Key Takeaways:
💡 Canva Code significantly outperformed Firebase Studio and DeepSite in real-world testing, producing functional code on the first try in 4 out of 5 test cases while requiring only 5.5 hours to build a working prototype.
💡 Firebase Studio failed to deliver despite its promising Firebase integration—generating non-functional code on all first attempts, suffering from poor file management, and producing outdated UI designs that required 14+ hours of troubleshooting.
💡 DeepSite showed understanding of application architecture but was hampered by a hostile user interface, lack of iteration capabilities, and virtually non-existent debugging tools—resulting in 17 hours of work for a partially functioning product.
💡 Most AI coding tools fail because they prioritize impressive demos over real-world usability, lack proper debugging capabilities, and are built by AI researchers rather than developers who understand practical software development workflows.
Most comparisons of AI coding tools are complete bs. They’re written by people who’ve barely used the products, or worse, by content marketers who’ve never shipped real software in their lives.
I don’t have time for that nonsense. I’ve built and sold companies, and I know what actually matters when you’re trying to ship products quickly. So I tested Firebase Studio, DeepSite, and Canva Code by building actual products with real requirements and hard deadlines.
The results were shocking—and not in a good way for most of these tools. Here’s the unfiltered truth about which AI coding platform is worth your time in 2025 and which ones are just burning venture capital while wasting yours.
The Testing Method That Actually Matters
I didn’t just play around with these tools for a few hours and call it a day. I built the exact same product on all three platforms: a simple workout planner.
I tracked:
- Time to get from first prompt to working code
- Number of attempts before getting something that actually functions
- How many bugs I had to fix manually
- Whether I wanted to throw my laptop out the window during the process
Let’s break down what I found.
Firebase Studio: Huge expectations + disappointed
I attempted 4 different ideas and they all failed
When Firebase Studio launched, I was genuinely excited. Firebase has been core infrastructure for multiple products I’ve built. The idea of generating code with direct Firebase integration seemed like it could actually save serious development time.
Boy, was I wrong.
What Doesn’t Completely Suck
The concept is good: Integrating Firebase’s authentication, Firestore, and hosting with AI code generation is genuinely smart.
It’s free: Unlike most tools that charge you before you realize they’re garbage, at least Google lets you discover Firebase Studio’s limitations without charging you first.
Decent code structure: On the rare occasions when the code actually works, it’s reasonably well-structured.
Where It All Falls Apart
Nothing works on the first try. Nothing. I tried five different approaches to build my test app. Not a single one produced functional code on the first generation. Not one.
Here’s a real example: The authentication code it generated referenced variables that didn’t exist elsewhere in the codebase. It’s like if I told you to open a door using a key I never gave you. The code looked plausible until you actually tried to run it—then it spectacularly failed.
File management is a nightmare. Try reorganizing your components or moving code between files, and you’ll quickly contemplate a career change. The file browser lags constantly, search functionality barely works, and finding specific code becomes a digital version of Where’s Waldo.
The UI it generates looks like it’s from 2010. Despite Google supposedly caring about design, the interfaces Firebase Studio generates look like they were built by a backend engineer who thinks CSS is a type of cancer.
After 14+ hours of fighting with it, I did eventually get a working app. But I could have built it from scratch faster.
DeepSite: Wasted Potential
DeepSite launched with serious hype. Created by one of the founders of Hugging Face, it promised to revolutionize web development. Instead, it revolutionized my understanding of frustration.
The Few Things It Gets Right
It understands application architecture: DeepSite actually seems to grasp how different components of an application should connect.
The code is relatively complete: Unlike some tools that generate skeleton code with comments like “implement this part yourself,” DeepSite at least attempts to generate everything.
The code performance isn’t terrible: When it works (a big when), the code is reasonably efficient.
The Catastrophic Failures
The interface is actively hostile to users. The non-adjustable input box forces you to either write tiny prompts or play a scrolling game that would make arcade machines proud. It’s like they never actually watched someone use their product.
