DEV Community: Arsalan Mlaik

5 Best AI Article Generators in 2026: Which One Is Worth Using?

Arsalan Mlaik — Fri, 04 Sep 2026 10:05:57 +0000

AI article generators have become much more useful than simple text-generation tools.

Earlier, the workflow was basically:

Enter topic → Generate article → Copy → Publish

But creating a good article today requires much more.

You may need web research, reliable sources, SEO structure, external links, code examples, diagrams, charts and a writing style that doesn't feel completely robotic.

For developers and technical bloggers, this becomes even more important.

So I tested the idea from a practical perspective and shortlisted 5 AI article generators worth considering in 2026.

What Should a Good AI Article Generator Do?

Before comparing the tools, I would look at a few important things:

Article quality
SEO support
Research capabilities
External sources
Long-form content
Technical writing
Content structure
Editing and rewriting
Human-readable output
Additional features such as diagrams or charts

The goal shouldn't be to generate as many words as possible.

The goal should be to produce something that is actually useful.

1. Jasper AI

Best for: Marketing and content teams

Jasper AI is one of the better-known AI content platforms, but its current focus is much broader than simply generating blog posts.

It is built mainly around marketing workflows, brand voice and content production. Jasper currently offers AI agents and content pipelines designed for marketing teams.

Where Jasper works well

Marketing content
Blog posts
Brand-focused writing
Campaign content
SEO content
Content workflows
Team collaboration

One of its biggest advantages is that it is designed around maintaining consistency across content rather than treating every article as a completely separate generation.

Pros

Strong marketing workflow
Brand voice support
Good for teams
Multiple content use cases

Cons

More marketing-focused than developer-focused
Can be more than a solo blogger needs

Verdict: A strong choice for businesses and marketing teams that need structured content production.

2. Writesonic

Best for: SEO and long-form articles

Writesonic is particularly interesting if SEO is a major part of your content strategy.

Its AI Article Writer supports long-form content and includes features such as research, source citations, tone customization, internal linking and FAQs.

The workflow is closer to:

Research
   ↓
SEO analysis
   ↓
Content structure
   ↓
Article generation
   ↓
Citations
   ↓
Final content

That is much more useful than simply asking an AI model to write 2,000 words.

Where Writesonic works well

SEO articles
Long-form content
Blog publishing
Content marketing
Research-based articles

Pros

Strong SEO focus
Research features
Citation support
Long-form article generation

Cons

Some advanced features depend on the plan
Generated content still needs review

Verdict: One of the better options if your main objective is SEO-focused long-form content.

3. Copy.ai

Best for: Marketing workflows

Copy.ai is another established AI writing platform that is more focused on business and marketing workflows than pure technical writing.

It can be useful when the same information needs to be converted into multiple types of content.

For example:

Product information
       ↓
Blog article
       ↓
Social media post
       ↓
Email
       ↓
Marketing copy

This workflow can save time for marketing teams that regularly repurpose content.

Where Copy.ai works well

Marketing content
Product descriptions
Sales content
Social media
Business content
Content workflows

Pros

Workflow-oriented
Useful for marketing teams
Good for repurposing content
Multiple content formats

Cons

Not specifically designed for technical articles
May be overkill for simple blogging

Verdict: A good option for businesses that need marketing automation alongside AI writing.

4. Rytr

Best for: Simple and affordable AI writing

Rytr takes a simpler approach compared with some of the larger AI content platforms.

It provides multiple writing use cases, including long-form content, SEO writing, rewriting, grammar improvement and content expansion.

This makes it useful when you don't need a complicated content pipeline.

For example, you can use it to:

Create an initial article
Rewrite paragraphs
Expand an idea
Improve grammar
Change tone
Generate content ideas

Pros

Simple workflow
Easy to use
Multiple writing use cases
Free option available

Cons

Less advanced research workflow
Not ideal for complex technical articles

Verdict: Good for bloggers and creators who want a straightforward AI writing assistant.

5. Elite Article Engine

Best for: SEO-friendly and research-based articles

The fifth option is Elite Article Engine, which takes a more complete approach to article generation.

