Over the past few weeks, I’ve been exploring a range of AI tools, testing them in real projects and integrating them into my daily workflow. My goal was simple: to see how far AI can truly go in helping developers build real, production-ready products.
During this time, I used several of these tools to ship client projects, including a full web MVP built in just 30 days and an Android mobile app. Since I already had the UI designs, my main focus was turning them into fast, pixel-perfect frontends and integrating a solid backend behind them. Throughout that process, I tried dozens of AI tools for both frontend and backend tasks.
In a space where every major company is experimenting with AI, I wanted to dig deeper and test tools that don’t just generate code but also understand design.
In this article, I’ll take you behind the scenes of my experience with front-end AI tools. I’ll talk about how they performed, what impressed me, and why prompting is a crucial skill every developer should master when using AI tools.
What We Will Cover
What is an AI Agent?
AI agents are computer programs that can do certain jobs on their own. They collect information, think about it, and then decide what to do — a bit like how people make decisions.
For example, a customer help agent can look for answers to a person’s question or ask a real person to help if needed.
Here’s how AI agents work:
Have a goal: Each agent is given something to do, like “answer people’s questions” or “help design something.”
Make choices: The agent decides on its own how to do that job in the best way.
Use information: It looks around – like reading messages, files, or the internet – to find helpful data.
Take action: It performs an action based on what it has learned, such as replying to someone, finishing a task, or using another app.
Work by themselves: They can do many things without people telling them each step, but for very hard problems, they can ask a person for help.
Agents can learn and improve over time, and they get better with practice. Agents can also work together and share information to more effectively finish a big job. And they can use other programs that help them get live information, like the weather or news.
Examples of agents:
Customer help: An agent can answer customer questions and find solutions.
Computer work: It can help make apps, write small parts of code, or fix basic computer tasks.
Personal helper: Voice apps like Google Assistant can set reminders or answer your questions.
What is Front-End Development (and Why It’s So Challenging)
Front-end development is the part of web and app creation that users actually see and interact with. It’s where creativity meets logic, and where we turn designs into real, functional experiences. Every button you click, animation you notice, and page you scroll through is built by front-end developers.
A front-end dev’s goal is simple but demanding: make websites beautiful, fast, and easy to use. That means balancing visuals with performance, like choosing the right layouts, colours, and interactions so everything feels smooth and intuitive.
The front end is often called the client side. It’s what users touch and feel. Behind it is the back end, which handles servers, data, and logic. APIs connect the two, ensuring design and functionality work together.
But building that harmony isn’t easy. Front-end development moves fast, and developers face constant challenges:
Keeping up with change: New frameworks and tools appear every month. The key is to learn continuously but choose wisely.
Cross-device consistency: Websites must look great on every screen, from desktop to mobile.
Speed and performance: Users hate waiting, so optimising code, images, and assets keeps experiences fast.
Responsive design: Adapting layouts for different devices is now a must, not a choice.
Accessibility: Good design includes everyone. Small tweaks like clear colours and keyboard navigation matter.
State management: Handling dynamic data efficiently is crucial for smooth user interactions.
Collaboration: Front-end work is teamwork – Git, reviews, and automation keep projects organised.
In short, front-end development is the art of turning design into function. It’s all about crafting interfaces that look great, perform well, and feel effortless. It’s fast, creative, and constantly evolving – and that’s exactly what makes it so exciting.
Front-End Tools, Technologies & Web Design Essentials
Before exploring AI-assisted tools, it’s important to understand the basics of how modern front-end development works.
At its core, every website runs on three pillars: HTML, CSS, and JavaScript. HTML gives structure to the page, CSS handles the design, and JavaScript adds interactivity. Together, they turn static layouts into dynamic experiences.
Once you’ve mastered the basics, frameworks and libraries help you move faster:
React makes it easy to build reusable UI components that scale well.
Next.js adds server-side rendering and better SEO performance for modern web apps.
Tailwind CSS speeds up styling with ready-to-use utility classes, helping developers design responsive layouts in less time.
