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Digital Customer Experiences

In today’s rapidly evolving digital landscape, businesses are increasingly focusing on enhancing their digital customer experiences. This shift is driven by the need to meet the growing expectations of tech-savvy consumers who demand seamless, personalized, and efficient interactions with brands. As a result, companies are leveraging digital platforms to transform how they engage with customers, creating more meaningful and impactful connections.

Transforming Engagement Through Digital Platforms

Digital platforms have revolutionized the way businesses engage with their customers, offering a range of tools and technologies that facilitate more dynamic and interactive interactions. From social media and mobile apps to chatbots and virtual reality, these platforms provide businesses with opportunities to reach customers in innovative ways, tailoring experiences to individual preferences and behaviors. By harnessing data analytics and machine learning, companies can gain insights into customer needs and preferences, enabling them to deliver personalized content and services that enhance customer satisfaction and loyalty. As digital platforms continue to evolve and mature, they are set to play an even more critical role in shaping the future of customer engagement.

The transformation of digital customer experiences is not merely a trend but a fundamental shift in how businesses operate and interact with their audiences. By embracing digital platforms and technologies, companies can not only meet the demands of modern consumers but also drive growth and innovation in an increasingly competitive marketplace. As businesses continue to explore new ways to enhance customer engagement, the focus will remain on creating experiences that are not only efficient and effective but also meaningful and memorable.

5 AI Projects to Build This Weekend Using Python: From Beginner to Adv

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5 AI Projects to Build This Weekend Using Python: From Beginner to Adv


Getting hands-on with real-world AI projects is the best way to level up your skills. But knowing where to start can be challenging, especially if you’re new to AI. Here, we break down five exciting AI projects you can implement over the weekend with Python—categorized from beginner to advanced. Each project uses a problem-first approach to create tools with real-world applications, offering a meaningful way to build your skills.

1. Job Application Resume Optimizer (Beginner)

Updating your resume for different job descriptions can be time-consuming. This project aims to automate the process by using AI to customize your resume based on job requirements, helping you better match recruiters’ expectations.

Steps to Implement:

Convert Your Resume to Markdown: Begin by creating a simple markdown version of your resume.

Generate a Prompt: Create a prompt that will input your markdown resume and the job description and output an updated resume.

Integrate OpenAI API: Use the OpenAI API to adjust your resume dynamically based on the job description.

Convert to PDF: Use markdown and pdfkit libraries to transform the updated markdown resume into a PDF.

Libraries: openai, markdown, pdfkit

Code Example:

import openai
import pdfkit

openai.api_key = “your_openai_api_key”

def generate_resume(md_resume, job_description):
prompt = f”””
Adapt my resume in Markdown format to better match the job description below. \
Tailor my skills and experiences to align with the role, emphasizing relevant \
qualifications while maintaining a professional tone.

Resume in Markdown:
{md_resume}

Job Description:
{job_description}

Please return the updated resume in Markdown format.
“””

response = openai.Completion.create(
model=“gpt-3.5-turbo”,
messages=[{“role”: “user”, “content”: prompt}]
)

return response.choices[0].text

md_resume = “Your markdown resume content here.”
job_description = “Job description content here.”

updated_resume_md = generate_resume(md_resume, job_description)

pdfkit.from_string(updated_resume_md, “optimized_resume.pdf”)

This project can be expanded to allow batch processing for multiple job descriptions, making it highly scalable.

2. YouTube Video Summarizer (Beginner)

Many of us save videos to watch later, but rarely find the time to get back to them. A YouTube summarizer can automatically generate summaries of educational or technical videos, giving you the key points without the full watch time.

Steps to Implement:

Extract Video ID: Use regex to extract the video ID from a YouTube link.

Get Transcript: Use youtube-transcript-api to retrieve the transcript of the video.

Summarize Using GPT-3: Pass the transcript into OpenAI’s API to generate a concise summary.

Libraries: openai, youtube-transcript-api, re

Code Example:

import re
import openai
from youtube_transcript_api import YouTubeTranscriptApi

openai.api_key = “your_openai_api_key”

def extract_video_id(youtube_url):
match = re.search(r'(?:v=|\/)([0-9A-Za-z_-]{11}).*’, youtube_url)
return match.group(1) if match else None

def get_video_transcript(video_id):
transcript = YouTubeTranscriptApi.get_transcript(video_id)
transcript_text = ‘ ‘.join([entry[‘text’] for entry in transcript])
return transcript_text

def summarize_transcript(transcript):
response = openai.Completion.create(
model=“gpt-3.5-turbo”,
messages=[{“role”: “user”, “content”: f”Summarize the following transcript:\n{transcript}}]
)
return response.choices[0].text

youtube_url = “https://www.youtube.com/watch?v=example”
video_id = extract_video_id(youtube_url)
transcript = get_video_transcript(video_id)
summary = summarize_transcript(transcript)

print(“Summary:”, summary)

With this tool, you can instantly create summaries for a collection of videos, saving valuable time.

