Prajwal Tomar Profile picture
Mar 19 11 tweets 3 min read Read on X
AI can build landing pages, but most look generic.

Here’s my structured approach using @lovable_dev + 21stdev to create high-converting, polished designs, FAST.

A complete breakdown: Image
1. Start with Design Inspiration & Structure

Before touching Lovable, I gather design references from Dribbble, Behance, and top SaaS landing pages.

I focus on:
- Clean typography and spacing
- High-converting CTAs
- Smooth animations and interactions

Good design starts with understanding what works.
2. Convert Ideas into a Structured Layout

Instead of randomly prompting Lovable, I generate a detailed landing page structure using a custom GPT prompt generator (will share the prompt tomorrow along with the video based on this topic)

This includes:
- Page sections (hero, features, CTA, FAQ, footer)
- UI elements, colors, and typography
- Hover states, animations, and micro-interactions

This ensures AI creates something well-planned, not random.
3. Generate the First Draft in @lovable_dev

I feed Lovable two things:
- A detailed textual description of the page
- A design reference screenshot

This leads to a much more accurate output compared to vague prompts.
4. Refine with 21stdev for High-Quality UI

Lovable speeds up the base design, but I fine-tune it using 21stdev’s UI components to polish the final look.

Some key pre-built components:
- Navigation bars with hover effects
- Optimized hero sections
- Pre-styled feature blocks
- High-converting CTA sections

This eliminates unnecessary manual adjustments.
5. Fix AI Misinterpretations

AI-generated layouts always need refinement. I fix:

- Branding & colors (Lovable defaults to blue, I align it to the brand)
- Spacing & layout (proper padding and margins)
- Typography (ensuring readability and consistency)
- Backgrounds & depth (subtle gradients > flat colors)

This step makes the design feel intentional, not AI-generated.
6. Mobile Optimization is Non-Negotiable

Most users browse on mobile, so I make sure:

- Input fields go full-width for better usability
- Font sizes adjust for readability
- Buttons are tap-friendly
- Sections stack smoothly

Skipping this step kills conversions.
7. Add Subtle Animations for a Modern Feel

A static page feels outdated. I add:

- Smooth fade-ins for hero sections
- Button hover effects
- Feature sections that slide in on scroll
- Background animations for depth

This makes the page feel polished without being overwhelming.
8. Deploy with a single click

Lovable lets you launch your web app instantly. Once tested, connect a custom domain via Netlify, and your site is live, no extra setup needed.
Why This Works Better Than Relying Purely on AI

Most people expect AI to generate a perfect design on its own. It doesn’t work like that.

With a structured approach:

- The AI output is much closer to the final version from the start
- I spend less time fixing errors
- The page has a clear layout instead of random sections thrown together

Lovable + 21stdev lets me build fast without sacrificing quality.
Key Takeaways

- AI-generated designs need structure before prompting
- Lovable + 21stdev is a game-changer for AI-powered landing pages
- AI speeds up development, but manual refinements are still necessary
- Animations & interactions elevate the user experience

This is the fastest and most effective way I’ve found to build landing pages that look great and convert well.

A detailed video on this is coming tomorrow. Stay tuned!

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More from @PrajwalTomar_

Mar 17
Cursor + Supabase + MCP = AI-powered MVP development at its best.

This is how I’m shipping client MVPs faster, cheaper, and smarter with AI handling the backend, database, and migrations.

Let’s break it down. Image
1/ The Problem with Traditional Development

Building MVPs the old way takes too much time:

- Setting up the database
- Writing boilerplate code
- Managing API integrations
- Handling migrations manually

But with Cursor + Supabase + MCP, AI automates most of this, letting you focus on shipping fast.
2/ AI-Powered Frontend with Cursor

Cursor isn’t just an AI assistant, it’s a co-developer that helps:

- Generate UI components instantly
- Automate Next.js & TypeScript setups
- Optimize and refactor code
- Connect to APIs and databases

Think of it as an AI pair programmer that understands your entire project.
Read 11 tweets
Mar 16
Over the last six months, I’ve built 16+ SaaS products using Cursor.