Good design is just as important as good code. Web design focuses on usability, layout, and visual appeal, ensuring users find what they need effortlessly. A great design balances aesthetics with performance and accessibility, working smoothly across devices and screen sizes.
As a developer, it’s also crucial to think like a designer: paying attention to typography, spacing, and colour harmony. Tools like Figma, Adobe XD, and Sketch make collaboration between designers and developers easier by providing visual clarity before coding begins.
In short, mastering these tools – from HTML and CSS to React, Next.js, and Figma – gives you everything you need to build fast, responsive, and visually engaging web experiences.
My Experience with AI Tools
As AI is changing how software is being built and delivered, I have had the opportunity to explore and work with a lot of tools in the areas of Web Development, Data Analysis, Testing, DevOps and Web Design. And I’m impressed by the quality of work that an AI agent can do based on what I have observed so far.
But what I’ve learned is that you will have plan very smartly if you’re going to use any AI tools for your projects, especially if you’re building full stack software. This process requires that multiple parts talk in parallel – and if you don’t give the AI clear context and a well-crafted and specific prompt, chances are things won’t turn out as you hoped.
I have used many of the top AI tools like Cursor, Windsurf, Replit, V0 by vercel, and Bolt.dev for my personal projects. But for my clients, I still prefer doing things primarily on my own, using AI as a helpful tool when appropriate.
Why and How I Tested the AI Agent
If you’ve ever built a mobile or web app from a Figma design, you already know the pain. Manually copying pixels, measuring paddings, and aligning text styles is like playing Tetris with code.
Recently, I was building a mobile app for a client. I received a complex Figma design with over 20 screens, animations, and responsive layouts. My task was to turn it into real, production-ready code: design, develop, integrate the backend, and finally deploy it to the Play Store.
The work was rewarding, but one thing was painfully clear: Copying and pasting values from Figma to the code editor was exhausting and time-consuming.
That’s when I started looking for a smarter way to do this – and stumbled upon “Design to Code” tools like Kombai, Builder.io, and V0. These are the tools we’ll be examining here, so let’s meet them.
What Is Kombai?
Kombai is an AI-powered design-to-code platform that converts Figma designs into clean, production-grade React, React Native, or Next.js code.
But it’s not just a Figma converter. It positions itself as a front-end intelligence layer. It’s an AI agent that understands your codebase, plans refactors, and applies real-world development logic.
What Is V0?
V0 is an AI-powered design-to-code from the Vercel platform that converts Figma designs into clean, production-grade code, along with an option to build a modern frontend design with prompts. V0 is specialised in making frontends with prompts.
V0 focuses on seamless Next.js integration and instant code generation within the Vercel ecosystem, and it’s ideal for rapid prototyping.
What Is Builder.io?
Builder.io is an AI-powered design-to-code tool, from the Vercel platform, that converts Figma designs into clean, production-grade React or Next.js code, along with an option to build
Builder.io, on the other hand, takes a visual-first approach, combining CMS-like editing with design-to-code export capabilities
Why I Decided to Test and Compare
My first impression was curiosity mixed with skepticism. The benchmarks for all the coding tools were awesome, as each company claims they are perfect for the desired niche. But I wasn’t satisfied with their claims, so I decided to test them on my own and then make a final call.
My goals were simple:
See how well each tool translates design components into usable React code.
Test their understanding of layouts, responsiveness, and reusability.
Evaluate whether these tools can genuinely save time for real developers, not just look fancy in demos.
The end goal was to chase these personal benchmarks which I set for myself:
The 12 Benchmarks I Used to Compare AI Design-to-Code Tools
When testing tools that turn Figma designs into code, it’s easy to get lost in flashy features. So instead of judging by first impressions, I used a set of clear benchmarks: things that actually matter to developers when building real projects.
Here’s a simple explanation of each benchmark I wanted to test and why it’s important:
1. Setup and Integration
This checks how easy it is to get started. Can you connect your Figma design and start generating code quickly? A smooth setup means less time configuring and more time creating.
2. Figma-to-Code Workflow
This is the heart of the process. It measures how accurately and efficiently the tool turns your Figma design into working code. The better this step is, the less time developers spend fixing layout or spacing issues later.
