prompts. For the first prompt, the goal is to set up the entire dashboard structure with placeholders, including all the required sections and widgets without building the actual functionality. Not yet. Now, Base 44 completed this in just around 3 minutes, making it the fastest start so far. And it doesn't feel rushed either. The build is quick. It's still stable throughout.

[00:29:48]And what stands out immediately is the efficiency here. There are no revisions needed at all. The entire foundation is built in a single attempt with no interruptions or adjustments along the way. And looking at the implementation, base 44 follows the prompt exactly. All the required sections are in place. The widgets are correctly positioned. And the layout feels clean and polished right from the start. There aren't any unnecessary additions or missing elements. It just sticks closely to what was added. And the result is straightforward and solid. Base 44 delivers the foundation quickly, correctly, and without any need for extra steps, making this one of the cleanest starts across all of the platforms. After setting up the foundation, we're now going to focus here on building the actual calorie tracking system, which means removing all placeholder data and allowing users to log food through a modal with details like calories, macros, and other information. And Base 44 completes this in around, say, 4 minutes, maintaining a fast pace even as the app transitions into real functionality. And just like the first prompt, there are no revisions needed here either. The entire feature works immediately on the first attempt with no need to go back and fix or adjust anything. The process stays smooth from start to finish. And in terms of implementation, everything is handled properly. The food logging system works as expected, including the modal for input, the handling of calorie and micro data, and the way the information is reflected in the app. And from the footage here, we can see that once food is logged, the data updates instantly, which shows that the feature is fully connected and functional. So overall, this is another strong result.

[00:31:42]Base 44 builds the calorie tracking feature correctly on its first try, and the entire process just feels seamless without any issues or extra steps required. And now that the food logging is already working, this step is about making that data useful by building the stats and insights section where users can actually track progress and adjust their targets. And Base 44 completes this in just again around 3 minutes, making it one of the fastest implementations so far without sacrificing quality. And efficiency remains consistent here. There are no follow-up prompts needed at all. The entire feature is built in a single attempt continuing the same smooth pattern from the earlier prompts. Now in terms of implementation, everything is handled properly. All of the required widgets are present and more importantly they're connected to real data. Users can view their stats clearly and make adjustments to things like calorie and macro targets directly within the app.

[00:32:47]So nothing feels missing or left incomplete and the functionality works exactly as intended. So this ends up being another clean result. Base 44 delivers the stats and insights feature fully with no gaps in implementation and no extra steps needed along the way.

[00:33:02]Adding the AI chatbot introduces a more interactive layer to the app where users can ask questions and log food directly through of course conversation. So this step depends heavily on how well everything connects behind the scenes and the build takes around say 5 minutes which is quite reasonable given the added complexity of integrating AI into the workflow. Now what stands out here is how smooth the process is. There are no reprompts needed at all. The chatbot just works right away without any adjustments or fixes. Continuing the same pattern from the earlier prompts.

[00:33:40]Now looking at the implementation, everything is handled cleanly. The chatbot responds properly to user input and more importantly it can log food directly through chat. It doesn't feel like a separate feature at all. It's fully integrated into the app, working alongside the existing tracking system, and the result is straightforward and reliable. The feature works immediately.

[00:34:05]It behaves as expected and doesn't require any extra effort to get it into a usable state. So, building on the existing AI features, now this prop adds imagebased food scanning, allowing our users to upload an image and then automatically log it into the app. So, this comes together in say about 4 minutes. keeping the pace consistent even as more advanced functionality is introduced. There's no friction here at all. The feature works immediately on the first attempt with no need for follow-up prompts or adjustments. The process stays clean from start to finish. And in terms of implementation, everything is handled properly. The app accepts image input, processes it correctly, and then connects the results directly into the food logging system.

[00:34:54]The flow feels quite natural actually.

[00:34:57]Scan, get results, and then log without any extra steps or workarounds. The result is exactly what you'd expect. The feature works right away. It integrates seamlessly into everything and doesn't introduce any other issues along the way. So this is where we are supposed to deal with login, sign up, authentication, and database setup. With B 44 though, there's barely anything to set up. It already comes with a native database and authentication system. So everything is basically ready from the start. There are no external tools, no extra configuration. It just works, which makes this part way easier compared to the other platforms. And so for the final step, we're adding payments and locking features behind a subscription, which usually ends up being one of the more complicated parts.