You get exactly one shot to get it right. There’s effectively no iteration capability. If the generated code doesn’t work, good luck figuring out why. The lack of chat history means you’re starting from scratch each time.
Debugging is like trying to fix a car engine while blindfolded. When something breaks—and something always breaks—DeepSite gives you virtually no information to help fix it. I spent three hours tracking down why data wasn’t saving to the database only to discover the code was trying to access a collection that hadn’t been initialized. A proper error message would have solved this in minutes.
After 17 hours, I had something partially working, but with so many lingering issues that I wouldn’t even consider showing it to a client.
Canva Code: The Unexpected Winner by a Mile
I was extremely skeptical about Canva Code. Canva is a design tool company. What the hell do they know about generating functional code?
Apparently, a lot more than Google or the Hugging Face founder.
What Canva Code Gets Shockingly Right
It actually works on the first try. This shouldn’t be revolutionary, but it is. In 4 out of 5 test cases, Canva Code generated applications that functioned correctly on the first attempt. Not perfectly, but functionally. That’s borderline miraculous in this space.
The UI doesn’t look like garbage. Leveraging their design expertise, Canva Code generates interfaces that actually look like they were made in the current decade. The layouts are sensible, the color schemes don’t cause eye strain, and components have proper spacing and alignment.
You can actually find and edit your files. The file management system is responsive, intuitive, and doesn’t lag when you have more than 10 files. This basic functionality somehow eludes the engineering prowess of Google.
Adding features doesn’t require sacrificing a goat. The code is modular and well-documented, making it straightforward to extend. I could actually build on what was generated rather than fighting with it.
When things break, you know why. Canva Code provides detailed error messages, a console log that actually shows useful information, and debugging tools that help you identify issues quickly.
The Few Limitations
Backend options are more limited. You’ll need to do more configuration for complex backend requirements compared to what Firebase Studio theoretically offers (if it worked).
Performance optimization is sometimes needed. The generated code handles test data well but needs some tweaking for production-scale data loads.
Initial learning curve is steeper. The first couple of hours with Canva Code can feel overwhelming due to the number of options available.
But these drawbacks are trivial compared to the fundamental failures of the other platforms. With Canva Code, I built a working prototype in 5.5 hours that would have taken me at least 15-20 hours with traditional coding methods.
Why Most AI Coding Tools Are Not Worth Your Time
After extensive testing, I’ve identified why most AI coding tools fail:
- They prioritize demos over debugging. They’re built to impress in controlled demos but fall apart in real-world use because they lack proper debugging tools.
- They’re built by AI researchers, not developers. The people building these tools don’t understand the actual workflow of software development.
- They assume code generation is the hard part. In reality, debugging and extending generated code is where most of the work happens.
- They sacrifice usability for AI capabilities. The most advanced AI is worthless if you can’t effectively use the tool it’s embedded in.
Who Should Use Each Platform
Firebase Studio: People who enjoy pain and have no deadlines.
DeepSite: Currently, nobody. Maybe check back in a year.
Canva Code: Anyone who needs to build a small functional web applications quickly
The Bottom Line
The AI coding tool space is filled with overhyped products that fall apart as soon as you try to build anything real. They’re selling the dream of effortless code generation while ignoring the realities of software development.
Canva Code is the exception. It’s not perfect—no AI coding tool is—but it’s the only one that delivered consistently functional results in a reasonable timeframe. The fact that it comes from a design company rather than a specialized AI or development company is both ironic and telling.
What makes Canva Code effective isn’t having the most advanced AI—it’s building a tool that actually works in the real world, with real projects and real constraints. It’s understanding that generating code is only useful if that code functions and can be effectively modified.
If you’re evaluating AI coding tools in 2025, ignore the hype and marketing buzzwords. Ask one simple question: Will this tool help me ship working software faster? By that measure, Canva Code is the clear winner, while Firebase Studio and DeepSite are still trying to figure out how to make their demos work outside of perfectly controlled environments.
Don’t waste your time or money on tools that can’t deliver. In this space, there’s Canva Code, and then there’s everything else.
