Web3 Bridges on Arbitrum

Karolina

27 Sep 2023
Web3 Bridges on Arbitrum

The relentless evolution of blockchain technology never ceases to amaze. Over the past few years, the industry has shifted its focus from simply understanding what decentralized ledgers are, to scaling and interoperability solutions that pave the way for mass adoption. Among these innovations, Web3 and Layer 2 scaling solutions stand out, promising to redefine how we perceive and interact with decentralized applications. Today, we'll be diving deep into one of the most groundbreaking topics in the realm of Ethereum scalability: Web3 Bridges on Arbitrum.

Arbitrum, a Layer 2 scaling solution, not only offers faster transactions and reduced fees but also a platform for projects to effortlessly interact with the broader Ethereum ecosystem. And this is where Web3 bridges come into play. Acting as connectors between different blockchain realms, these bridges ensure that value and data flow seamlessly, securely, and transparently across networks.

Whether you're a blockchain enthusiast, a developer eager to understand the underpinnings of Arbitrum, or someone merely curious about the next wave of decentralized tech, this post promises to enlighten. We'll demystify the intricate workings of Web3 bridges, shed light on their importance in the Arbitrum ecosystem, and, ultimately, help you grasp how they're pushing the boundaries of what's possible in the blockchain world.

So strap in and get ready for a journey through the sophisticated architecture and promising potential of Web3 Bridges on Arbitrum!

LayerZero

Chains: Ethereum, BNB Chain, Avalanche, Polygon, Arbitrum, Optimism

LayerZero is a protocol that facilitates interaction between separate blockchains and ecosystems. It does so with its underlying oracle and relayer that allows for cross-chain messaging and verification. LayerZero supports a wide array of blockchains including Aptos, Avalanche, Polygon, and Harmony.

Connext

Chains: Ethereum, Polygon, Avalanche, BNB Chain, Arbitrum, Optimism

The ethos of Connext is to herald a time when the end users would not even know the actual chain they are using, as it aims to strike a high-level composability in the blockchain industry. Connext has a decentralized network of routers that earn as they facilitate the activities of the bridge and secure it at the same time.

Router Protocol

Chains: Arbitrum, Ethereum, Polygon, Optimism, BNB Chain

Router Protocol is a cross-chain interoperability layer connecting different blockchains. Currently, Router Protocol's infrastructure is live on nine different EVM chains: Ethereum, BSC, Fantom, Polygon, Optimism, Arbitrum, Avalanche, Aurora, and Kava. Router Protocol's current offerings are: (1.) Flagship DApp: Voyager provides any asset to any asset cross-chain swap; and (2.) CrossTalk: Creates a communication layer for cross-chain smart contracts and enables use cases such as cross-chain lending, staking, etc. For more information, visit our docs!

deBridge

Chains: Arbitrum, BNB Chain, Avalanche, Ethereum, Polygon, Multichain

deBridge is a generic messaging and cross-chain interoperability protocol that enables anyone to build powerful cross-chain applications where value and messages flow seamlessly. The protocol is a secure infrastructure and framework for: (1.) cross-chain composability of smart contracts; (2.) cross-chain swaps between any assets with deSwap, one of the applications built on top of deBridge; (3.) bridging of any arbitrary asset and message in one transaction; (4.) interoperability and bridging of NFTs

Across

Chains: Ethereum, Arbitrum, Polygon, Optimism

Transferring native tokens across several native blockchains in the Ethereum ecosystem gets easier with the surfacing of Across. Across allows users to tap into a rich network of relayers in moving funds across two distinct blockchains. Once the relayers approve the bridging, Across itself has an in-built optimistic validation.

LayerSwap

Chains: BNB Chain, Optimism, Starknet, Arbitrum

LayerSwap is a unique bridge that can move funds from centralized exchanges to layer-2 blockchains. For instance, a user can move funds from FTX to Arbitrum mainnet.  Layer swap has an intuitive interface that is simple to use for swapping, and it makes cross-chain transactions easier, lessening transaction fees by almost 10%.

Conclusion

Diving deep into Web3 bridges on Arbitrum has underlined a vital truth: blockchain's future is as much about interconnection as it is about decentralization. Through platforms like Arbitrum, we're witnessing the dissolution of silos, making way for faster, smoother, and more cohesive decentralized applications. These bridges are more than just technological marvels—they are the linchpins of an evolving blockchain landscape. As we move forward, embracing such innovations will not only be beneficial but essential. Here's to a united, decentralized future, bridged by the power of technology.

Tagi

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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

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!