3. Live Preview / Sandbox
A live preview lets you instantly see what the generated code looks like, right inside your browser or IDE.
This saves hours because you don’t have to export, run, or reload files just to check small design changes.
4. Repo and Code Intelligence
This one looks at whether the tool understands existing projects. Can it read your GitHub repo, generate documentation, or give code improvement suggestions? If yes, it’s more than just a design-to-code converter – it’s a real coding assistant.
5. Code Quality and Structure
This measures how clean, readable, and reusable the code is. Good tools generate code that looks like a developer wrote it – not a machine. This makes collaboration and scaling much easier.
6. AI Assistance / Agent Mode
Modern AI tools often act like smart teammates. This benchmark checks if the AI can refactor, explain, or modify code when you ask, not just generate it once and stop. A good AI agent saves time by helping you understand your own code better.
7. Design Fidelity
This is about accuracy. Does the generated code look exactly like your original Figma design? If the fonts, colours, and layouts don’t match, the whole purpose of using such a tool is lost.
8. Responsiveness
Websites and apps must work well on all screen sizes, from mobile phones to big desktop monitors.
This benchmark checks if the tool automatically handles breakpoints and layouts for different devices.
9. Cross-Platform Capability
Some tools only work for the web, while others can generate code for mobile or native apps too. Cross-platform support means you can reuse the same design logic across multiple projects, saving huge amounts of time.
10. Target Audience Fit
Not every tool is made for every kind of user. Some are developer-focused, others are more for designers or content teams. This benchmark helps identify who will benefit the most from each tool.
11. Unique Differentiator
Every tool needs to have something special. It could be repo analysis, AI-assisted coding, or tight integration with deployment platforms like Vercel. This benchmark helps highlight what makes one tool stand out from the crowd.
12. Overall Performance and Value
Finally, this is the big picture: how well the tool performs across all areas combined. Does it really save time? Is it worth using for production work, or only for prototypes? This helps developers decide whether it’s worth adopting long-term.
Testing in Real Time
Now it’s time to test the agents for real work. To do so, I’ll be using an open Figma file, which is complex and free for commercial use (for educational purposes only). Here is the Figma file. I’ll use this file for all the demonstrations below.
A few things to keep in mind as you get going:
You’ll need to enable WebGL if Figma is not working on your browser.
You need to know how to copy the Figma file link and how to export the design from Figma. To copy the Figma design file link, open your Figma design file and simply select your desired design, select copy/paste, and then select copy link to selection. It will copy the complete design file URL.
Now I’ll go through all the benchmarks one by one for each tool.
Benchmark 1: Setting Up the Tools
Kombai
To install the Kombai extension in VS Code, simply open the Extensions Marketplace, search for 'Kombai', and click 'Install'.
Now, click on the “Let’s get started” button and sign up or log in with your credentials.
The agent extension UI looks like this on VSCode:
V0
To get started with V0, visit the official V0 website and sign up or log in with your credentials.
After you’ve successfully login, you will be redirected to the screen below:
Builder.io
Visit the official builder.io website and sign up or log in with your credentials.
After you’ve successfully login, you will be redirected to the screen below:
Select “Convert Figma to Code” and Click on “Continue with Fusion”.
Then select your tech stack. I chose React.
Now you will be redirected to the screen below:
Benchmark 2: Testing Figma to Code
I tested the agent with my Figma setup on both my client projects and personal projects first. I used a simple design (which I gave in the link above).
To continue with our experiment, open the Figma design file and simply select your desired design. Then select copy/paste, and copy link to selection*.* It will copy the complete design file URL. Now we’ll look at how each tool handles the process of converting Figma designs to code.
Kombai
Open the Kombai agent in VSCode and select Figma (icon) agent at the bottom of the agent toolbar. It will open a new pop-up. In it, paste the link you copied to the design file. Then you can prompt the agent with what you want it to do with this Figma file.
