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Building Decentralized Oracles: A Comprehensive Guide for Developers

Karolina

01 Jun 2023
Building Decentralized Oracles: A Comprehensive Guide for Developers

In the realm of blockchain technology, decentralized oracles play a crucial role by facilitating secure and trustworthy connections between real-world data and blockchain networks. Acting as a conduit, these oracles enable the seamless integration of off-chain information into decentralized applications (DApps) and smart contracts. Utilizing such oracles allows developers to expand on the potential of blockchain technology by tapping into verifiable and resistant data from a variety of sources. We will delve into the complexities of constructing decentralized oracles in this guide, addressing their advantages, obstacles, recommended approaches, and available frameworks for crafting inventive blockchain solutions.

Table of Contents

Understanding Oracles in the Blockchain Context

Understanding Oracles

Specialized systems known as oracles facilitate the connection between blockchain networks and external data sources. Serving as intermediaries, they supply off-chain data to on-chain applications like smart contracts and DApps, allowing blockchain applications to access real-world information, events, and data feeds securely and reliably.

Types of Oracles

Centralized Oracles:

When it comes to providing data inputs for blockchains, centralized oracles depend on a single authority or entity. Though their implementation is relatively simple, they create a single point of failure and potential vulnerabilities, which can compromise the data's trustworthiness and security.

Decentralized Oracles:

In contrast, decentralized oracles use the principles of blockchain technology to offer a more secure and dependable method. They distribute the tasks of data retrieval, validation, and aggregation among multiple participants to ensure consensus while minimizing manipulation or tampering risks. Decentralized oracles enhance trust and verifiability in blockchain applications.

Decentralized oracles are prominent due to their capability to deliver reliable and tamper-proof data inputs that align with the core concepts of decentralization and trustlessness in blockchain technology. By comprehending the various oracle types and their implications, developers can make well-informed decisions when incorporating oracles into their blockchain endeavors.

Components of a Decentralized Oracle

A decentralized oracle is not a simple, single entity, but rather a combination of several components that work together to connect blockchain-based smart contracts with the external world. Understanding these components is vital for any developer who wishes to construct a decentralized oracle. Let's delve into the core components:

Oracle Node

Oracle nodes are the workhorses of a decentralized oracle network. They are responsible for retrieving and validating real-world data from external data sources. Oracle nodes are also responsible for reporting the retrieved data back to the blockchain. A decentralized oracle network includes multiple oracle nodes to ensure data accuracy and prevent manipulation.

Data Providers

Data providers are the external sources from which oracle nodes retrieve the necessary real-world information. They can be anything from APIs of web services, data feeds, databases, to IoT devices. The choice of data providers is critical as they directly affect the accuracy and reliability of the data used in smart contracts.

Aggregation Contract

Once oracle nodes retrieve and validate data, it must be processed and formatted in a way that's useful for the smart contract requesting the data. This is where the aggregation contract comes in. It takes the data from multiple oracle nodes, processes it (often by calculating a median or average), and then feeds the aggregated data to the requesting smart contract.

Reputation System

In a decentralized oracle network, a reputation system is typically used to incentivize honest behavior and discourage malicious activity. Oracle nodes are rewarded or penalized based on their performance. Nodes that consistently provide accurate and timely data are rewarded, while those found to be unreliable or dishonest are penalized.

Request and Response Model

The request and response model is a core component of how a decentralized oracle functions. When a smart contract needs data from the outside world, it sends a request to the oracle. The oracle nodes then fetch the required data from the chosen data providers, validate it, and send it back to the smart contract.

Security Mechanisms

Security is a paramount concern for any component interfacing with a blockchain. Decentralized oracles often incorporate various security measures to protect against attacks. These might include cryptographic proofs for data integrity, multi-signature confirmations for critical transactions, and secure data transmission protocols.

In summary, building a decentralized oracle involves constructing and connecting these components in a way that ensures the reliable, secure, and timely delivery of real-world data to smart contracts. Each component plays a crucial role in the overall functioning of the decentralized oracle. Understanding these components and their interactions is a prerequisite for creating a robust decentralized oracle.

Building a Decentralized Oracle - The Process

Creating a decentralized oracle is a challenging but rewarding process. It involves careful planning, thoughtful design, meticulous coding, rigorous testing, and effective deployment. Here is a step-by-step guide to building a decentralized oracle. 

Step 1: Setting up the Environment

The first step in building a decentralized oracle is setting up the development environment. This typically involves:

  • Choosing a blockchain platform: The choice of blockchain platform depends on various factors like the target audience, security requirements, scalability needs, and the type of data to be handled. Ethereum is a popular choice due to its extensive developer support and robust smart contract capabilities, but other platforms like Binance Smart Chain, Polkadot, or Cosmos might be more suitable depending on your specific needs.
  • Setting up the development tools: Depending on the chosen blockchain platform, you will need to install and configure the appropriate development tools. For Ethereum, this would include tools like Truffle, Ganache, and the Solidity programming language.

