In this episode, I tests Polymet AI, an AI product designer tool, by creating a YouTube analytics prediction SaaS concept inspired by a viral tweet about predicting tweet performance. I compares Polymet AI with V0, noting that while Polymet required multiple prompts and offered less feedback during the design process, both tools ultimately produced usable designs. I concludes that different AI tools have different strengths, and using multiple tools in combination might yield the best results.
Timestamps:
00:00 – Intro
02:20 – First Impressions of Polymet
03:44 – Startup Idea: Predicting YouTube Engagement
04:50 – Initial Design Prompt
09:59 – Polymet’s First Design Output: TubePredict
10:51 – UI and Functionality Issues and Debugging
15:55 – Polymet’s Second Design Output
17:31 – Debugging pt 2
20:35 – Prompting v0
22:18 – v0’s Design Output
23:28 – Polymet’s Third Design Output
24:37 – Comparing v0 and Polymer and Final Thoughts on Design Outputs
27:23 – Conclusion and Recommendations for AI Design Tools
Key Points
• I tests Polymet AI a new AI product designer tool that claims to help non-designers create production-ready designs
• I compares Polymet AI with v0, a similar tool he’s used before, by designing a YouTube analytics prediction tool
• Both tools produced functional designs, but with different user experiences and output quality
1) First impressions of Polymet AI:
• Clean interface similar to ChatGPT
• Includes voice input (huge plus!)
• Image upload capability for reference designs
• Credit-based system (250 free credits to start)
• 50 credits per page generation
2) The design process with Polymet was… interesting.
PROS:
• Named the product "TubePredict" automatically
• Created decent landing page copy
• Saved version history
CONS:
• Slow generation (2+ minutes)
• No progress indicator (frustrating!)
• Initial designs missed the mark completely
3) After 3 attempts with increasingly specific prompts, Polymet finally delivered:
• Clean, modern interface
• Detailed A/B testing dashboard
• Statistical confidence indicators
• AI suggestions for optimization
But the communication was ONE-WAY. No feedback loop!
4) Meanwhile, v0 showed its strengths:
• Real-time reasoning as it designed
• Conversational approach ("I’ll create a SaaS that…")
• Faster FEELING process (transparency helps!)
• Ability to ask clarifying questions
The difference in experience was NIGHT and DAY.
5) The final designs were surprisingly similar in quality!
Polymet’s strengths:
• More detailed product features
• Hover states built in
• Actual code generation
v0’s advantages:
• Slightly more polished visually
• More "glassy" as requested
• Better feedback loop
6) MAJOR INSIGHT: The future isn’t about finding ONE perfect AI design tool.
It’s about using MULTIPLE tools strategically:
• Generate initial concepts in one
• Refine in another
• Mix and match their strengths
Just like we do with ChatGPT, Claude, Gemini, etc.
7) The REAL lesson? Knowing design terminology matters!
When I specifically requested "glassmorphism" instead of just saying "glassy," both tools performed MUCH better.
The more precise your design vocabulary, the better your AI design results.
8) Would I recommend these tools? YES – but with expectations in check.
Two years ago this would have been MIND-BLOWING.
Today, our standards are higher.
But for quick prototyping or inspiration, both tools deliver value in different ways.
Notable Quotes:
"Two years ago, if I would have seen this, I would have been like, ‘Oh my God, everything has changed.’ And now our bar for all these AI products is so high that the output needs to be incredible for you to really use it in your workflow."
"How do you get the most out of them is just you use all of them, you realize what’s best for each individual product… There’s these nuances that make these products better."
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