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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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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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