Ethereum 2.0 – What does the release mean for your application?

Maciej Zieliński

18 Jan 2021
Ethereum 2.0 – What does the release mean for your application?

Ethereum 2.0, also known as Serenity is a long-awaited update to the Ethereum network, significantly improving the security and scalability of arguably the world's most popular Blockchain protocol. Above all, it will reduce power consumption and enable the network to process more transactions. The most important improvements from the technical side are to be the transformation of Ethereum into a proof-of-stake blockchain and the introduction of fragmented chains.  

Note, however, that this is a change to the Ethereum infrastructure only. Dapp users or developers and ETH holders can rest assured. Ethereum 2.0 will be fully compatible with the Ethereum 1.0 network they use today. On the other hand, they will also be able to use the ETH they own after the update. 

So why are these changes so important? On the Nextrope blog, we will try to cover everything you should know about Ethereum 2.0. 

Source: ethereum.org

Current restrictions

Released in 2015, Ethereum has quickly become the most widely used blockchain protocol (learn what blockchain protocols are and what distinguishes them from each other here). The open public system has enabled previously unseen software applications and generated billions of dollars in value. However, to realize its full potential, Ethereum still has to deal with a few limitations. 

Speed and efficiency:

Currently, Ethereum is capable of handling around 15 transactions per second. Compared to Visa or Mastercard, which are able to process up to 1,500 of them at the same time, it therefore comes off rather poorly. In addition, the process of "mining" ETH, on which verification of these transactions is based, consumes too much energy, which limits the scalability of the entire network. 

What does ETH 'mining' consist of?

Mining is the process of creating a block of transactions to be added to the Ethereum blockchain (hence blockchain). Each block contains transaction information and data such as the Hash - the unique code of the block and the hash of the previous block to which the block hash is compatible. 

Essentially, the miners' role is to process pending transactions in exchange for rewards in the form of ETH, Ethereum's native currency (2 ETH for each block generated, respectively). Generating a block requires the use of a lot of computing power, due to the difficulty level set by the Ethereum protocol. The difficulty level is proportional to the total amount of computing power used to mine Ethereum and serves as a way to protect the network from attacks, as well as to tune the rate at which subsequent blocks are created. This system of using computing power to secure and verify data is known as Proof of Work (PoW).

To maintain the security of the current Ethereum network, therefore, the high energy intensity of the mining process is necessary - making the cost of attacking the network, making any change to any of the already existing blocks, extremely high.

The problem of retaining decentralisation when scaling up 

There are, of course, Blockchain protocols such as Hyperledger Fabric or Quorumthat allow for more transactions per second. However, the higher performance in their case comes from being more centralised than Ethereum. By design, Ethereum is intended to remain a fully decentralised network, so such a solution in this case is not an option. It seems Ethereum 2.0 developers have found a way to improve performance and enable scaling without sacrificing decentralisation. 

What's new in Ethereum 2.0?

Fragmented chains (or chains of fragments) 

At the moment, all nodes in the Ethereum network have to download, read, analyse and store every previous transaction before they process a new one. Not surprisingly, Ethereum is currently unable to process more than the aforementioned 15 transactions per second. 

Ethereum 2.0 introduces fragmented chains, which are parallel blockchains that take over a fair share of the network's processing work. They allow nodes to be dispersed into subsets corresponding to fragments of the network. This ensures that each node does not have to process and store transactions from the entire network, but only those in its subset. 

Proof-of-stake in Ethereum 2.0

In Ethereum 2.0, Proof-of-Work is to be replaced by Proof-of-stake. Network security will be achieved through financial commitments rather than computing power - energy consumption. Proof-of-stake is a consensus process where ETH becomes the validator for Ethereum. The validator runs software that confirms the transaction and adds new blocks to the chain. To become a full validator, 32 ETH will be needed. However, there will be an opportunity to join a pool of smaller validators and thus offer a smaller stake. When processing transactions, validators will take care to maintain consensus over the data and thus the security of the entire network.

Proof-of-stake will drastically reduce the energy intensity of the entire network, which is a key step towards further scaling Ethereum and increasing its environmental friendliness. 

Beacon chain 

A decisive role in introducing proof of stake into Ethereum is played by the Beacon Chain, which, in simple terms, can be described as the layer that coordinates the operation of the entire system. However, unlike the core network (meinnet) present in Ethereum, it does not support accounts or smart contracts. Instead, its main task is to implement proof-of-stake protocol management for all fragmented chains (shards). It was the connection of the Beacon Chain to Ethereum that was the first step towards version 2.0 ( phase 0).

Ethereum 2.0, what will 2021 bring?

The introduction of Ethereum 2.0 developers will divide into 3 stages - phases: Phase 0, 1 and 2. In December 2020, the first one, which started in 2018, was completed. As we mentioned its main goal was to launch the Beacon chain. The success of Phase 0 will allow the start of Phase 1 in 2021 - the shard chain deployment, which will start the full-fledged transition to the Proof-of-stake protocol. The full upgrade to Ethereum 2.0 will be enabled by Phase 2 scheduled for late 2021/early 2022, this is when shard chains should start supporting all contracts and transactions. 

How might the next phases of Ethereum 2.0 implications affect ETH prices? This is a question we will certainly return to in the blog. 

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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!