In my case, I wanted to replicate the same design so I gave this prompt:
“You are an expert UI/UX designer and your task is to build and replicate the entire Figma design in the HTML/CSS/JS code from the attached URL.“
After confirming this, the agent will start working to replicate the same design as you’ve shared in the Figma file.
And here are the results:
You can also copy the link of any Figma component and follow the same approach to get the desired outcome.
V0
V0 doesn’t allow you to directly import designs using a simple Figma link. Instead, it provides an “Import from Figma” workflow where you first need to export your Figma design then upload it to V0. Once imported, you’ll need to describe your intent through detailed prompts or upload a screenshot of the design for interpretation.
I followed this process myself, but it turned out to be quite time-consuming. The tool didn’t generate the expected layout in a single attempt – I had to refine my prompts multiple times to get a usable output. Each iteration also consumed additional tokens, which can add up quickly during experimentation.
Builder.io
Before starting, make sure everything is ready.
Step 1: Install the Builder Plugin
Go to the Figma Community and search for “Builder.io” or “Visual Copilot” and then install the plugin.
You’ll want to use the auto layout option in Figma. This helps the AI understand your layout better (margins, spacing, and responsiveness). A clean Figma design means better code output.
Step 2: Link Your Components
This step makes Builder.io special. You “teach” the AI what each part of your design actually means.
- Example: Tell it, “When you see this Figma Button, use my real
<PrimaryButton />React component.” This way, it doesn’t just copy pixels. Instead, it builds real, reusable components that match your existing codebase.
Step 3: Export Your Figma Design
Once the components are mapped, exporting is simple:
Select the design frame you want.
Open the Builder plugin and click “Smart Export.”
The plugin copies all the important layout and style data automatically.
Step 4: Paste and Prompt the AI
Now switch to Builder.io Fusion. This is where the AI magic happens. Paste what you copied from Figma into the AI input box. Then just add your prompt, making sure it’s short and clear.
- Example: “Build this as a React component named ProductCard.” You can also specify small tweaks like “Make this layout stack vertically on mobile.”
Step 5: Review and Refine
Builder.io shows you a live preview of the generated code.
Check the result: Does it look and behave like your design?
Make tweaks: You can ask the AI for updates directly, like “Increase font size” or “Add hover effect.”
Create your PR: Once you’re happy, Builder.io helps you send it directly to GitHub as a Pull Request.
I found it too technical to simply convert a simple Figma design into code.
Benchmark 3: Real-Time Sandbox Preview
All the tools mentioned in this blog provide a live sandbox preview where you can see the generated output instantly.
This reduced the guesswork — I didn’t have to export, clone, or rebuild just to check how it looked.
Benchmark 4: Repo Analysis & Documentation
Repo analysis is the ability to scan your GitHub repo, generate summaries, and even suggest architectural improvements. This feature alone could save hours of onboarding time for new developers joining a project.
I tested this feature for
V0 - The Vercel repos can be connected easily, but understanding the complete code and context is still not available.
Builder.io- It performs repository analysis not just for understanding, but for the explicit purpose of generating code that is interoperable with and dependent on your existing architecture.
Kombai -I also tested repo intelligence by connecting an existing as well as a old project, which generated a full PROJECT_ANALYSIS.md file — summarising app’s structure, tech stack, and potential improvements.
Benchmark 5: Smart Planning Phase
The smart planning phase helps build an application before taking any action. It presents a detailed “plan” outlining what it will build. You can approve or modify this — giving you total control, just like working with a teammate.
It’s like having a junior developer who walks you through their approach before writing code.
V0: It skips this stage and starts generating output directly from the given prompt or Figma input. While it’s fast, you often need several prompt iterations to align the output with your expectations.
Builder.io: It offers partial planning through its component mapping step, where you define how Figma components connect to existing code. It’s useful, but more manual and setup-heavy any automatic planning phase.
Kombai: Instead of rushing into generation, Kombai first asks for confirmation before making changesits an automatic system by default.
Benchmark 6: Responsiveness and Design Fidelity
V0, on the other hand, focuses more on functional React/Tailwind output than pure design precision. It captures structure well but often requires post-generation adjustments to match the original Figma layout.
