The release of Gemini 3 has sent shockwaves through the tech world. Your YouTube feed is likely flooded with videos proclaiming the death of traditional coding, showing how a single prompt can now build a complete, deployed website.
As a developer, I was excited but skeptical. Could this new AI truly deliver production-ready apps? Instead of just adding to the hype, I decided to put Gemini 3 through a real-world test. I built actual projects to answer one crucial question: Is Gemini 3’s wipe coding truly viable, or is it just a sophisticated prototype?
Spoiler alert: My findings reveal a significant gap between the dazzling demos and practical, deployable applications.
The Allure: First Look at the AI Studio App Gallery
Google’s AI Studio (ai.studio.google.com/apps) features a gallery of example apps that are genuinely impressive. From visually stunning festival websites like “Lumina Festival” to image generation tools, the potential is undeniable. The animations are smooth, the layouts are modern, and they look professional at first glance.
This is the “hype” phase—and it’s convincing. You immediately think, “If it can build this, it can build anything I need.”
The Cracks Begin to Show: The Simple Logo Change Nightmare
To test its practicality, I decided to build a landing page for my Shopify partner agency. The initial result was fantastic—an awesome-looking page with a color scheme matching Shopify’s branding, a portfolio section, and full mobile responsiveness. Deploying it was a one-click breeze.
Then came the simplest real-world task: changing the logo.
In any business, rebranding is fundamental. I needed to replace the placeholder “Scaleflow” logo with my agency’s “WebSense Pro” logo. Using the “Annotate and Edit” feature, I uploaded my logo and gave the prompt: “Use logo attached.”
What happened next was a preview of the developer’s nightmare:
- It took nearly a minute to process.
- It generated a long list of code changes, updating text and components.
- But my logo never appeared.
The AI successfully changed the company name in the text but failed at the core task of swapping an image. For a non-developer, this is a dead end. The generated code is in TypeScript and React, and manually uploading an image asset and importing it is impossible without coding knowledge.
This single test exposes a critical flaw: Making iterative changes is where these tools fall apart.
Ignored Prompts and Hallucinated Features: The Pacman Game Test
I pushed further, testing if Gemini 3 could follow specific technical instructions. I used a prompt generated by AI Studio itself: “Act as a prompt engineer… build a Pacman game using HTML, CSS, and vanilla JavaScript with no external libraries.”
The result was baffling:
- Ignored Prompt: Instead of vanilla JavaScript, it built the entire game using React and TypeScript.
- Broken Logic: The game itself was flawed. The “Game Over” mechanic didn’t work, meaning the player could never lose.
While the AI provided a great starting boilerplate, it failed to adhere to the core requirements and delivered a broken product. This “hallucination” of features and ignorance of clear instructions makes it unreliable for precise project needs.
Why Gemini 3 Wipe Coding Isn’t Ready for Production
Based on my hands-on testing, here’s the hard reality:
- Iterative Changes are a Bottleneck: As shown with the logo, simple tweaks require endless prompting and battling hallucinations, wasting more time than it saves.
- Lack of Control and Precision: The AI often ignores specific prompts (like “vanilla JS”) and adds its own complex layers, making the codebase messy and unpredictable.
- The Scaling Problem: These tools are brilliant for a starting point or a prototype. However, scaling an app, adding complex features, or debugging AI-generated code is currently a significant pain.
- It’s a Boilerplate Generator, Not a Developer: Think of it as a super-powered template engine. It can give you a fantastic head start, but it cannot see a complex project through to a polished, production-ready finish.
The Verdict: A Powerful Prototype, Not a Panacea
Let me be clear: Gemini 3 is a monumental leap in AI. The fact that we can do unlimited prompting for free is incredible and poses serious competition to other tools. It’s an invaluable assistant for generating ideas, scaffolding projects, and learning.
However, the claim that it allows “wipe coding” for non-developers to build and deploy live applications is premature. The journey from a first draft to a shippable product is filled with tiny, precise changes that Gemini 3 struggles to handle consistently.
For now, it empowers developers to be more efficient but still requires a developer’s expertise to bridge the gap between AI output and a real-world application.
What do you think? Have you tried building with Gemini 3? Did you face similar issues, or did you have a better experience? Let me know your thoughts in the comments below
