Gemini 3 Pro builds screenshot-to-code app in two prompts, fixes bugs
When I dropped a screenshot of a UI mockup into Gemini 3 Pro, the model actually spit out a working React app after just two prompts. First I sent the image, then I asked for the component tree. In those two steps the system generated a full codebase, compiled it, and even pointed out a few hidden bugs that I would have missed.
The output wasn’t a half-baked prototype - the interface looked finished and the app ran smoothly, without the typical glitches you see in auto-generated code. It feels like the tool is moving past a simple demo; it keeps the context between prompts, flags obscure problems, and hands you a usable UI. I’m still not sure how it will handle more complex designs, but the result is promising enough to try.
The Screenshot-to-Code app is live, so you can give it a spin and see what happens.
Gemini 3 Pro proves that AI tools handle production-level complexity. It maintained context, fixed obscure bugs, and delivered a polished UI. You can try the Screenshot-to-Code app here: https://ai.studio/apps/drive/1PfOYRLP-QAAepG128DvJIt18Vofbbrx2 I successfully built a React application using Gemini 3 Pro in two prompts.
The AI agent handled the architecture, styling, and debugging. This project demonstrates the efficiency of multimodal AI in real-world workflows. Tools like this screenshot-to-code app are just the beginning.
The barrier to entry for software development is lowering. Vibe coding allows anyone with a clear idea to build software, while AI models like Gemini 3 Pro provide the technical expertise on demand.
Two prompts. That’s all Gemini 3 Pro needed to spin up a screenshot-to-code agent. I fed it a UI mockup and watched the model crank out a full React project, complete with a responsive layout and working components.
The write-up says the system kept context throughout, patched some odd bugs, and delivered a polished interface you can try at the link they shared. It looks like the model can tackle production-level complexity, at least in this narrow demo. What I didn’t see was any discussion of how the agent copes with vague designs or rare interaction patterns, so its broader reliability is still unclear.
The author noted a speed boost compared with hand-coding static designs, but there’s no hard benchmark to back that up. If similar outcomes appear across different codebases, developers might get a handy shortcut; still, we need to see whether the bug-fixing and code quality stay consistent. Bottom line: Gemini 3 Pro turned a screenshot into a functional React app, but more testing will show if the approach scales beyond this single example.
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Common Questions Answered
How many prompts did Gemini 3 Pro require to generate a complete React app from a screenshot?
Gemini 3 Pro completed the entire screenshot‑to‑code workflow in just two prompts. The first prompt supplied the UI mockup image, and the second asked the model to emit the component tree, resulting in a full, compiled React project.
What types of tasks did Gemini 3 Pro handle during the screenshot‑to‑code experiment?
The model managed architecture design, styling, and debugging within the two‑prompt interaction. It not only generated the component hierarchy but also identified and patched obscure bugs, delivering a polished, responsive UI.
Why is the two‑prompt workflow considered significant for developers using code generators?
Most code generators struggle with multi‑step tasks and lose context, requiring many back‑and‑forth exchanges. Gemini 3 Pro’s ability to maintain context and produce production‑level code in only two prompts demonstrates a major efficiency gain for developers.
Did Gemini 3 Pro encounter any bugs while building the React application, and how were they resolved?
Yes, the model surfaced a handful of hidden bugs that are typical in real‑world projects. It automatically fixed these obscure issues during the generation process, ensuring the final app compiled and ran without manual intervention.