Exploring Layer 2 Scaling Solutions: Lightning Network, Plasma, and Rollups

Karolina

31 May 2023
Exploring Layer 2 Scaling Solutions: Lightning Network, Plasma, and Rollups

As the blockchain ecosystem continues to evolve, one of the key challenges it must overcome is scalability. The underlying technology of the blockchain is capable of disrupting various industries, but its potential is often bottlenecked by scalability issues. Layer 2 scaling solutions have emerged as a promising approach to overcome these challenges. In this section, we will delve into the specifics of three main Layer 2 scaling solutions: Lightning Network, Plasma, and Rollups.

Lightning Network

The Lightning Network is a Layer 2 payment protocol that operates on top of a blockchain-based cryptocurrency like Bitcoin. Its main goal is to enable fast, low-cost transactions between participating nodes.

How Lightning Network works:

The Lightning Network relies on off-chain state channels. A state channel is essentially a private two-way route opened between two parties. These parties can conduct an unlimited number of transactions between themselves, off the main blockchain. Only when the channel is closed does the final state of these transactions get recorded on the blockchain. This approach significantly reduces the load on the blockchain, allowing for faster and cheaper transactions.

Use cases and benefits of Lightning Network:

Quick micro-transactions: The Lightning Network allows for instant, high-volume transactions, making it suitable for micro-transactions and instant payments.

Lower fees: Since transactions occur off-chain, the cost associated with transactions is significantly reduced.

Challenges and limitations of Lightning Network:

While the Lightning Network does offer compelling benefits, there are also challenges associated with its use. These include complexity of use, the requirement for nodes to be online for transactions, and potential privacy issues.

Plasma

Plasma is a Layer 2 scaling solution proposed for the Ethereum blockchain. It aims to enable the processing of smart contracts on a large scale by creating off-chain channels.

How Plasma works:

Plasma works by creating a series of child chains (smaller blockchains) that branch off from the main Ethereum blockchain. These child chains can handle a significant amount of computational work that would otherwise slow down the main chain.

Use cases and benefits of Plasma:

High throughput: Plasma can handle a large number of transactions per second, which is critical for applications requiring high throughput.

Scalable smart contracts: By handling smart contracts off the main chain, Plasma enables scalable decentralized applications (dApps) on Ethereum.

Challenges and limitations of Plasma:

Just like the Lightning Network, Plasma also faces several challenges. These include the complexity of the Plasma architecture, difficulty in handling mass exits from child chains, and the fact that it's still largely theoretical and not widely adopted.

Rollups

Rollups are another Layer 2 solution primarily designed for the Ethereum network. They boost the network's capacity by rolling multiple transactions into a single transaction on the blockchain.

How Rollups work:

There are two main types of Rollups: zk-Rollups and Optimistic Rollups. Both types essentially bundle or "roll up" multiple transactions into one, but they use different methods for verifying the validity of transactions.

Use cases and benefits of Rollups:

Greater scalability: Rollups can significantly increase the transaction throughput of the Ethereum network.

Lower fees: By bundling multiple transactions, Rollups can reduce the cost per transaction.

Challenges and limitations of Rollups:

As with any technology, Rollups come with their own set of challenges. These include the complexity of the technology, the reliance on relayers to bundle transactions, and potential centralization risks.

Each of these Layer 2 solutions offers unique approaches to solving the scalability issue, and they each come with their own set of trade-offs. Understanding these technologies is crucial as we continue to innovate and improve upon the existing blockchain infrastructure.

Comparing Layer 2 Scaling Solutions

As we have explored, each Layer 2 scaling solution - Lightning Network, Plasma, and Rollups - offers unique benefits and faces distinct challenges. These solutions aren't one-size-fits-all; their effectiveness can vary greatly depending on the specific requirements and constraints of the blockchain network they are applied to. In this section, we will compare these solutions on various key aspects such as speed, security, complexity, and current adoption rates.

Speed

Lightning Network: The Lightning Network provides instant, high-volume transactions, making it extremely fast for applicable use cases, particularly micro-transactions and instant payments.