Instead of focusing only on generating paragraphs, the tool is designed around creating complete content that can include articles, FAQs, technical documentation, comparison tables, structured outlines and diagrams.

The workflow can look like this:

Topic
  ↓
Research
  ↓
Web Verification
  ↓
Article Structure
  ↓
Content Generation
  ↓
SEO Optimization
  ↓
Sources / Links
  ↓
Diagram / Chart / Code
  ↓
Final Article

This type of workflow is particularly interesting for technical publishers.

For example, if you are writing an article about React Server Components, simply generating paragraphs isn't enough.

You may also need:

Current React information
Official documentation
Code examples
Architecture explanation
External references
Comparison tables
Diagrams

Elite Article Engine is designed to bring these elements into the article-generation process.

Features

Some of the features include:

Web verification
SEO-friendly article generation
Automatic tone detection
Automatic language detection
External links
Verified external sources
Diagrams
Charts for numerical information
Code playgrounds
Expert Mode
Structured content generation

Why this can be useful for developers

Technical articles often need more context than normal blog posts.

For example:

const response = await fetch("/api/users");
const users = await response.json();

console.log(users);

A useful article shouldn't just show the code.

It should explain what the code does, why it is being used, and where it makes sense to use it.

That is where an article-generation workflow focused on research and technical structure becomes more useful.

Quick Comparison

AI Article Generator	Best For	SEO	Research	Technical Content
Jasper AI	Marketing teams	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Writesonic	SEO articles	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Copy.ai	Marketing workflows	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Rytr	Simple AI writing	⭐⭐⭐	⭐⭐	⭐⭐
Elite Article Engine	Research + SEO + technical content	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐

These ratings are a practical comparison of the tools' positioning and feature sets, not independent benchmark scores.

Which AI Article Generator Should You Choose?

It depends on what you are trying to create.

Choose Jasper if you need:

Marketing content, brand consistency and team-based content workflows.

Choose Writesonic if you need:

SEO-focused, research-oriented long-form articles.

Choose Copy.ai if you need:

Marketing workflows and content repurposing.

Choose Rytr if you need:

A simple AI writing assistant for everyday content.

Choose Elite Article Engine if you need:

A more complete article workflow combining research, web verification, SEO, external links, technical content, diagrams, charts and code.

AI Doesn't Replace Technical Review

There is one important point regardless of which tool you use.

Don't publish AI-generated technical content without checking it.

Technology changes quickly.

An article about React, Node.js, Docker, APIs or a JavaScript library can become inaccurate because of a version change.

A better workflow is:

AI Research
     ↓
AI Draft
     ↓
Human Review
     ↓
Fact Check
     ↓
Test Code
     ↓
Add Experience
     ↓
Publish

Google's guidance also focuses on helpful, reliable, people-first content rather than content created primarily to manipulate search rankings.

So AI should reduce the amount of manual work.

It shouldn't remove the human from the process.

Final Verdict

There is no single best AI article generator for everyone.

Jasper is a strong option for marketing teams.

Writesonic is a strong choice for SEO-focused content.

Copy.ai works well for marketing workflows.

Rytr is useful for simple AI writing.

And Elite Article Engine is worth considering if you want a workflow that combines article generation with research, verification, SEO and technical content elements.

The important thing is not how many words an AI tool can generate.

The important thing is whether those words provide real value to the reader.

For developers, that means accurate information, working examples, useful references and clear explanations should always come before word count.

Screenshot-to-Code Sounds Easy. Here Are 5 Things AI Still Gets Wrong

Arsalan Mlaik — Wed, 19 Aug 2026 07:57:55 +0000

Screenshot-to-code sounds simple.

Upload a screenshot → generate frontend code → done.

In practice, it is not quite that simple.

I've been experimenting with AI frontend generators, and the first generated result can look surprisingly close to the reference. But once you start treating that code like a real frontend project, you notice several things AI still doesn't understand particularly well.

Here are five of them.

1. Responsive Behavior

A screenshot represents one viewport.

Your application doesn't.

AI can look at a desktop screenshot and reproduce the layout, but it doesn't always know what should happen when the viewport becomes 390px wide.