Step 2: Developing the Oracle Smart Contract

The next step is to write the oracle smart contract. This contract will handle requests from other contracts, fetch data from the external world, and return the retrieved data. This process involves:

  • Designing the contract: Before you start coding, you should design the contract's interface and determine how it will interact with other contracts and external data sources.
  • Writing the contract: Using your chosen programming language (such as Solidity for Ethereum), write the smart contract code. Be sure to follow best practices for security and efficiency.
  • Debugging: Debugging is a critical part of the development process. Test your contract thoroughly to ensure it behaves as expected and doesn't contain any vulnerabilities.

Step 3: Integrating Data Providers

Once your oracle smart contract is ready, you'll need to connect it to external data providers. This involves:

  • Selecting data providers: Choose reliable and accurate data providers that can supply the type of data you need. This could be anything from financial data feeds, weather APIs, IoT devices, or other web services.
  • Writing the integration code: Write the necessary code to fetch data from your chosen data providers and feed it into your oracle contract.

Step 4: Testing and Deploying the Oracle

The final step is to test your oracle thoroughly and then deploy it on your chosen blockchain. This includes:

  • Testing: Conduct thorough testing to ensure that your oracle works correctly and securely. This should include unit tests, integration tests, and stress tests. Consider using testing frameworks and tools to automate this process.
  • Deployment: Once you're confident that your oracle is ready, deploy it on the blockchain. Be sure to follow best practices for contract deployment, and consider using a deployment tool to make the process easier and more reliable.
  • Building a decentralized oracle is a complex but rewarding process. By following these steps, you'll be well on your way to creating a powerful tool that can bridge the gap between the blockchain and the outside world.

While it's entirely possible to build a decentralized oracle from scratch, leveraging existing oracle platforms can significantly ease the development process. These platforms offer tools, services, and frameworks that simplify the creation of secure, reliable, and efficient decentralized oracles. Let's explore some popular platforms:

Chainlink is one of the most well-known and widely used decentralized oracle platforms. It provides a flexible framework for connecting smart contracts with real-world data, APIs, and other off-chain resources. Developers can use Chainlink to create custom oracle networks, choose their own data sources, and define aggregation strategies.

Band Protocol

Band Protocol offers a decentralized data oracle that allows smart contracts to access external data in a secure and scalable manner. The Band Protocol is known for its efficient design, which reduces the amount of data stored on-chain, leading to faster transactions and lower costs.

Provable (formerly Oraclize)

Provable provides reliable oracle services for various blockchains, including Ethereum, Bitcoin, and EOS. It focuses on data transport, authenticity proofs, and easy integration. Provable's technology allows developers to fetch data from any web API, ensuring a wide range of potential use cases.

Challenges and Potential Solutions

Building decentralized oracles comes with a set of unique challenges. Here are some of the most common ones, along with potential solutions:

Data Accuracy and Reliability

Challenge: Ensuring the accuracy and reliability of data from external sources

Solution: Using multiple data sources for cross-verification and choosing reputable and reliable data providers. Implementing a consensus mechanism for data validation can also help.

Timeliness of Data

Challenge: Providing real-time or near-real-time data to smart contracts, as fetching data from external sources can introduce latency

Solution: Optimizing the data retrieval process, using fast and reliable data providers, and implementing predictive algorithms to anticipate future data needs.

Security

Challenge: Protecting against potential attacks such as man-in-the-middle attacks, Sybil attacks, or direct attacks on the data source

Solution: Implementing robust security measures like cryptographic proofs for data integrity, secure data transmission protocols, and utilizing additional security services from oracle platforms, if available.

Complexity of Development

Challenge: Navigating the complex process of building a decentralized oracle, which requires deep knowledge of blockchain technology and the specific oracle platform

Solution: Leveraging existing oracle platforms that offer tools and frameworks to simplify the development process, and utilizing educational resources and developer communities around these platforms.

Despite these challenges, with the right approach and tools, it's entirely possible to build effective and secure decentralized oracles to bridge the gap between blockchain networks and the real world.

Conclusion

Decentralized oracles play a crucial role in connecting blockchain networks with real-world data, facilitating secure and dependable interactions. By leveraging these oracles, developers can tap into verified data from diverse sources, extending blockchain technology's reach across numerous sectors. In this exhaustive guide, we have delved into the concept of oracles, making a distinction between centralized and decentralized varieties while shedding light on their advantages and drawbacks. Additionally, we have explored the fundamental elements of a decentralized oracle, the procedure for constructing one, and well-known platforms that streamline oracle development. Equipped with this information, developers can seamlessly incorporate decentralized oracles into their blockchain ventures, uncovering new potential and transforming how blockchain engages with the real world.

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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?
Table of Contents

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

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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!

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