How to create a simple smart contract to manage auctions ?

Paulina Lewandowska

07 Dec 2022
How to create a simple smart contract to manage auctions ?

In the previous tutorial you learned how to create a smart contract, which is a wallet. In this tutorial we will try to consolidate the knowledge from the previous tutorial, and expand it with new knowledge. 

So let's get to work. All the things you may not understand will be explained to you later in the text.

Defining variables

Let's start by defining the variables we'll need to make the smart contract work. We certainly need :

  • address of the person to whom the highest bid will be sent,
  • time when the auction in question will end,
  • address of the person who placed the highest bid,
  • why wei is the highest bid,
  • a table of addresses and the amount of money they transferred to the smart contract so that those who did not win the auction can withdraw their money,
  • variable whether the auction is completed or not.
pragma solidity 0.8.11;

contract Auction {

    address payable public beneficiary;

    uint public auctionEndTime;

    address public highestBidder;

    uint public highestBid;

    mapping(address => uint) pendingReturns;

    bool ended;

}

What can you not understand from the above code?

Mapping in solidity is a key-value array, equivalent to dictionary in other languages.

If you don't know why the auctionEndTime variable is given in plain uinta, it's because the time when the auction ends will be given in Unix time.

Let's create a constructor that will take the address to which the highest bid will be sent and how long the auction will last.

constructor(

        uint biddingTime,

        address payable beneficiaryAddress

    ) {

        beneficiary = beneficiaryAddress;

        auctionEndTime = block.timestamp + biddingTime;

    }

What is worth paying attention to ?

  • block.timestamp is a variable that simply means what time it is now given as Unix time of course ;)))

Function for making an offer

Now let's create a function that will be used to submit your bid. The function will return an error if :

  • The auction has ended
  • Our bid will be lower than the highest bid

At the end of the function execution, the function will emit an event that the highest bid has changed. The frontend application can listen to the emitted events on the smartcontest, so that as soon as the highest bid has changed, it can update it in the user interface.

event HighestBidIncreased(address bidder, uint amount);

    event AuctionEnded(address winner, uint amount);

    error AuctionAlreadyEnded();

    error BidNotHighEnough(uint highestBid);

    error AuctionNotYetEnded();

    error AuctionEndAlreadyCalled();

    function bid() external payable {

        if (block.timestamp > auctionEndTime)

            revert AuctionAlreadyEnded();

        if (msg.value <= highestBid)

            revert BidNotHighEnough(highestBid);

        if (highestBid != 0) {

            pendingReturns[highestBidder] += highestBid;

        }

        highestBidder = msg.sender;

        highestBid = msg.value;

        emit HighestBidIncreased(msg.sender, msg.value);

    }

As you can see in the code above, we define ourselves events with parameters that we can emit on the blockchain. To emit an event in solidity we type 

emit + event name and parameters

We have defined our own errors, which if we want to call them we type in revert + the name of our errror and parameters.

You have probably already guessed that instead of ifs and custom errors we could have used requier.

This function checks if the auction has already ended, if msg.value is higher than the highest bid, if so we update the mapping pendingReturns so that the person who placed the highest bid earlier can get his money back. We assign the highest bid to msg.value and the highestBidder to msg.sender, at the end of the function execution we emit an event that informs that the highest bid has been increased.

Function to end the auction, and transfer the highest bid to the benefactor

Now let's create a function so that after the auction ends, the beneficiary's address can send money to his wallet.

This function should:

  • Return an error if the auction has not yet ended,
  • return an error if this function has already been called,
  • change the ended variable to true,
  • emit an event indicating that the auction has ended,
  • transfer an amount of Ethereum equivalent to the highest bid to the benefactor.
function auctionEnd() external {

        if (block.timestamp < auctionEndTime)

            revert AuctionNotYetEnded();

        if (ended)

            revert AuctionEndAlreadyCalled();

        ended = true;

        emit AuctionEnded(highestBidder, highestBid);

        beneficiary.transfer(highestBid);

    }

Function for people who did not win the auction and want their money back

Now, the last function we need to make our contract ready! It will be for addresses that did not win the auction and want to get their money back. Let's consider what such a function should have in it :

  • it should check how much Ethereum you owe in the mapping pendingReturns and assign this value to a variable,
  • it should change how much you owe to 0,
  • it should send as much Ethereum as you have stored in the variable.

Well, let's get to work !

  function withdraw() external{

        uint amount = pendingReturns[msg.sender];

        pendingReturns[msg.sender] = 0;

        payable(msg.sender).transfer(amount);

    }

That's the end of today's tutorial ! Our smart contract is ready. In order to practice and consolidate your knowledge, as a task you can try to replace custom errors with requirs. However, if this is not enough for you, you can also improve this contract so that it can be used to conduct several auctions at once.

Tagi

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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!