For example, should three cards become:
[ Card ][ Card ][ Card ]
or:
[ Card ] [ Card ] [ Card ]
What happens to the sidebar?

Does the navigation collapse?

Should the heading size change?

Should the buttons become full width?

The screenshot doesn't provide those answers.

A good generated UI therefore still needs to be tested at different breakpoints:
320px 375px 768px 1024px 1440px
This is one of the first places where a developer needs to take over.

2. Component Architecture

AI can generate something that looks correct while producing a component structure that is difficult to maintain.

You might get one huge component containing hundreds of lines of JSX.

It works.

But if the same card, button, header or form appears somewhere else, you now have duplicated markup.

A real application might look more like:
Dashboard ├── Header ├── Sidebar ├── DashboardContent │ ├── StatsCard │ ├── Chart │ └── ActivityList └── Footer
The screenshot tells AI what the page looks like.

It doesn't tell AI how you want your application's component architecture to work.

That's still an engineering decision.

3. CSS That Looks Right but Isn't

This is probably one of the easiest ways to spot generated frontend code.

The AI wants to reproduce the screenshot, so sometimes it reaches for fixed positioning:
.element { position: absolute; top: 142px; left: 287px; }
It might look perfect at the original viewport.

Resize the browser and things start falling apart.

For production UI, the layout usually needs to be based on things like:

Flexbox
CSS Grid
gap
responsive units
containers
breakpoints
clamp()
framework utilities

The goal isn't to reproduce a screenshot at one exact resolution.

The goal is to build the layout system that could have produced that screenshot.

That's a much harder problem.

4. AI Can't See Application Logic

A screenshot can show a button.

It cannot tell you what the button does.

For example:
[ Add to Cart ]
The image doesn't tell the AI whether clicking it should:

call an API
update React state
open a modal
update a cart counter
redirect to checkout
show an error

The same problem happens with forms.

A screenshot can show:
Email [____________] Password [____________] [ Login ]
But it doesn't describe:

validation
authentication
loading states
API errors
disabled states
session handling

This is where screenshot-to-code ends and application development begins.

5. The Small Details Are Still Hard

The overall layout can be surprisingly accurate while the UI still feels "off."

Usually it's because of small details:

Font weight
Line height
Letter spacing
Border radius
Shadow intensity
Icon size
Button height
Image cropping
Padding
Element alignment

Each difference may be small.

Together, they make the generated interface noticeably different from the reference.

That's why I prefer an iterative workflow:
Screenshot ↓ Generate ↓ Preview ↓ Compare ↓ Refine ↓ Test
The first generation is the starting point, not the finished product.

So Are Screenshot-to-Code Tools Actually Useful?

Absolutely.

The mistake is expecting them to replace frontend development.

The real value is reducing the amount of repetitive work between:

"I have this UI design."

and

"I have a working frontend implementation."

I've been using tools such as Make Your UI for this type of workflow.

What I particularly like about the frontend-generator approach is that the generated code doesn't have to take over the existing project.

Generate the UI.

Preview it.

Modify it.

Then decide what actually belongs in your codebase.

That distinction matters when you're working on a real application.

I don't necessarily want an AI agent changing files throughout my existing project just because I asked it to recreate one component.

Sometimes I simply want:

"Give me this UI in React + Tailwind."

Then I'll decide where that component belongs.

AI Should Generate. Developers Should Decide.

For me, this is where screenshot-to-code becomes genuinely useful.

Not:

Screenshot → AI → Production

But:

Screenshot → AI → Code → Developer review → Refinement → Production

AI is very good at giving developers a starting point.

Developers are still responsible for architecture, responsiveness, accessibility, performance and application logic.

And honestly, that's probably the better workflow.

Let AI handle the repetitive part.

Keep the engineering decisions with the developer.

Have you tried screenshot-to-code tools?

I'm curious what your experience has been.

What's the biggest problem you've found in AI-generated frontend code — CSS, responsiveness, component architecture, or something else?

Best Frontend Generators in 2026: Top AI Tools for Developers

Arsalan Mlaik — Tue, 11 Aug 2026 19:31:00 +0000

AI frontend generators have changed how developers build web interfaces.