Builder.io delivers strong design accuracy when working with mapped components, but it sometimes simplifies layouts to maintain consistency with your design system.
Kombai preserves the design fidelity from Figma to code. Even typography, colour palettes, and animations carry over seamlessly.
Benchmark 7: Code Quality & Structure
V0 also generates solid and clean React + Tailwind code but leans heavily on its internal conventions, making customisation trickier. It also uses Shadcn component by default until explicitly asked to use something else
Builder.io prioritises no-code flexibility; while its code export is clean, it’s more suited for CMS-driven projects than heavy custom logic.
Kombai-generated code isn’t bloated or minified. It’s readable, clean, and production-grade — making handoff between design and dev smooth.
Benchmark 8: AI Agent Mode
V0 has a prompt-driven system too, but it’s less conversational and context-aware — it works well for isolated code generation, not project-level edits.
Builder.io is more rule-based, with minimal AI-driven modification features.
Kombai: You can talk to it like an assistant — ask it to refactor files, add sections, or explain existing code.
It understands context and acts on real project data, not just prompt guesses.
Benchmark 9: Cross-Platform Vision
V0: remains web-focused (optimised for React + Next.js).
Builder.io: supports multiple frameworks (React, Angular, Vue) but stays primarily within the browser ecosystem.
Kombai isn’t limited to web — it can also handle React Native, making it a powerful tool for both web and mobile developers.
Target Audience
Kombai – For Developers Who Want AI Inside the Codebase
Best suited for frontend developers who want AI as a coding partner rather than just a design converter.
Ideal if your workflow includes both design-to-code automation and repository-level analysis.
Think of Kombai as an AI teammate that understands your project, not just your Figma design.
V0 (by Vercel) – For Speed-Focused Frontend Developers
Built for developers who want fast UI generation without leaving Vercel’s ecosystem.
Perfect for rapid prototyping or spinning up functional interfaces in minutes.
If your focus is efficiency and integration with Next.js workflows, V0 is a solid choice.
Builder.io – For Product and Marketing Teams
Great for teams who need visual control over design and content without diving deep into code.
Works best when you want a no-code or low-code workflow that still respects your design system.
Ideal for marketers, designers, or PMs who want to update layouts and pages independently.
Feature Table
I have built this table to show how the different tools perform on each benchmark. It will give you a complete idea about what’s going on
Feature / Phase
Kombai
V0 (by Vercel)
Setup & Integration
• Direct VS Code extension for instant setup. • Connects with Figma & GitHub. • Auto-generates a Plan Phase outlining build steps.
• 100% web-based; paste Figma link or prompt. • Optimized for Next.js & Vercel deployment. • No local IDE setup required.
• Drag-and-drop visual builder. • Integrates via SDKs/APIs for React, Angular, Vue. • Expansive plugin ecosystem for dev tools.
Figma-to-Code Workflow
• One-click import of Figma design link. • Auto-fetches layouts inside VS Code agent. • Outputs pixel-perfect React or React Native components with responsive logic.
• Converts Figma links or screenshots into React/Tailwind code. • Includes iterative AI refactoring.
• Converts visual design blocks into reusable components. • Requires page mapping for deeper Figma code integration.
Live Preview / Sandbox
• Real-time sandbox preview inside VS Code. • Updates instantly when code is modified.
• Browser-based preview for Next.js environments. • Syncs with Vercel’s live deployment previews.
• True WYSIWYG editor; visual changes reflected instantly. • Live sync with backend CMS.
Repo & Code Intelligence
• Performs full repo analysis. • Generates PROJECT_ANALYSIS.md summarizing architecture. • Suggests structural and performance improvements.
• Project-aware but limited repo insights. • Primarily focused on UI generation.
• No code-level repo analysis. • CMS-focused integration instead of repository parsing.
Code Quality & Structure
• Clean, readable React code with proper naming conventions. • Modular, reusable components with prop management. • No minified or bloated output.
• Auto-generated React/Tailwind code. • Readable but may require manual refinement.
• Clean component bindings, but limited control beyond visual UI.
