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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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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?

Follow us to stay updated!

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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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