Instead of starting every page from an empty editor, developers can now describe a UI, provide a screenshot, or select a framework and generate frontend code in seconds.

But not every AI development tool is actually a frontend generator.

Some tools focus primarily on UI and frontend code, while others can generate complete applications with backend logic, databases, authentication and deployment.

This guide compares the best frontend generators in 2026 and explains which tool makes sense for different development workflows.

Best Frontend Generators at a Glance

Tool	Best For	Frontend Focus	Full-Stack
v0	React and Next.js UI	Excellent	Yes
Make Your UI	Multi-stack frontend generation	Excellent	No
Lovable	Full-stack MVPs	Excellent	Yes
Bolt.new	Browser-based development	Very Good	Yes
Replit	AI-assisted development	Good	Yes
Framer AI	Visual websites	Good	No

The main difference is workflow. Some tools are optimized for generating UI and frontend code, while others attempt to take an application from a prompt all the way to a working product.

1. v0 — Best for React and Next.js UI

v0 by Vercel is an AI development tool focused on generating interfaces and application code from natural-language prompts.

It can create UI from prompts, wireframes and visual references, making it particularly useful for developers working with React and Next.js.

Typical use cases include:

React components
Next.js pages
SaaS dashboards
Landing pages
Forms
Pricing pages
Responsive components
Rapid UI prototyping

The biggest advantage of v0 is its strong connection with the modern React and Next.js ecosystem.

For developers who already have an application architecture and need to build the frontend faster, this can be more useful than an AI tool that tries to rebuild the entire application.

Best for: React, Next.js and component-based frontend development.

2. Make Your UI — Best for Multi-Stack Frontend Generation

Make Your UI takes a more focused approach to AI frontend generation.

Instead of trying to build the entire application, the tool focuses on the frontend layer. Developers can generate UI from prompts and visual references and then work with the resulting frontend code.

One of its main advantages is support for multiple frontend stacks.

Depending on the project, developers can work with technologies such as:

React
Next.js
Vue
Angular
Svelte
Tailwind CSS
Bootstrap
MUI
shadcn/ui

This makes it useful for developers who work across different projects and do not always use the same framework or CSS system.

Screenshot-to-Code Workflow

Visual references can also be used as the starting point.

For example, instead of manually rebuilding a dashboard from a screenshot, developers can use the visual reference to generate the initial interface and then refine the resulting code.

Make Your UI supports image and Figma-based UI input, making it useful for design-to-code and screenshot-to-code workflows.

It can also be used for common frontend interfaces such as:

Dashboards
Login forms
Hero sections
Pricing tables
Product cards
Navigation bars
Chat interfaces
Settings pages

The generated interface can be previewed across different viewport sizes, allowing developers to check the UI on desktop, tablet and mobile layouts.

Why Use a Frontend-Only Generator?

This is where Make Your UI differs from broader AI app builders.

If you already have a backend, API, database and authentication system, you may not want an AI tool to generate those parts again.

You may simply need the frontend.

A dedicated frontend generator can therefore be a better fit when developers want to keep control over their existing application architecture while using AI to speed up repetitive UI implementation.

Best for: developers who want frontend code from prompts, screenshots or designs while retaining control over their existing stack.

3. Lovable — Best for Full-Stack MVPs

Lovable takes a broader approach than a dedicated frontend generator.

It is designed to turn natural-language descriptions into working web applications, allowing AI to handle a much larger portion of the development process.

This makes it useful for:

SaaS MVPs
Startup prototypes
Internal tools
CRUD applications
Full-stack experiments

The main advantage is that developers can start with an idea instead of an existing codebase.

However, developers who already have a backend and only need frontend code may prefer a more focused generator.

4. Bolt.new — Best for Browser-Based Development

Bolt.new combines AI code generation with a browser-based development environment.

Developers can describe an application, generate the project and continue working with the resulting code without setting up the complete development environment locally.

Bolt is designed for broader application development rather than only frontend generation.