AI Assistance / Agent Mode
• Full conversational AI agent inside IDE. • Can refactor, modify, and explain code contextually. • Understands real project context.
• Basic chat-style UI component generator. • Limited contextual awareness.
• Minimal AI — primarily rule-based logic for layout generation.
Design Fidelity
• Near pixel-perfect accuracy for typography, spacing, colour, and animation.
• Medium fidelity; optimised for speed and developer control.
• Visual-first; sometimes diverges from original Figma layouts.
Responsiveness
• Auto-detects mobile breakpoints. • Generates adaptive layouts natively.
• Supports responsive design with limited manual controls.
• Fully responsive UI is possible but often requires tweaking.
Cross-Platform Capability
• Supports both Web and React Native builds.
• Web-focused (React + Next.js).
• Web and CMS-powered apps (React, Angular, Vue).
Target Audience
• Developers who want AI inside the codebase. • Ideal for design-to-code + repo analysis workflows.
• Frontend developers who need fast UI generation within Vercel’s ecosystem.
• Product and marketing teams seeking visual control with minimal coding.
Unique Differentiator
Combines agentic AI + repo intelligence + Figma-to-code in one continuous workflow.
Deeply integrated with Vercel’s hosting and deployment pipelines.
Highly visual CMS-powered builder for content-driven sites.
Overall Verdict
Developer-first approach that seamlessly merges Figma, repo analysis, and AI-assisted coding.
Great for rapid prototyping and quick Next.js frontends.
Best suited for designers and content teams needing flexible, visual control.
I have also seen some areas where I think making small improvements will make this awesome tool better
A Few Areas to Improve
Even though all the agents performed exceptionally, here are some things I noticed that can make all even better:
Figma Auth Flow: Occasionally re-asks for connection; smoother OAuth handling would help.
Tech Stack Memory: Sometimes retains old stack choices — needs a clearer “Confirm Stack” UI.
Session Management: The Ability to reset or switch projects quickly would streamline workflow.
These are small usability fixes, not performance issues.
How to give proper context and prompt to AI Agents/tools
When I have to speak to masses on any topic, I always say one thing first, that “I am only responsible for what I speak, not for what you listen” by saying this, I make sure that people don’t get confused they hear and understands me clearly similar process is followed by AI tools as AI tools are trained on predefined data sets and to fetch the exact information or get any task done you will have to give clear instruction to any AI tool let tell me you a small story Imagine you’ve just met a super-intelligent assistant — an AI who can write, design, analyze, and even brainstorm ideas with you. But here’s the catch: this AI doesn’t read your mind. It only understands what you say. So, if you want great results, you have to talk to it clearly — like explaining your idea to a teammate who’s brilliant but new to your project.
That’s where the art of giving the right prompt and context comes in.
The Power of Context
Think of context as the background story. Without it, the AI is like a painter who doesn’t know what scene you want.
To give the right context, you can do a few things:
-
Define roles: Tell the AI who you are and what role you want it to play.
For example: “You’re a marketing expert helping me write a blog post.”
-
State your goal: Explain why you’re asking.
Are you trying to get information, write an article, or brainstorm ideas?
Add details: Give as much background as possible — facts, examples, or the situation you’re dealing with.
-
Identify your audience: Let the AI know who will read or use the final output.
Is it for professionals, everyday readers, or kids?
This helps the AI choose the right tone and vocabulary.
The Craft of a Prompt
Once your context is set, the prompt is your actual instruction — the “what to do.”
Here’s how to make it powerful:
Be clear and specific: Avoid vague or complicated sentences. Use straightforward language.
-
Break it down: If your task is big, divide it into steps.
Like: “First do this, then that, and finally summarize.”
Show examples: Demonstrate what kind of result you’re expecting.
Set tone and format: Tell the AI how to reply — formal, friendly, or creative — and in what format — list, paragraph, or table.
Set limits: Mention what you don’t want in the answer, so it stays focused.
A Quick Example
Let’s see the difference between a weak prompt and a strong one.
❌ Weak Prompt:
“Tell me something about history.”
