no time, jump straight to the conclusion
The world of AI-powered app creation is evolving at lightning speed, and it’s fascinating to watch. This past week, the buzz around Claude’s new AI-powered apps caught my attention, reminding me of a powerful feature Google Gemini has offered for a while now: “Create with Canvas.“:
2025.05.20 – Create with Canvas
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Vibe coding apps in Canvas just got better too! With just a few prompts, you can now build fully functional personalized apps in Canvas that can use Gemini-powered features, save data between sessions, and share data between multiple users. You can even save a shortcut to your apps on your phone home screen for easy access. Lastly if there are errors in the app, Canvas will automatically try to resolve them for you.
As someone keenly interested in “secure & fast vibe coding” – the ability to quickly spin up functional prototypes – I was curious to see how these two leading AI models would compare in a direct test. I put Claude Sonnet 4 and Google Gemini 2.5 Flash through their paces, focusing on their capability to generate a specific type of application: a quiz.
The Initial Challenge: “Create a Weird Animals Quiz”
My first prompt was straightforward: “Create a weird animals quiz.”
Both models quickly spun up functional quizzes. What immediately stood out was Gemini’s speed; it was significantly faster in generating this initial prototype. This early win for Gemini highlighted its potential for accelerating the very first steps of development.
Diving Deeper: The Detailed Quiz App Prompt
To truly test their capabilities, I followed up with a much more elaborate prompt, designed to push the boundaries of what these models could generate for a mobile-friendly, secure quiz app:
Create a mobile-friendly, secure quiz app about weird animals with these specific requirements:
Content & Structure:
- 9 questions total: 3 easy, 3 medium, 3 hard (clearly labeled difficulty)
- Each question includes relevant animal emojis and high-quality descriptions
- Focus on bizarre behaviors, unique adaptations, and shocking animal facts that would fascinate nature documentary fans
- Target curious teenagers (ages 13-17) with engaging, discovery-focused content
Timing & Flow:
- 30-second countdown timer per question (pause timer when showing results)
- After each answer: show correct/incorrect feedback + detailed fun fact
- Include a mandatory “Next Question” button (no auto-advance)
- Allow 15-25 seconds minimum for users to read explanations comfortably
- Progress indicator showing current question and difficulty level
Interactive Features:
- One-time hint system per question (reveals one wrong answer or gives a clue)
- Visual feedback for correct/wrong answers with smooth animations
- Final score breakdown by difficulty level
- Option to retry specific difficulty levels
Design & UX:
- Nature-inspired UI: earthy color palette (forest greens, ocean blues, sunset oranges)
- Organic shapes and flowing transitions between screens
- Mobile-first responsive design optimized for thumb navigation
- Accessibility features: good contrast ratios and readable fonts
- appealing design suitable for curious teenagers who love nature documentaries
Technical Details:
- Smooth animations between question transitions
- Touch-friendly button sizes (minimum 44px)
- Loading states and error handling
- Local storage for progress saving
- The app should include a start screen and a results screen with a “Play Again” option.
Security:
- Avoid slopsquatting – NEVER install packages with typos or similar names to popular packages
- Only use well-established, verified packages from official repositories
- Always verify package names exactly match official documentation
- Check package download counts (>1M weekly downloads preferred)
- Verify package maintainers and GitHub repositories before use
- Implement rate limiting for API calls
- Use parameterized queries for any database operations
- Sanitize column names and data before processing
- Prevent code injection through data manipulation
- Implement proper error handling without exposing internals
- Implement proper CORS policies
- Validate all user inputs on both client and server
- Use HTTPS for all communications
- Store data in memory only (no localStorage/sessionStorage)
The “Do Better” Test
Inspired by Simon Willison’s explorations into AI prompting, I concluded my tests with a simple, yet powerful command: “Do better.”
Interestingly, I found that issuing this prompt did not significantly change or improve the results beyond what was already generated. This suggests that while these models are adept at interpreting detailed instructions, a generic “do better” might not always yield clear, actionable improvements in complex prototype generation.
A Curious Observation: The “Hint” Bug
During one of my multiple iterations (not captured in the accompanying video), I encountered an interesting bug in one of the generated quizzes. The “hint” feature inadvertently gave away the actual answer to the question. This transformed the quiz from a challenge into more of a “show and tell,” highlighting that even with advanced AI, careful human review and testing remain crucial for functional integrity.
Overall Conclusions: Prototyping Powerhouses
Here’s a summary of my head-to-head comparison:
Google Gemini 2.5 Flash (via Canvas)
- Speed: Consistently and significantly faster in generating prototypes. This is a huge advantage for rapid iteration.
- Aesthetics: I personally preferred the visual design and user experience of the prototypes it generated.
- Observation: Encountered a minor bug (hint revealing answer) in one instance, underscoring the need for testing.
Claude Sonnet 4
- Quality: Produced highly comparable and impressive results, on par with Google Gemini in terms of meeting prompt requirements.
- Speed: Generally took longer to generate prototypes.
See the Speed in Action!
To truly appreciate the difference in generation speed, I’ve embedded three versions of the same process below. The first plays at normal speed, allowing you to see the real-time interaction. The second is sped up 4-8 times, offering a quicker overview. Finally, the third video is accelerated by 20 times, vividly illustrating just how quickly these AI models can translate prompts into functional prototypes, particularly highlighting Gemini’s rapid iteration capability.
Final Takeaway
Both Google Gemini’s “Create with Canvas” and Claude Sonnet 4 “AI-powered apps“ demonstrate exceptional prototyping capabilities. They can quickly translate complex, detailed prompts into functional application drafts, making them invaluable tools for developers and innovators looking to rapidly test ideas.
However, Google Gemini’s faster creation time gives it a tangible advantage in the “fast vibe coding” space. A quicker turnaround means a better overall feedback loop, allowing for more iterations and refinements in less time.
Whenever you vibe code, ensure a security-first approach e.g. with the detailed prompt above.
What are your experiences with AI-powered app generation? Have you tried Gemini’s Canvas or Claude’s capabilities for prototyping? Share your thoughts in the comments below!
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