What is Arbitrum?

Karolina

19 Sep 2023
What is Arbitrum?

As the blockchain technology landscape continues to expand and evolve, two major challenges remain prominent, particularly within the Ethereum network: scalability and transaction cost. In response to these issues, we find Arbitrum as a promising solution. So, what is Arbitrum?

Arbitrum is a Layer 2 scaling solution designed exclusively for the Ethereum network. Its core function involves processing the majority of transactions off the primary Ethereum chain (off-chain) and submitting a summarized version, or 'rollup,' of these transactions to the main chain. This approach significantly alleviates the burden on the main Ethereum chain, leading to faster transaction times and considerably reduced gas fees.

Analysis of Arbitrum

In today's dynamic blockchain environment, continuous development and growth are imperative. As platforms like Ethereum become increasingly popular, scalability emerges as a considerable challenge. This is where Arbitrum comes into play - a Layer 2 scaling solution aimed at addressing many of the limitations Ethereum currently experiences. So, what is Arbitrum, and why is it garnering such attention within the blockchain sphere?

The Origin Story

Arbitrum, created by Offchain Labs, emerged due to the rising need for a more efficient transaction process on the Ethereum blockchain. As user adoption and decentralized applications on Ethereum began to surge, it became evident that the existing network structure could not efficiently manage high volumes without exorbitant transaction fees or delayed transaction times.

Fundamental Idea and Methodology

At its foundation, Arbitrum employs something referred to as "Optimistic Rollups." What does this entail? Generally, rollups involve consolidating or "rolling up" numerous transactions into one which gets recorded on the main chain. This translates to less on-chain data, leading to faster and more affordable transactions.

The "Optimistic" component of Optimistic Rollups stems from its mechanism. Rather than verifying every individual transaction (a burdensome and time-consuming effort), Optimistic Rollups operate based on trust by presuming each transaction is legitimate. There's a catch though - if any transaction is discovered to be invalid, mechanisms exist to penalize those involved. This approach effectively maintains a balance between trust and validation while enabling faster transaction times without sacrificing security.

Arbitrum's Enhancement of Ethereum

Ethereum boasts a strong and groundbreaking foundation; however, its shortcomings in scalability are apparent. This is where Arbitrum steps in. By processing the bulk of transactions off-chain and only submitting crucial data to Ethereum's main chain, it substantially eases the burden on Ethereum in the following ways:

  • Faster Transactions: No more lengthy waits for transaction confirmations.
  • Lower Fees: Reduced on-chain data processing leads to substantially lower transaction costs.
  • Improved Scalability: this layer 2 solution can accommodate a greater volume of transactions simultaneously, making it suitable for extensive dApps and platforms.

Essentially, Arbitrum serves as a connection point, maximizing Ethereum's advantages while concurrently offering solutions to its limitations. As the cryptocurrency community progresses and expands, innovative technologies like Arbitrum will take center stage in shaping the decentralized landscape of the future.

Features and Advantages of Arbitrum

Promising Layer 2 solution introduces a suite of features that cater to the prevailing issues of blockchain scalability and cost. Here’s a closer look at its main features and inherent advantages:

Enhanced Scalability

Higher Transaction Throughput: this layer 2 solution can process a multitude of transactions simultaneously, considerably enhancing the speed of operations.

Parallel Execution: With the ability to handle multiple transactions in tandem, Arbitrum reduces the backlog that's often witnessed on Ethereum’s main chain.

Cost Efficiency

Lower Gas Fees: Transactions on it are processed off-chain, resulting in significantly reduced gas fees on Ethereum.

Optimized Data Storage: With only essential data being recorded on the main chain, Arbitrum optimizes storage and, consequently, costs.

Compatibility

Seamless Ethereum Integration: Arbitrum is designed to be fully compatible with Ethereum's smart contracts, requiring little to no changes for developers to migrate their dApps.

Interoperable Tooling: Developers can employ familiar Ethereum tools and frameworks when working with Arbitrum.

Security Measures

Secure Consensus Mechanism: Leveraging Ethereum's security, Arbitrum benefits from the same trust and decentralization.

Fraud Proofs: The Optimistic Rollup design ensures that any fraudulent activity can be quickly detected and penalized.

Potential Use Cases for Arbitrum

Arbitrum’s unique feature set positions it as a sought-after Layer 2 solution for various applications.

Decentralized Finance (DeFi)

High-frequency Trading: With reduced transaction costs and faster speeds, Arbitrum can enable efficient high-frequency trading platforms in the DeFi space.

Yield Farming: Users and protocols can achieve better operational efficiency, making yield farming strategies more effective and lucrative.

Gaming

Real-time Gameplay: it can facilitate real-time, on-chain gaming experiences.

In-game Asset Trading: Speedier and cheaper transactions could revolutionize how in-game assets are traded and monetized.

NFT Marketplaces

Cost-efficient Trades: Reduced transaction fees can potentially lower the barriers for trading NFTs, encouraging a more vibrant marketplace.

Fast Auctions: Quicker transaction times can facilitate real-time bidding wars and instantaneous auction results.

The Future of Arbitrum

Recent Developments

Strategic Partnerships: Many projects and platforms are beginning to integrate to leverage its advantages. Highlighting some key partnerships can showcase its growing influence.

Tech Upgrades: As with any technology, this layer 2 solution continues to evolve. Future updates might introduce even more optimizations and features.

Expected Growth and Adoption

Mainstreaming Layer 2: As more entities recognize the importance of Layer 2 solutions, Arbitrum's adoption is poised to grow exponentially.

Potential Beyond Ethereum: While currently focused on Ethereum, the technology behind this layer 2 solution has the potential to be adapted for other blockchains, broadening its horizons and influence.

As the blockchain ecosystem continues its march towards mainstream adoption, solutions like Arbitrum will be pivotal in addressing the challenges of today and shaping the decentralized platforms of tomorrow.

Conclusion - What is Arbitrum?

Arbitrum's introduction into the blockchain domain stands as a testament to the industry's drive towards innovation and optimization. As Ethereum continues to serve as a foundational layer for countless decentralized applications, the need for solutions like Arbitrum becomes ever more apparent. With its ability to drastically improve transaction speeds while concurrently slashing costs, Arbitrum not only addresses some of Ethereum's current limitations but also paves the way for a more scalable and cost-effective decentralized future. As we continue to push the boundaries of what's possible in the blockchain sphere, tools like Arbitrum will undeniably play a central role in shaping that journey.

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!