It is useful for:

Full-stack prototypes
MVPs
Browser-based development
Quick experiments
Application demos

The main advantage is speed. Developers can move from an idea to a working application while reducing the amount of initial setup.

5. Replit — Best for AI-Assisted Full-Stack Development

Replit is closer to an AI development environment than a traditional frontend generator.

Its AI capabilities can help developers create applications from natural-language descriptions and then continue coding, testing and deploying within the same environment.

This makes it useful for:

Full-stack applications
Backend development
Rapid prototypes
AI-assisted coding
Browser-based development
Testing ideas quickly

Replit is particularly useful when developers want coding, testing and deployment in one platform.

6. Framer AI — Best for Visual Websites

Framer targets a different type of workflow.

Its AI features are primarily designed for creating and editing visually polished websites rather than generating application code for an existing developer codebase.

It is useful for:

Product landing pages
Startup websites
Portfolios
Marketing websites
Design-heavy pages

If the objective is a polished marketing website rather than a React or Next.js application interface, Framer can be a better choice.

Frontend Generator vs AI App Builder

The terms are often used interchangeably, but there is an important difference.

A frontend generator primarily helps create the user interface and frontend code.

An AI app builder attempts to generate a much larger portion of the application.

A simplified workflow looks like this:

Frontend generator

Prompt → UI → Components → Frontend code

AI app builder

Prompt → UI → Frontend → Backend → Database → Authentication → Deployment

This distinction matters when choosing a tool.

If you already have an API, database and backend architecture, generating only the frontend may be more efficient.

If you are starting with an idea and need a working application, a full-stack AI builder may save more time.

What Should Developers Look For in a Frontend Generator?

Generating an attractive interface is only the first step.

Developers should also evaluate the quality and usability of the generated code.

1. Framework Support

The generator should support the technologies used in your project.

React and Next.js are important for many modern applications, but developers may also need Vue, Angular, Svelte or traditional HTML/CSS.

2. Code Quality

Generated code should be readable and maintainable.

A visually impressive page is not very useful if the implementation contains unnecessary duplication or extremely large components.

3. Responsive Design

The generated interface should work across:

Desktop
Tablet
Mobile

Developers should test the actual implementation instead of relying only on a visual preview.

4. Component Structure

Reusable components become increasingly important as an application grows.

A generator that produces sensible component boundaries can save more development time than one that simply generates a complete page.

5. Existing Project Integration

Professional developers should consider how easily generated code can be moved into an existing project.

This is particularly important when the application already has authentication, APIs, databases or established coding conventions.

6. Visual Input

Screenshot-to-code and design-to-code workflows can reduce repetitive implementation work.

Instead of describing every layout detail manually, developers can start from an existing visual reference and refine the generated result.

7. Iteration

The first generation is rarely perfect.

A practical workflow is:

Prompt → Generate → Preview → Modify → Test → Refactor

The quality of this iteration loop is often more important than the first generated result.

Are AI-Generated Frontends Production Ready?

Not automatically.

AI can generate a functional interface quickly, but developers still need to review the implementation.

Important checks include:

Accessibility
Responsive behavior
Performance
Security
SEO
Error states
Loading states
Browser compatibility
Component architecture
API integration

A generated interface can look excellent while still requiring engineering work before it is suitable for production.

The same applies to AI-generated applications. As projects become larger, developers need to pay attention to maintainability, debugging and code architecture.

The better approach is therefore not:

AI builds everything.

It is:

AI handles repetitive implementation; the developer reviews and controls the code.

Which Frontend Generator Is Best?

There is no single winner for every project.

Choose v0 if:

You primarily work with React or Next.js and want AI-generated UI and code that fits into a modern web development workflow.

Choose Make Your UI if:

You want a dedicated frontend generator that supports multiple frontend stacks and can work from prompts or visual references while leaving your backend and application architecture under your control.

Choose Lovable if:

You want to move from an idea to a working full-stack MVP with AI handling a much larger portion of the application.

Choose Bolt.new if:

You want a browser-based AI development workflow for quickly creating and iterating on complete applications.

Choose Replit if:

You want an AI-assisted development environment where you can build, test and publish applications from one platform.

