Arbitrum vs Polygon

Karolina

21 Sep 2023
Arbitrum vs Polygon

As the world of blockchain technology quickly progresses, the issue of scalability is among the greatest hurdles yet. With the expansion and popularity of decentralized applications (dApps) and smart contracts, finding effective and economical solutions is crucial. Two leading contenders tackling these challenges are Arbitrum and Polygon, each offering distinct Layer 2 scaling solutions and features. This article delves into the complexities of "Arbitrum vs Polygon" to help readers make well-informed choices for their blockchain requirements.

Understanding Layer 2 Scaling

Blockchain technology fundamentally consists of interconnected blocks which create a transparent and unchangeable ledger. However, as this technology gained widespread adoption, particularly through Ethereum's smart contracts and dApps, it was evident that Layer 1—the base layer—had limitations concerning transaction throughput and speed.

Often referred to as "L2," Layer 2 scaling presents a suite of solutions constructed atop existing Layer 1 blockchains. These solutions do not intend to supplant the main chain but rather augment its capabilities. By relocating certain operations from the main chain (such as calculations or storage) and subsequently consolidating them back, L2 solutions can significantly boost transactions per second, minimize fees, and enhance overall user experience.

Numerous L2 techniques exist, including state channels, Plasma, sidechains, and rollups. Each provides a unique scalability approach accompanied by individual advantages and trade-offs. Arbitrum and Polygon have risen as top solutions among these techniques, attracting both developers and investors.

Arbitrum

Read our post 'What is Arbitrum?'

Developed by Offchain Labs, Arbitrum is a Layer 2 scaling solution employing Optimistic Rollups that enhance the efficacy of Ethereum-based applications. Here's an in-depth examination:

Origin and Development

Arbitrum was devised to tackle Ethereum's scalability issues without jeopardizing security. The developers at Offchain Labs concentrated on crafting a user-friendly solution that lowers fees and expedites transactions for users.

Technical Insights

Arbitrum's central technology is Optimistic Rollup. Instead of performing each transaction on the main Ethereum chain, Arbitrum conducts the majority of transactions off-chain. These are periodically amalgamated into a single group and sent to Ethereum, significantly reducing data stored on the main chain and improving efficiency.

The "optimistic" aspect relates to how transaction validity disputes are managed. Arbitrum presumes transactions are valid unless contested, circumventing validation for each transaction. An on-chain mechanism is in place to resolve any disputes that may surface.

Adoption and Use Cases

Numerous applications and projects have started integrating with Arbitrum due to its advantages. Its ability to preserve Ethereum's security guarantees while bolstering its capabilities makes it an attractive option for many in the industry.

Polygon

Previously known as Matic Network, Polygon has emerged as a leading multi-chain scaling solution for blockchain networks compatible with Ethereum. It strives to offer a means for building a more interconnected and scalable decentralized web.

Foundations and Evolution

Initially introduced as Matic Network, Polygon evolved into a broader multi-chain ecosystem to tackle Ethereum's scalability problems and functionality challenges. Polygon's creators saw the need for a Layer 2 solution that surpassed a singular method and chose to develop a more inclusive framework.

Technical Perspectives

Employing a mixed Proof-of-Stake (PoS) and Plasma framework, Polygon enables quicker, low-cost transactions. Its sidechains function concurrently with the Ethereum main chain, easing transaction volume and facilitating faster confirmations.

Moreover, Polygon's architecture is crafted to accommodate multiple Layer 2 solutions, guaranteeing adaptability and extensive applicability for diverse use cases.

Implementation and Use Cases

With its powerful scaling solution and conformity with Ethereum's tools and infrastructure, Polygon has drawn an extensive variety of decentralized projects – ranging from DeFi platforms to game applications and NFT marketplaces. Its versatility has made it popular among those seeking scalability without sacrificing security or decentralization.

Arbitrum vs. Polygon

To compare Arbitrum and Polygon accurately, it is vital to comprehend the subtleties that set these Layer 2 powerhouses apart.

Technical Approaches and Mechanisms

Arbitrum utilizes Optimistic Rollups to primarily bundle multiple off-chain transactions before submitting them onto the chain as one unit. Transactions are assumed valid by default, with on-chain verification needed only if conflicts arise.

In contrast, Polygon implements sidechains that function in tandem with Ethereum's main chain employing a combination of PoS and Plasma to enable speedy transactions at reduced cost. Its structure is created to incorporate numerous Layer 2 solutions, supplying a wider array of tools and techniques.

Adoption and Ecosystem

Despite both platforms witnessing significant adoption, they serve slightly diverse users. Arbitrum attracts projects seeking uncomplicated integration while maintaining a strong connection with Ethereum, primarily reaping the benefits of reduced fees and heightened throughput.

On the other hand, Polygon delivers a more all-encompassing multi-chain environment, appealing to projects that seek an extensive toolset, increased flexibility, and a broad vision of interconnected chains.

Expenses and Fees

Both solutions strive to substantially lower Ethereum transaction costs. That said, specific fee structures may differ based on transaction volume, network congestion, and other factors. Generally, both Arbitrum and Polygon offer substantially lower transaction fees compared to Ethereum Layer 1.

Compatibility and Interoperability

Both Arbitrum and Polygon emphasize Ethereum compatibility, assuring projects can effortlessly migrate or incorporate without significant restructuring. However, Polygon's wider objective of crafting an interconnected multi-chain ecosystem presents a unique aspect of interoperability, aiming to merge various Layer 2 solutions into an integrated network.

Conclusion

Choosing between these solutions is not a matter of which is objectively superior, but rather which aligns more closely with a project's unique needs and goals. Some may favor Arbitrum's streamlined integration with Ethereum, while others might lean towards Polygon's expansive toolkit and vision.

Tagi

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

Follow us to stay updated!

AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series