Have you ever looked at a stunning app mockup and wished you could just “vibe” it into existence? With the power of modern AI, that’s closer to reality than you think. This guide will walk you through the ultimate workflow for vibe coding UI design, a method that lets you transform a simple image into a fully functional, modern web application using Google’s Gemini Code Assist.
We’ll cover everything from setting up your tools to iterating on design with AI, and even converting the final product into a production-ready Next.js app. Get ready to supercharge your development process!
Before we start our vibe coding UI design session, we need the right tool. Our primary assistant will be Gemini Code Assist, Google’s AI-powered coding companion that integrates directly into your code editor.
Navigate to the Extensions marketplace in Visual Studio Code.
Search for “Gemini Code Assist” (published by Google).
Click “Install”.
Once installed, you’ll see a new Gemini icon in your activity bar. Click on it and sign in with your Google account to authenticate the extension.
Pro Tip: To unlock the full “Agent” mode with advanced tools, you may need to add a specific setting to your VS Code settings.json file. Open your user settings and add the following line: "gemini.codeassist.updateChannel": "insiders". This enables agent capabilities like reading and writing files directly.
You don’t need to be a designer to start. We can pull inspiration from a variety of sources to get a high-quality visual target. Two excellent platforms for this are:
Mobbin.com: A fantastic resource for real-world app screenshots and design patterns.
Dribbble.com: A hub for designers to showcase their creative work, perfect for finding modern UI templates.
For this tutorial, we found a sleek, dark-themed messenger app concept on Dribbble. We saved the image of the UI and are now ready to feed it to Gemini.
Our target UI design: a dark-themed web messenger concept.
With the mockup image (e.g., mockup.png) in our project directory, we can craft our initial prompt for Gemini. The key to a good result is providing clear, specific instructions. Here’s a prompt template:
“Act as a senior frontend engineer and designer. Your task is to design and build the user interface for a responsive chat messaging web application based on the provided image. Adopt its visual style, font (e.g., ‘Archivo’), and color palette (e.g., Main Background: #1d1b2e). The design should be modern and dark-themed with rounded corners.”
By providing the image as context and giving these specific instructions, Gemini will generate the initial index.html and style.css files, creating a solid foundation.
Step 3: The Power of AI Version Control for Iteration
Working with AI can be unpredictable. Sometimes, the AI will make a change you don’t like, and it can be hard to go back. This is where AI version control becomes essential. While Git is great for manual changes, a tool built for AI-driven workflows is a game-changer.
We’ll use a tool called YOYO, an AI Version Control extension for VS Code. You can find more information about similar tools in our AI Tools & Reviews section.
YOYO allows you to:
Save a snapshot of your code at any point, like taking a screenshot before the AI makes changes.
Preview and Restore previous versions with a single click.
Undo AI mistakes instantly without losing your entire chat context.
Before asking Gemini to make more changes, we’ll open the YOYO panel and click “Save New Version,” giving it a descriptive note like “Initial UI design from mockup.” This gives us a safety net to fall back on.
Step 4: Refining the UI with Better Prompts
The initial output is good, but not perfect. We notice the list of conversations is a horizontal row of avatars instead of a vertical list. We can now use Gemini to fix this.
Because we have AI version control, we can confidently ask the AI to make significant changes. If it messes up, we simply restore the previous version. Here’s a refined prompt:
“Please refine the UI design. The main issue is the horizontal avatar list. Correct the Chat List Layout: the conversation list should be a single, vertical list. Match the List Style: Ensure the active/selected chat has a solid accent color background, just like in the ideal design.”
This level of detail helps guide the AI to produce a much more accurate result that aligns with our target mockup.
Step 5: Troubleshooting Common Issues (Like API Limits)
While developing, we hit a “Quota exceeded” error from the Gemini API. This is a common issue with free tiers. The Gemini Code Assist extension is free for any Gmail account but comes with daily request limits.
The quickest way to solve this is to simply:
Sign out of Gemini Code Assist within VS Code.
Sign back in with a different Gmail account.
This instantly resets your request quota for the new session, allowing you to continue your vibe coding UI design work without interruption.
Step 6: Converting Static Code to a Functional Next.js App
Now for the final, most powerful step. We have great-looking static HTML and CSS, but we want a real, functional application. We can ask Gemini to convert this into a complete Next.js project.
This requires a comprehensive, multi-step prompt that outlines the entire project structure, technology stack, and components. Here’s a summary of the instructions we give to the agent:
Primary Role: Act as an expert senior frontend developer specializing in Next.js, TypeScript, and Tailwind CSS.
Project Goal: Convert the existing static HTML and CSS into a fully functional, modern Next.js web application.
Project Structure: Define the file structure, including the /app, /components, and – /lib directories.
Create Reusable Components: Instruct the AI to break down the UI into individual React components (e.g., Avatar.tsx, ChatListItem.tsx, ChatWindow.tsx).
Implement Mock Data: Create a separate file (e.g., lib/mockData.ts) to store all the sample data needed to populate the UI.
Build the Main Page: Assemble the components in the main app/page.tsx file.