[00:35:48]So here, it only takes around 5 minutes to get everything set up. There's no back and forth either. The entire Stripe integration and Premiere feature setup just works in one go with no need for extra prompts or fixes. And looking at the implementation here, everything behaves the way that you'd expect. The payment flow works, subscriptions go through, and the feature locks are applied correctly, and once subscribed, access is unlocked without any issues.

[00:36:19]So instead of being a complicated kind of setup, well, this one kind of ends up being very straightforward, too.

[00:36:26]Everything works on the first try, and there's no extra effort needed to get it running. Looking at Base 44 as a whole now, the difference in experience is pretty clear compared to the other platform. From an app building standpoint, this is easily the smoothest out of everything that we've tested.

[00:36:43]Every feature from the basic dashboard all the way to AI, authentication, and payments. It all worked on the first attempt. There's no back and forth.

[00:36:52]There's no fixing broken parts and no need to guide the platform through extra steps. It follows the prompts exactly and it stays consistent throughout the entire build, which honestly makes the whole process feel effortless. And on top of that, since everything is already integrated into from the platform, it removes a lot of the friction that we would normally have to deal with. Now, for beginners especially, this makes a huge difference. Publishing is just as straightforward as well. Instead of setting up external hosting or configuring deployments, you can just publish the app directly using a builtin publish button. And it is instantly available on the web. And beyond that, base 44 also supports native iOS and Android builds, meaning it can wrap the app and then generate proper mobile builds without having to need separate tools. So that's quite something none of the other platforms handled as cleanly as well. Pricing is also much easier to understand. There's a free tier of course that already includes core features like database, authentication, and hosting, which is enough to build and test projects. And paid plans start at around $16 to $20 per month and scale up depending on usage with the $40 builder plan being just more than enough for most real applications. Now, what stands out here is that everything is included. There's no need to pay separately for services like Superbase or Firebase or hosting. And because of that, costs are actually more predictable and easier to manage. So, putting everything together then, Base 44 delivers the most complete experience out of all the platforms tested today.

[00:38:36]It's fast, it's consistent, and it doesn't require extra setup to get working results. And more importantly, it removes a lot of the complexity that comes with building and shipping apps, making it the most beginnerfriendly option in this comparison. So, across all three platforms so far, the gap in experience becomes pretty noticeable once you go through the full build process, and B 44 clearly stands out as the strongest option. It consistently builds features correctly, even on the first attempt, without needing revisions or extra guidance. And more importantly, it already includes essential systems like database, authentication, and publishing, which removes a lot of the usual setup and keeps everything in one place. So, that alone makes the process much faster and a lot easier, too.

[00:39:24]Anti-gravity is capable of producing a working application, but it does come with more friction. Several features require multiple reprompts and there's a heavier setup involved especially with its reliance on Google cloud. So that added complexity can slow things down and then make it less approachable particularly for beginners. Odd code falls somewhere in between. It produces cleaner implementations with fewer errors but it does still depend on external services for things like databases and hosting. And because of that, the workflow just becomes longer and requires more effort outside the platform itself. In the end, Base 44 stands out just because everything is already here. It's all integrated.

[00:40:10]There's less setup, fewer issues, and a much smoother path from idea to a working app, making it the easiest and fastest platform to use overall. So, at this point, it's got to be pretty clear AI app building isn't just hype anymore.

[00:40:24]No, you can go from idea to a fully working app without writing any code, but the tool you choose does make all the difference. Some platforms will get you there, but only after a little bit of trial and error. Others are more reliable, but still require extra setup behind the scenes. Then there are the ones that just work. And if you're serious about turning your app ideas into something real, then you already know which direction to go. And if you want to try it yourself, well, I've linked everything down below. And if you want a step-by-step breakdown of how to actually build and launch your own apps using the best tool that we've tested, then you got to check out the free course in the description down below.

[00:41:05]All right, so that's it for this one.

[00:41:06]Thank you for investing your time with me today and I'll see you at the next video.