We explain how staking works in Ethereum 2.0

Maciej Zieliński

09 Aug 2022
We explain how staking works in Ethereum 2.0

For a long time, there has been news about the Ethereum upgrade. It is likely that from mid-September 2022. PoW will transform into PoS.  The developer of the leading altcoin suggested last month that the merger could take place on September 19. Ethereum has seen significant growth (more than 60%) relative to other cryptocurrencies during the bull market. What will Ethereum 2.0 be? What are the differences between classic ETH and the merger? How do you stack on Ethereum 2.0? We write about it below! 

What is Ethereum 2.0?

Ethereum 2.0 is a new version of the Ethereum blockchain. It will use Proof - of - Stake to verify transactions. The Ethereum 2.0 staking engine itself will replace the proof-of-work model, in which cryptocurrency miners use powerful computers to perform complex mathematical functions called hashes. Currently, the mining process requires an ever-increasing amount of electricity to verify Ethereum transactions before they are written to the public blockchain. The Proof of Work and energy consumption on ETH annually can be compared to the carbon footprint of all of Switzerland or Finland. Ethereum 2.0 is expected to change the energy world and reduce the carbon footprint by 99.95 percent. Ethereum 2.0 features four elements: 

  • Efficiency - Ethereum will be 99.95% more energy efficient than ETH. It is estimated that proof of rate will no longer require a national authority to secure the network.
  • Partitioning - Ethereum will be divided into 18 "shards." Each will operate separately and simultaneously, improving the efficiency of the project. Each shard will contain its independent state, which means a unique set of account balances and smart contracts. Sharding is by far the most complex Ethereum scaling solution.
  • Staking - Ethereum will move to PoS so everyone can participate and help secure the network.
  • Security - in Proof-of-Stake, network disruption becomes more expensive. 

Proof Of Stake vs Proof Of Work 

Proof of Stake (PoS) is a mechanism for validating mining transactions. With PoS, users can mine and validate their own transactions based on their coin holdings. Therefore, each miner's amount of owned cryptocurrencies is related to the crypto mining capabilities.  

Proof of Work works based on how quickly miners mine crypto and solve equations. PoW is a system that focuses on network cybersecurity, a consensus mechanism that requires miners' efforts to counter malware and negative use of computing power. 

Ethereum 2.0

In what phases will Ethereum 2.0 be implemented? 

The Ethereum 2.0 project is being implemented in three phases: 

  • Phase 0- Beacon Chain - launched on December 1, 2020. This is where PoS is introduced into the Ethereum ecosystem. With this phase, the Ethereum network is coordinated and can serve as a consensus value. This acts as a precursor to the upcoming steps. 
  • Phase 1 - The Merge - the stage of merging the new consensus layer with the existing execution layer. At this point, there will be an end to mining on Ethereum. From this point on, the footprint will be reduced, and the implementation of new scalability elements for Ethereum - such as sharding - will begin. It is likely to go into effect around September 19, 2022. 
  • Phase 2- Sharding - there will be database partitioning, which will distribute the network load. In this phase, everyone will be able to run a node independently on weaker hardware (than before), Ethereum will be able to be staked on any hardware - a laptop or phone, and network participation will increase. 
Ethereum 2.0

What is staking on Ethereum 2.0? 

Many people are wondering what staking will look like on Ethereum 2.0. In the Beacon Chain phase, 32 ETH can be subject to community staking on validation nodes. Remember that 32 ETH2 staking is used to verify transactions and status on the network. In addition, it serves the function of guaranteeing that the approval node is operating correctly and honestly. As part of this, stakers receive Ethereum. In practice, validators will generate ETH as passive income and receive ETH dividends at specific intervals. According to estimates, staking in Ethereum 2.0 can create an ROI of 14% per year. According to analysts, the demand for ETH will increase after the proof-of-stake implementation due to the additional demand for ETH by proof-of-stake and validation nodes. In contrast, the demand for GPUs will decrease as mining on Ethereum ends.

How does staking work?

Unlike PoW, PoS-based blockchains combine 32 blocks of transactions in each round of validation, which takes an average of 6.4 minutes. "Epochs" is the name given to these groups of blocks. When a blockchain adds two additional Epochs one after the other, it is considered irreversible and finalized. Beacon Chain divides the stakers into 128 "committees" and randomly assigns them to specific block shards. Each committee is designated a "slot" and has a certain amount of time to propose a new block and then approve internal transactions. Each epoch has 32 slots and requires 32 sets of committees to complete the validation run.  Once a committee is formed for a block, a randomly selected member is given the exclusive right to propose new blocks of transactions. The remaining 127 members vote on the proposals and approve the transactions. Beacon Chain collects information about the state of shards. It distributes it to neighboring fragments to keep the network synchronized. Validators will be managed by Beacon Chain, which handles everything from recording their contributions to rewards and penalties. In addition, the second phase, which involves sharding, will see the process of dividing the Ethereum network into chunks called "shards." Each shard will have a state that contains a separate set of account balances and smart contracts. New blocks are added to the blockchain, and a "cross-link" is created to verify them after approval by the majority of the committee. Only after this approval does stakers selected to propose new blocks receive rewards.

How much can be earned by staking Ethereum 2.0?

To calculate the rewards in Ethereum 2.0, you need to use the annual interest rate and the function of the inverse of the square root. In practice, this means that the lower the total rate of ETH 2.0, the lower the profit. The reward models for proposers and validators are different. The block proposer will receive ⅛ of the base reward, and the validator will receive the remainder (7/8). To receive the exclusive reward, the validator must apply as soon as possible. For each gap (including block validation) completed without command, the payout is reduced. The bonus is reduced by 7/16 if two sites are seized before being submitted for validation, then to 7/32 if three sites pass, and so on. The speed of Ethereum 2.0 issuance depends mainly on the base reward. The lower the base reward, the higher the number of validators connected to Ethereum 2.0. 

How to start?

In order to start staking on the new Ethereum network, there must be the creation of a staking node between Ethereum 1.0 and Ethereum 2.0. Then it would help if you had computer hardware. The minimum requirements are not great. It is enough for users to have enough memory to download old and new Ethereum blockchains. Ethereum 1.0 already has about 900 TB of data and is growing at a rate of about 1 GB per day. In addition, validators will be required to maintain nodes connected to the blockchain. In practice, you need to have a good Internet connection to start staking. Once you install the validator's software on your hardware, you must send ETH to the Ethereum staking contract address. To do this, you need to generate two keys: 

  • one for signing and validating transaction blocks,
  • the other for cash withdrawals. 

Note that you will not be able to create keys for withdrawals until Eth1.0 and Eth2.0 merge in 2022. Before you send funds to the protocol address for staking, you must first go through launchpad 2.0. and follow the procedures. Going through the process and making the payment is supposed to block potential fraudsters who want to undermine the authenticity of the Ethereum 2.0 project. 

Is it worth betting on Ethereum 2.0?

Do many people wonder if Ethereum 2.0 is better than ETH? The answer to this question is that you need to grow and be open to new technologies. An annual interest rate of 6 to 15% is more attractive than any bank deposit. With a minimum requirement of 32 ETH, you can expect to earn between 2 and 5 ETH in practice at current prices. The downside is that you are freezing your capital. Another problem is that no one knows the value of ETH 2.0. The project could turn out to be a bigger success as well as a sizable failure. 

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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!