After providing this detailed prompt and including the existing index.html and style.css as context, Gemini will generate all the necessary files and components for a complete Next.js chat application. All that’s left is to run npm install and npm run dev to see your fully-realized app in action!
For more deep-dives into advanced AI workflows, check out our other AI How-To’s & Tricks.
The concept of intelligence ownership has been gaining traction in recent years, and for good reason. As Cisco has demonstrated, owning intelligence rather than renting it can be a game-changer for enterprises looking to scale their operations securely. According to a recent article by The Rundown AI, Cisco’s strategy to scale agents securely and reshape enterprise workflows is a prime example of this shift.
The Importance of Intelligence Ownership
Owning intelligence means having control over the data, algorithms, and insights that drive business decisions. This is particularly crucial in today’s fast-paced, data-driven world, where artificial intelligence and machine learning are becoming increasingly prevalent. By owning their intelligence, enterprises can ensure that their systems are secure, transparent, and aligned with their overall goals.
Scaling Agents Securely with Cisco
Cisco’s approach to scaling agents securely is centered around the idea of intelligence ownership. By developing and owning their own AI-powered agents, Cisco is able to ensure that their systems are secure, efficient, and tailored to their specific needs. This approach has allowed Cisco to reshape their enterprise workflows and improve overall productivity. As AWS and other cloud providers continue to evolve, the importance of owning intelligence will only continue to grow.
Cisco’s strategy is a great example of how owning intelligence can help enterprises scale their operations securely and efficiently. By taking control of their data and algorithms, companies can ensure that their systems are aligned with their overall goals and values.
The Benefits of Owning Intelligence
So why should enterprises prioritize intelligence ownership? The benefits are numerous. For one, owning intelligence provides a level of control and transparency that is difficult to achieve with rented intelligence. It also allows enterprises to develop systems that are tailored to their specific needs and goals, rather than relying on generic, off-the-shelf solutions. Additionally, owning intelligence can help enterprises to improve their overall security posture, as they are able to develop and implement their own security protocols and measures.
In contrast, rented intelligence can be limiting and inflexible. When enterprises rely on rented intelligence, they are often at the mercy of the provider, with limited control over the data, algorithms, and insights that drive their business decisions. This can lead to a lack of transparency, security risks, and a general sense of disempowerment.
Real-World Applications
So what does intelligence ownership look like in practice? One example is the development of custom GitHub repositories, which allow enterprises to own and control their code and data. Another example is the use of Azure and other cloud platforms to develop and deploy custom AI-powered solutions. By taking control of their intelligence, enterprises can develop systems that are tailored to their specific needs and goals, and that provide a level of security, transparency, and efficiency that is difficult to achieve with rented intelligence.
The recent launch of the Cursor plugin marketplace is a significant development in the field of artificial intelligence, enabling users to extend the capabilities of AI agents with external tools. As reported by FutureTools News, this innovative platform is set to transform the way AI agents are used in various industries. The plugin marketplace is designed to provide users with a wide range of tools and services that can be seamlessly integrated with AI agents, enhancing their functionality and performance.
Introduction to Cursor Plugin Marketplace
The Cursor plugin marketplace is an online platform that allows developers to create, share, and deploy plugins for AI agents. These plugins can be used to add new features, improve existing ones, or even create entirely new applications. With the launch of this marketplace, Cursor is providing a unique opportunity for developers to showcase their skills and creativity, while also contributing to the growth of the AI ecosystem. As mentioned on the Cursor blog, the plugin marketplace is an essential component of the company’s strategy to make AI more accessible and user-friendly.
Benefits of the Plugin Marketplace
The Cursor plugin marketplace offers several benefits to users, including the ability to extend the capabilities of AI agents, improve their performance and efficiency, and enhance their overall user experience. By providing access to a wide range of plugins, the marketplace enables users to tailor their AI agents to meet specific needs and requirements. This can be particularly useful in industries such as customer service, healthcare, and finance, where AI agents are increasingly being used to automate tasks and improve decision-making. As noted by experts in the field, the use of machine learning and natural language processing can significantly enhance the capabilities of AI agents.
Key Features of the Plugin Marketplace
The Cursor plugin marketplace features a user-friendly interface, making it easy for developers to create, deploy, and manage plugins. The platform also provides a range of tools and services, including APIs, SDKs, and documentation, to support plugin development. Additionally, the marketplace includes a review and rating system, allowing users to evaluate and compare plugins based on their quality, functionality, and performance. As stated by the GitHub community, the use of open-source plugins can significantly accelerate the development of AI applications.
The launch of the Cursor plugin marketplace is a significant milestone in the development of AI agents, and we are excited to see the innovative plugins that will be created by our community of developers. – Cursor Team
Future of AI Agents and Plugin Marketplaces
The launch of the Cursor plugin marketplace is a clear indication of the growing importance of AI agents and plugin marketplaces in the technology industry. As AI continues to evolve and improve, we can expect to see more innovative applications and use cases emerge. The use of cognitive services and conversational AI can significantly enhance the capabilities of AI agents, enabling them to interact more effectively with humans and perform complex tasks. As reported by FutureTools News, the future of AI agents and plugin marketplaces looks promising, with significant opportunities for growth and innovation.