If you’re a beginner, this thread has everything you need to get started and make the most of Cursor
1/ Plan Before Coding

Always start with a solid plan. Use ChatGPT to create your PRD, database design, color palette, and structure. Save these as .md files in Cursor to keep everything consistent. A clear roadmap saves time.
2/ Start with a Strong Foundation

Cursor shines when it’s building on something. Use tools like V0/Lovable to generate the initial UI code, then refine in Cursor. This approach improves accuracy and reduces rework.
Read 15 tweets
Mar 13
MCP + Cursor: AI That Codes, Debugs, and Automates

Here’s how it changes the game.
1/ What is MCP

AI coding assistants have come a long way. But most are still just autocomplete on steroids.

They suggest code, but when something breaks, you’re left troubleshooting manually.

What if AI could connect to databases, debug web apps, and manage GitHub repos, all by itself?

That’s exactly what MCP (Model Context Protocol) enables inside Cursor
2/ What Makes MCP Different?

Traditional APIs are like an old-school assembly line, efficient, but rigid.

They expect specific inputs and outputs, and if something changes unexpectedly, the whole process grinds to a halt.

MCP, on the other hand, is like a seasoned engineer. It doesn’t just follow instructions, it assesses the situation, adapts, and finds alternative solutions.
Read 11 tweets
Mar 12
Cursor + MCP make debugging 10x faster

Here’s how I use them to capture errors instantly, fix bugs quicker, and streamline my workflow.

Full breakdown:
1. What is Cursor & MCP integration?

Debugging is a pain. Jumping between your browser's console and your code editor slows you down.

Cursor is an AI-powered IDE that uses advanced language models to boost your coding efficiency.

MCP (Model Context Protocol) connects external data sources, like your browser, to your editor, making debugging much smoother.

Here's how I use both together.
2. Step-by-step MCP setup (browser extension + IDE)

The setup takes just 2 minutes:

Install the "Browser Tools MCP" Chrome extension.

Open Cursor, then edit the MCP settings to connect with your browser.

Run this quick command to start the MCP server in your IDE terminal:

npx @agentdeskai/browser-tools-mcp@1.2.0

That's it. you're connected.

Here's the GitHub repository:
github.com/AgentDeskAI/br…
Read 7 tweets
Mar 10
How to Set Up Cursor the Right Way

Cursor Rules are outdated. Project Rules is the correct way now. Here’s why it matters and how to set it up properly: Image
1. Why .cursorrules Wasn’t Enough

Cursor originally used a single .cursorrules file at the root of a project, but this approach had serious problems:

Limited control
A single rules file applied to the entire project, even when irrelevant.

Context overload
Cursor’s AI had to process every rule at once, often leading to ignored or misunderstood instructions.

Lack of scalability
As projects grew, .cursorrules became bloated and difficult to manage.

Inconsistent AI responses
The AI often skipped or misapplied rules because the file lacked structure and precision.

Instead of improving AI-generated code, .cursorrules often led to unpredictable results.
2. Introducing Cursor’s Project Rules (.mdc files)

Cursor solved these issues by introducing Project Rules, stored as modular .mdc files inside .cursor/rules/.

This allows for precise rule application per file type, module, or feature instead of one massive, overloaded rules file.

Why Project Rules Are Better:

Granular control
Rules are split into smaller, targeted files, ensuring AI follows the right instructions for each part of the project.

Better AI-generated code
Since Cursor loads only relevant rules, responses are more accurate and consistent.

Easier rule management
Rules are modular, easier to edit, and can be updated independently without affecting unrelated parts of the project.

This means fewer ignored rules, better AI responses, and a cleaner development workflow.
Read 9 tweets
Mar 9
Cursor Agent can break your code.

8/10 times it can work against you and mess up the codebase.

This is how I make Cursor code exactly what I need every single time.

A complete handbook: Image
1. AI coding is 70% planning, 30% execution

Most developers jump straight into coding, expecting Cursor to magically write perfect code.

That never works.

Without proper context and structure, Cursor makes assumptions, leading to inaccurate and messy output.

The key is feeding the AI the right inputs so it follows your intent without guessing.

Here is exactly how I do it.
2. Start with ChatGPT Voice for Planning

Before writing any code, I define exactly what I want Cursor to do.

I use ChatGPT Voice to break down:

- The core idea
- Essential features
- The app flow (pages, navigation, user actions)

Then, I ask ChatGPT to draft a structured project outline.

This ensures I have absolute clarity before moving forward, so Cursor knows exactly what to generate.Image
Read 12 tweets

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