Plasma: Plasma can handle a large number of transactions per second by offloading the computational work to child chains, providing high throughput for applications.

Rollups: Rollups, both zk-Rollups and Optimistic Rollups, can significantly increase the transaction throughput of the Ethereum network by bundling multiple transactions into one.

Security

Lightning Network: The security of the Lightning Network relies on the security of the underlying blockchain. However, issues can arise if nodes aren't online, and potential privacy issues exist.

Plasma: Plasma inherits the security of the main Ethereum chain. However, it faces potential issues in the event of mass exits from child chains.

Rollups: Rollups also inherit the security of the underlying Ethereum blockchain. zk-Rollups provide more immediate finality and security, while Optimistic Rollups rely on a challenge period for transaction validation.

Complexity

Lightning Network: The Lightning Network requires a good understanding of channel management and liquidity provision, which adds to its complexity.

Plasma: The architecture of Plasma is complex, involving the management of multiple child chains branching off from the main chain.

Rollups: Rollups, especially zk-Rollups, involve complex cryptographic proofs, making them complex to understand and implement.

Adoption

Lightning Network: The Lightning Network has seen significant adoption, especially in the Bitcoin ecosystem, for micro-transactions and instant payments.

Plasma: Plasma, while promising, is still largely theoretical and has seen limited adoption due to its complexity and the challenges it faces.

Rollups: Rollups are gaining traction in the Ethereum community. Notably, the Ethereum 2.0 upgrade roadmap includes the use of Rollups for scalability.

In conclusion, each Layer 2 solution has its own strengths and weaknesses. The choice between Lightning Network, Plasma, and Rollups depends on various factors such as the specific use case, the underlying blockchain, and the trade-offs that are acceptable for the desired application. As the blockchain space continues to evolve, we can expect these solutions to mature and new ones to emerge, providing ever more efficient ways to scale blockchain networks.

The Future of Layer 2 Scaling Solutions

The blockchain ecosystem continues to evolve at a rapid pace. As we've seen, Layer 2 scaling solutions are instrumental in helping the technology overcome its inherent limitations and reach its full potential. As we look ahead, several key trends emerge that hint at the future direction of Layer 2 technologies.

Continued Innovation and Development

As with any emerging technology, we can expect continued innovation and development in the field of Layer 2 solutions. This could mean the refinement of existing technologies, such as Lightning Network, Plasma, and Rollups, but it could also mean the introduction of entirely new solutions as developers identify new approaches and techniques.

Widespread Adoption and Use

Currently, Layer 2 solutions are being adopted and implemented across a number of blockchain networks. As the benefits of these solutions become more widely recognized and understood, we can expect to see an increase in their adoption. This could mean more businesses and users utilizing these solutions, leading to an overall increase in the efficiency and scalability of blockchain networks.

Integration with Layer 1

Layer 2 solutions will likely become increasingly integrated with Layer 1, the underlying blockchain protocol. This could mean a closer integration between the two layers, allowing for smoother and more efficient transactions. In fact, in the Ethereum community, there is already talk of "Layer 1.5" solutions that blend elements of both layers.

Increased Interoperability

As more Layer 2 solutions are developed, there will be a need for increased interoperability between them. This could mean the development of protocols or standards that allow for different Layer 2 solutions to interact and work together, providing users with more flexibility and choice.

Regulatory Challenges

As Layer 2 solutions become more widespread, they will likely face increased scrutiny and regulation. This could pose challenges for the development and adoption of these solutions, but it could also lead to greater transparency and trust in the technology.

You want to read more? Read this article!

Conclusion

In conclusion, the future of Layer 2 scaling solutions is exciting and full of potential. While challenges remain, the ongoing development and increasing adoption of these technologies are a positive sign for the future of blockchain technology. As we continue to innovate and push the boundaries of what's possible, Layer 2 solutions will undoubtedly play a critical role in the evolution of the blockchain ecosystem.

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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!