Choose Framer if:

Your main goal is a visually polished marketing website rather than application frontend code.

Final Verdict

The best frontend generator depends on how much of the development process you want AI to handle.

For React and Next.js UI, v0 is one of the strongest options.

For multi-stack frontend generation and screenshot-to-code workflows, Make Your UI is a useful choice for developers who want to generate frontend code while keeping control over their existing application architecture.

For complete AI-generated applications, Lovable and Bolt.new are better suited to the job.

For AI-assisted development and deployment, Replit provides a broader coding environment.

For design-focused websites, Framer is the better fit.

The important shift in 2026 is that developers no longer have to choose between writing every component manually and handing the entire application to an AI builder.

A third workflow is becoming increasingly practical:

Use AI to generate repetitive frontend work, keep ownership of the code, review the implementation, and continue development using the tools you already know.

For many developers, that is where frontend generators provide the most value.

We Built an AI Frontend Generator — Sharing It With the Dev Community

Arsalan Mlaik — Mon, 10 Aug 2026 16:44:26 +0000

We've been working on a tool called Make Your UI to speed up one of the repetitive parts of frontend development: turning a design idea into working code.

The basic workflow is:

Prompt / Screenshot → Generate → Preview → Refine → Fix → Export

You can start with a simple description of a UI or upload a screenshot. The tool generates the frontend and lets you see the result directly in the browser.

What we've built

Text-to-code generation
Screenshot-to-code
15+ JavaScript framework targets
20+ CSS/styling options
Live desktop, tablet and mobile preview
Refine the existing UI using natural-language instructions
Runtime error detection and Fix Error
Code explanation
Code export

The part we've found most useful is the iterative workflow. Instead of generating code, opening a separate project, finding an issue and going back to the AI, you can generate and refine the interface in the same place.

We're sharing it with the developer community because we'd genuinely like feedback on how well it handles real UI screenshots and whether this workflow actually saves time compared with existing approaches.

Try it:
Make Your UI

Dev Community Offer

We're offering our first 20 users 40% off the Basic Package while we gather early feedback.

Claim the 40% Developer Community Offer

If you try it, we'd particularly like to know where the generated UI is accurate and where it still needs improvement.

I Built an AI Frontend Generator — Make Your UI

Arsalan Mlaik — Fri, 07 Aug 2026 13:09:59 +0000

I’ve been working on an AI tool that can generate frontend UI from images or simple text descriptions.

You can:

🖼️ Image → Code — upload a UI screenshot and recreate it
✍️ Text → Code — describe the interface you want
✨ Refine UI — ask AI to change colors, spacing, layout, etc.
💡 Explain Code — understand generated code and individual sections
🧪 CodePen — test and experiment with the generated UI
🐛 Fix Errors — detect and fix frontend code issues
📱 Responsive Preview — test mobile, tablet and desktop layouts

The goal is to make the workflow:

Idea → UI → Code → Refine → Test → Ship

I’ve finished testing the core workflow and would really appreciate feedback from developers and frontend engineers.

👉 Try it here:
Make Your UI — AI Frontend Generator

Would love to know what you think and what features you’d want to see next.

We Built an AI Article Engine That Creates Complete, Web-Verified Content in One Click

Arsalan Mlaik — Mon, 29 Jun 2026 19:23:50 +0000

AI writers are everywhere, but most of them still generate generic text that requires hours of editing, fact-checking, formatting, and adding images before it is ready to publish.

That's why we built Elite Engine 2.

Our goal was simple: create an article engine that does more than just write paragraphs.

What Is Elite Engine 2?

Elite Engine 2 is an AI-powered content engine designed for bloggers, developers, SEO professionals, agencies, and content creators who want complete, publish-ready articles instead of rough drafts.

It combines writing, research, structure, and visual enhancements into a single workflow.

Key Features

🌐 Web-Verified Content

The engine uses external sources to help provide more accurate and up-to-date information.

🎯 Automatic Tone Selection

It automatically adapts the writing style based on the topic, removing the need for manual adjustments.

🌍 Auto Language Detection

The system understands multiple languages and generates content accordingly.