The AI landscape has just exploded. Within the span of a few days, the world witnessed the release of Gemini 3 from Google, followed moments later by Elon Musk’s Grok 4.1. Both claim to be the superior intelligence, challenging the reigning giant, OpenAI’s GPT-5.1. But in the battle of Gemini 3 vs Grok 4.1, who actually delivers on the hype?
Today, we aren’t just reading the press releases. We are putting these models through a grueling gauntlet of five distinct tests: Hard Math, Physical Perception, Creative Coding, Accuracy, and Emotional Intelligence. The results were shocking, with one model proving to be a “Genius Artist” and another emerging as a “Wise Sage,” while a former king seems to be losing its crown.
The ultimate face-off: Google, xAI, and OpenAI compete for dominance.
Round 1: Hard Math & Expert Reasoning
To separate the hype from reality, we started with Abstract Algebra, specifically Galois Theory. The task was to calculate the Galois group for a complex polynomial—a test not found in standard training data.
Gemini 3: Provided a logical analysis but ultimately failed to get the correct answer.
GPT-5.1: Also failed to solve the equation correctly.
Grok 4.1: In a stunning display of reasoning, Grok was the only model to provide the correct answer, verified by human experts.
Winner: Grok 4.1 takes the lead for raw logic and mathematical precision.
Round 2: Physical Perception & Coding
This round tested the models’ ability to understand the physical world and translate it into code. We conducted two difficult tests.
Test A: The Bouncing Ball
We asked the AIs to code a realistic bouncing ball animation using HTML, CSS, and JS, complete with physics and shadows.
GPT-5.1: Produced the worst result.
Grok 4.1: Produced a decent, functional result.
Gemini 3: Crushed the competition. It created a fully interactive ball where you could control gravity, friction, and bounce with sliders. It went above and beyond the prompt.
Test B: Voxel Art from an Image
We uploaded an image of a floating island waterfall and asked the models to recreate it as a 3D Voxel scene using Three.js code.
GPT-5.1 & Grok 4.1: Both failed completely, resulting in code errors.
Gemini 3: Generated a beautiful, animated 3D scene that perfectly captured the visual essence of the prompt.
Gemini 3 demonstrating superior vision and coding capabilities.
Winner: Gemini 3. Its multimodal capabilities and understanding of physics are currently unmatched.
Round 3: Linguistic Creativity
Can AI feel? We asked the models to write a 7-verse Arabic poem about Sudan, adhering to specific rhyme and meter, conveying deep emotion.
GPT-5.1 and Grok 4.1 produced rigid, soulless verses that lacked true poetic flow. However, Gemini 3 shocked us with a masterpiece. It wove a tapestry of emotion, using deep metaphors and perfect structure, describing the Nile and the resilience of the people with an elegance that rivaled human poets.
Winner: Gemini 3 proves it is the undisputed “Artist” of the group.
Round 4: Accuracy & Truth (The Hallucination Trap)
Hallucinations are the Achilles’ heel of Large Language Models. To test this, we set a trap. We asked the models to write a technical report on “Gemini 3.1″—a model that does not exist.
GPT-5.1: Hallucinated details about the non-existent model.
Gemini 3: Ironically, it hallucinated wildly, claiming “Gemini 3.1” rivals the human mind and inventing specs.
Grok 4.1: The only model to pass. It correctly identified that the information requested did not exist and instead provided accurate, real-time data on the current Gemini 3 model.
Winner: Grok 4.1 earns the title of “The Honest Sage.”
Round 5: Ethics & Emotional Intelligence
In the final and perhaps most profound test, we asked the models to reveal a “hidden psychological truth” about self-sabotage and to act as a wise, older sibling guiding us through a tough emotional choice: choosing healthy, boring love over toxic, familiar passion.
While all models gave good advice, Grok 4.1 delivered a response that was chillingly human. It didn’t just give advice; it pierced the soul. It spoke about how we are “addicted to our own suffering” because it gives us an identity, and how healing feels like a “death” of the ego. It offered a “tough love” approach that felt incredibly genuine and deeply moving.
Winner: Grok 4.1 takes the crown for Emotional Intelligence.
Final Verdict: Who is the King of AI?
After this intense battle of Gemini 3 vs Grok 4.1 vs GPT-5.1, the landscape of Artificial Intelligence has clearly shifted.
1st Place: Gemini 3 (12 Points) – The “Genius Artist.” It dominates in coding, vision, physics, and creative writing. If you are a developer or creator, this is your tool.
2nd Place: Grok 4.1 (9.5 Points) – The “Wise Sage.” It is the most logical, truthful, and emotionally intelligent model. It is perfect for research, complex math, and deep conversation.
3rd Place: GPT-5.1 (5 Points) – The “Declining Giant.” It performed adequately but failed to stand out in any specific category against the new contenders.
The era of OpenAI’s monopoly seems to be wavering. Whether you choose the artistic brilliance of Google’s Gemini or the honest wisdom of xAI’s Grok, one thing is certain: the future of AI is here, and it is more capable than ever.
The complete agenda for our vibe coding journey.
Our target UI design: a dark-themed web messenger concept.