✅ Improved Prompt:
Role: “You’re a history professor.”
Context: “I’m preparing a presentation for college students.”
Goal: “I need a summary of the main causes and results of World War II.”
Format: “The information should be in bullet points.”
Tone: “Keep it simple and easy to understand.”
✅ Best Prompt (all in one):
“You’re a history professor, and I’m creating a presentation for college students about World War II. Please give me a brief summary of the major causes and outcomes of the war in bullet points, using clear and easy-to-understand language.”
Q&A
How much experience do I have with tech and AI tools?
I’ve been a software engineer for several years, working across multiple technologies and building products for a variety of domains. My journey with AI started back in college, where my final-year project focused on AI. Since then, I’ve been exploring AI tools extensively, testing their capabilities, and leveraging them to deliver projects efficiently within tight timelines. So yes—I have practical experience and a solid understanding of AI.
Methodology & Disclosure
For fairness, I used one common Figma design (linked above) and the same control prompt across tools where possible. I performed hands-on testing with Kombai, V0 and Builder.io (VS Code extension) and captured live previews, generated components, and repo analysis artefacts (screenshots in the demo gallery).
Responsible use of AI in code generation
While AI accelerates design-to-code workflows, developers must still review generated outputs for maintainability, accessibility, and long-term performance.
Why am I writing about Frontend AI tools?
This is an important question. Being a frontend developer myself for 5 years, I have had the opportunity to work with all the top tools like Figma, Adobe XD and Canva, etc, for designing applications. As I am a developer, I had to convert the designs into working code now, since AI is dominating everywhere. I had the chance to automate my design to code work using AI.
What’s my experience with AI tools?
From my experience, I’ve been using tools like Kombai, V0, Builder.io, Replit and Cursor for my development workflow; all have worked well, but sometimes they break or make something I never asked them to do. The type of work decides the type of tool, like for backend, Cursor is good, and for frontend, Kombai, which is what I am working on.
What’s the code quality produced by the Tools?
The code quality is impressive. I’ve deployed multiple frontend projects generated by such agents for clients, and after rigorous reviews, no client has reported UI/UX issues. Everything works smoothly and reliably.
Most of the frontend code is safe until it throws a bug, but still, I prefer to do a rigorous code review for frontend and backend.
How can I get AI tools for testing?
It’s simple: visit the respective website of the tools and do a simple sign-up. Most of the tools have free AI credit limits.
Final Notes
Using AI to accelerate your work is a valuable skill—but relying on it entirely can backfire. When AI starts hallucinating or fails to deliver exactly what you need, the responsibility falls on you to fix it and reach the desired outcome.
Frontend development, in particular, can be challenging. Before using any AI tool to complete a task, make sure you understand the fundamentals of the technology and your chosen stack. This knowledge is often underrated but incredibly useful in the long run.
In this blog, I’ve shared what I’ve found most helpful while working with Frontend AI tools. The market is full of high-level solutions from tech giants, but many emerging tools are delivering top-notch results, competing at the same level and sometimes even surpassing big names.
AI won’t take your job—but a developer with AI skills certainly will.
Conclusion
In practice, these tools free up hours of manual layout work — but they don’t replace developer judgment. They accelerate the repetitive parts so you can focus on logic, architecture, and user experience, where developers can focus on building good projects, not hustling to copy and paste CSS from Figma to the IDE.
I wrote this blog to educate about how different tools are available in the market and how you can make the best use of those tools. I have even introduced a long benchmarking guide for developers so that next time, when they have to choose an AI tool for anything how they know how to proceed and using the right tool is more important than chasing 10 different tools.
In my next blog, I will be writing more about something new I have been working on for a long time, and I am contributing to it.
Design+ AI + Development = The Ultimate Developer Stack 🔥
By mastering these skills, you can turn any idea into a real-world product, secure high-paying jobs, and even start your tech venture.
Now it's your turn—what are you building next? Let me know in the comments or DM me! 👇
That’s all from my side. If you found this article helpful, feel free to share it and connect with me. I’m always open to new opportunities:
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