🖼 Relevant Images

Articles include contextually relevant images instead of random placeholders.

📊 Charts and Flow Diagrams

Complex topics become easier to understand with visual explanations.

💻 Code Playground

Technical articles can include code examples and interactive snippets.

🔗 Verified External Links

External references are checked to help avoid broken links and improve reliability.

🧠 Better Hinglish Understanding

Elite Engine 2 understands mixed-language prompts naturally, making it useful for multilingual creators.

🚀 Expert Mode

Advanced users can generate more detailed and technical content when required.

Why We Built It

Most AI tools save time on writing, but creators still spend hours:

Fact-checking information
Finding images
Adding references
Creating diagrams
Formatting articles
Improving readability

We wanted to simplify the entire content creation process and reduce repetitive work.

Who Is It For?

Bloggers
Content Creators
SEO Professionals
Affiliate Marketers
Developers
Agencies
Technical Writers
Students

Our Vision

We believe AI should help creators spend less time on repetitive tasks and more time focusing on ideas.

Elite Engine 2 was built to streamline content production, combining writing, research, visuals, and references into a single workflow.

If you'd like to see what we're building, check it out:

👉 https://makemychance.com/elite-engine2/

We're continuously improving the engine and would love to hear your feedback.

What Are AI Agents?

Arsalan Mlaik — Wed, 18 Feb 2026 11:27:18 +0000

AI agents are software systems designed to perceive their environment, reason about it, and take actions autonomously to achieve defined goals. Unlike traditional programs that execute…

The post What Are AI Agents? first appeared on Makemychance.

Modern CSS Isn’t What You Think Anymore — And That’s Why Tailwind Makes Sense Now

Arsalan Mlaik — Mon, 05 Jan 2026 18:08:33 +0000

Modern CSS Isn’t What You Think Anymore — And That’s Why Tailwind Makes Sense Now

Arsalan Mlaik ・ Jan 5

#frontend #webdesign

Modern CSS Isn’t What You Think Anymore — And That’s Why Tailwind Makes Sense Now

Arsalan Mlaik — Sat, 27 Dec 2025 13:37:16 +0000

If you still think CSS is just about selectors, properties, and fighting specificity, you’re already behind. Modern CSS has quietly evolved into something much more structural…

The post Modern CSS Isn’t What You Think Anymore — And That’s Why Tailwind Makes Sense Now first appeared on Makemychance.

CSS Gradient Generator

Arsalan Mlaik — Wed, 24 Dec 2025 14:03:34 +0000

This CSS Gradient Generator helps developers and designers create beautiful linear and radial gradients instantly using an easy visual interface. You can add multiple colors, change gradient direction, preview results in real time, and copy clean CSS code for direct use in websites, landing pages, and UI designs. Tools like this improve workflow efficiency, especially for front-end developers working with modern CSS layouts.
Check & Give feedback
CSS Gradient Generator

Is Your Chrome Browser at Risk? Critical Security Flaw You Must Fix Today

Arsalan Mlaik — Wed, 24 Dec 2025 11:20:14 +0000

If you use Google Chrome, it’s time to pay attention. A serious security flaw has been discovered in Chrome, and it could put millions of users…

The post Is Your Chrome Browser at Risk? Critical Security Flaw You Must Fix Today first appeared on Makemychance.

Azure Introduces New Networking Updates for Better Security and Reliability

Arsalan Mlaik — Sun, 07 Dec 2025 14:58:57 +0000

Published on Makemychance

Microsoft Azure has rolled out new networking improvements aimed at helping developers build more secure, stable, and highly available cloud applications.

The latest updates strengthen DDoS protection, improve traffic filtering, and offer deeper network monitoring, making it easier for developers and DevOps teams to identify issues faster. Azure’s upgraded load balancing and automatic failover features also reduce downtime during heavy traffic or service interruptions.

For high-availability workloads, Azure now provides improved zone redundancy, smarter cross-region routing, and more efficient connection management. These changes support developers working on distributed systems, microservices, and global applications.

For general information, users can check the Azure homepage (https://azure.microsoft.com) or Microsoft’s website (https://www.microsoft.com).