Arbitrum vs Optimism

Karolina

29 Sep 2023
Arbitrum vs Optimism

In the extensive and continuously growing realm of blockchain and cryptocurrency, the search for scalable Ethereum solutions has become a crucial battlefront. As Ethereum shifts towards proof-of-stake, layer-2 scaling solution contenders like Arbitrum and Optimism have gained prominence. The launch of the ARB token has further fueled the competition, enticing crypto aficionados and professionals to examine and assess these two platforms' technical capabilities. While they vie for the esteemed title of 'King of Ethereum Scaling,' it leads to the question: Is there a clear frontrunner between them? This article aims to shed light on this urgent query by exploring the structure, ecosystems, and potential trajectories of both Arbitrum and Optimism.

READ ALSO: Arbitrum vs Polygon

Arbitrum vs Optimism: Architectural Differences

As Ethereum strives for enhanced scalability, layer-2 chains such as Arbitrum and Optimism have emerged as prominent solutions. Utilizing "optimistic rollups," these chains offer an innovative approach by condensing several transactions into one, effectively moving traffic away from the congested Ethereum mainnet to a swifter, more efficient second layer. This brilliant technique significantly reduces the burden on the primary layer, leading to faster transactions and more affordable fees.

Optimistic rollups rely on a trust-based principle, optimistically presuming that collected transaction data in batches are valid when verified on the main Ethereum chain. "Fraud proofs" provide a strong safety measure, inspecting and confirming data legitimacy. This trustless system enables instant transaction finality and substantial throughput improvements compared to traditional Ethereum setups. However, there is a compromise – a seven-day challenge window exists to question transaction validity. Furthermore, these systems utilize a 'sequencer' to order transactions prior to their integration into the main chain.

Despite appearing similar at first glance, Arbitrum and Optimism possess subtle architectural distinctions. Fundamentally, Optimism implements single-round fraud proofs for expediency but incurs higher gas fees due to transactions occurring on the core Ethereum layer. Conversely, Arbitrum adopts a more economical method using multi-round fraud proofs, which take longer but are less expensive.

Distinct choices in virtual machines are another differentiating factor. While Optimism solely relies on the Ethereum Virtual Machine (EVM) and confines its programming paradigm to Solidity, Arbitrum expands its possibilities with the custom Arbitrum Virtual Machine (AVM), supporting all EVM-compatible languages for increased adaptability.

Ultimately, these minor technical variations between Arbitrum and Optimism can result in significantly different user experiences as each platform addresses specific needs and preferences.

Arbitrum vs Optimism: Analyzing the Ecosystem

A thorough analysis of both Arbitrum's and Optimism's ecosystems in the struggle for supremacy among Layer-2 scaling solutions reveals intriguing findings. Both platforms are built on optimistic rollups, and their growth metrics tell a fascinating tale.

A distinct lead is evident for Arbitrum when looking at the Total Value Locked (TVL). Data indicates that Arbitrum's TVL surpasses Optimism's by over twice as much, representing a substantial difference in the DeFi realm. Delving into the distribution of TVL, this difference becomes even more marked. Derivatives and decentralized exchanges (Dexes) contribute significantly to Arbitrum's diversified portfolio, whereas Dexes account for a considerable proportion of Optimism's TVL.

Exploring native versus non-native protocols further highlights the dominance of Arbitrum. A large portion of its TVL comes from native protocols, while Optimism falls behind in this regard. It is essential to recognize GMX's substantial impact on Arbitrum, contributing significantly to its TVL.

Arbitrum maintains its superior position from an activity perspective, boasting higher protocol numbers, increased daily active addresses, and improved transaction rates. The recent airdrop amplified their dominance; however, it remains critical to observe whether these metrics will stabilize or change after the initial excitement surrounding the airdrop.

Arbitrum vs Optimism: Upcoming Catalysts

Even though current information seems to favor Arbitrum, both platforms' future plans present exciting prospects capable of reshaping the L2 space.

Optimism spearheads an innovative project with OP Stack: an ambitious plan aiming to create a scalable, modular, and interoperable blockchain system. Their forthcoming product, Optimism Bedrock, is expected to incorporate modularity and usher in improved performance statistics. Their collaboration with Coinbase could be their most significant advantage in forming a "Superchain": a seamless network built on top of the OP Stack to enhance inter-chain connectivity and functions.

Arbitrum, on the other hand, does not intend to rest on its achievements. The introduction of the ARB token drop not only supports greater decentralization but also aims to entice a larger user base. Other noteworthy initiatives include Arbitrum Nova, which intends to lower gas fees dramatically, favoring applications requiring high bandwidth. Their prioritization shifts to developer access, with projects like Arbitrum Orbit and the Stylus upgrade meant to draw in a wider developer audience.

To understand this topic better, we recommend you watching this video:

https://www.youtube.com/watch?v=WsHH_JGK390

Conclusion

Which platform emerges as the victor in this struggle of optimistic rollup titans?

Arbitrum currently claims the crown, with its remarkable TVL, heightened user engagement, and notable transaction volume amplified by the ARB airdrop. Nonetheless, the dynamic nature of the crypto world is notorious for swift changes and surprising turns – and with a promising roadmap and partnership with Coinbase, Optimism might well become a powerful competitor.

Both L2 platforms offer immense potential as they continue to develop, further shaping the story of this captivating rivalry.

READ ALSO: 'Arbitrum to Polygon Bridge'

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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!