3. Automatic PDF Organizer by Topic (Intermediate)

If you have a collection of research papers or other PDFs, organizing them by topic can be incredibly useful. In this project, we’ll use AI to read each paper, identify its subject, and cluster similar documents together.

Steps to Implement:

Read PDF Content: Extract text from the PDF’s abstract using PyMuPDF.

Generate Embeddings: Use sentence-transformers to convert abstracts into embeddings.

Cluster with K-Means: Use sklearn to group documents based on their similarity.

Organize Files: Move documents into folders based on their clusters.

Libraries: PyMuPDF, sentence_transformers, pandas, sklearn

Code Example:

import fitz
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import os
import shutil

model = SentenceTransformer(‘all-MiniLM-L6-v2’)

def extract_abstract(pdf_path):
pdf_document = fitz.open(pdf_path)
abstract = pdf_document[0].get_text(“text”)[:500]
pdf_document.close()
return abstract

pdf_paths = [“path/to/pdf1.pdf”, “path/to/pdf2.pdf”]
abstracts = [extract_abstract(pdf) for pdf in pdf_paths]
embeddings = model.encode(abstracts)

kmeans = KMeans(n_clusters=3)
labels = kmeans.fit_predict(embeddings)

for i, pdf_path in enumerate(pdf_paths):
folder_name = f”Cluster_{labels[i]}
os.makedirs(folder_name, exist_ok=True)
shutil.move(pdf_path, os.path.join(folder_name, os.path.basename(pdf_path)))

This organizer can be customized to analyze entire libraries of documents, making it an efficient tool for anyone managing large digital archives.

4. Multimodal Document Search Tool (Intermediate)

Key information may be embedded in both text and images in technical documents. This project uses a multimodal model to enable searching for information within text and visual data.

Steps to Implement:

Extract Text and Images: Use PyMuPDF to extract text and images from each PDF section.

Generate Embeddings: Use a multimodal model to encode text and images.

Cosine Similarity for Search: Match user queries with document embeddings based on similarity scores.

Libraries: PyMuPDF, sentence_transformers, sklearn

Code Example:

import fitz
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer(‘clip-ViT-B-32’)

def extract_text_and_images(pdf_path):
pdf_document = fitz.open(pdf_path)
chunks = []
for page_num in range(len(pdf_document)):
page = pdf_document[page_num]
chunks.append(page.get_text(“text”)[:500])
for img in page.get_images(full=True):
chunks.append(“image_placeholder”)
pdf_document.close()
return chunks

def search_query(query, documents):
query_embedding = model.encode(query)
doc_embeddings = model.encode(documents)
similarities = cosine_similarity([query_embedding], doc_embeddings)
return similarities

pdf_path = “path/to/document.pdf”
document_chunks = extract_text_and_images(pdf_path)
similarities = search_query(“User’s search query here”, document_chunks)
print(“Top matching sections:”, similarities.argsort()[::-1][:3])

This multimodal search tool makes it easier to sift through complex documents by combining text and visual information into a shared search index.

5. Advanced Document QA System (Advanced)

Building on the previous project, this system allows users to ask questions about documents and get concise answers. We use document embeddings to find relevant information and a user interface to make it interactive.

Steps to Implement:

Chunk and Embed: Extract and embed each document’s content.

Create Search + QA System: Use embeddings for search and integrate with OpenAI’s API for question-answering.

Build an Interface with Gradio: Set up a simple Gradio UI for users to input queries and receive answers.

Libraries: PyMuPDF, sentence_transformers, openai, gradio

Code Example:

import gradio as gr
import openai
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(“all-MiniLM-L6-v2”)

def generate_response(message, history):
response = openai.Completion.create(
model=“gpt-3.5-turbo”,

messages=[{“role”: “user”, “content”: message}]
)
return response.choices[0].text

demo = gr.ChatInterface(
fn=generate_response,
examples=[{“text”: “Explain this document section”}]
)

demo.launch()

This interactive QA system, using Gradio, brings conversational AI to documents, enabling users to ask questions and receive relevant answers.

These weekend AI projects offer practical applications for different skill levels. From resume optimization to advanced document QA, these projects empower you to build AI solutions that solve everyday problems, sharpen your skills, and create impressive additions to your portfolio.



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Dogecoin Down 19% Since Hitting 3-Year High—Despite Bitcoin Rebound – Decrypt

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Dogecoin Down 19% Since Hitting 3-Year High—Despite Bitcoin Rebound – Decrypt

The crypto markets have seen intense volatility since Bitcoin broke through the $100,000 mark for the first time on December 4, with multiple plunges that have sent shockwaves that sank other assets—and piled up liquidations in the process.

But while Bitcoin has mostly rebounded from the sizable dips, Dogecoin has lost considerable steam over the last week since popping to a high of $0.48 for the first time since 2021.

At a current price just below $0.39, Dogecoin is down nearly 19% since that peak seen late on December 7. And over the last seven days, including data from the hours before that recent high, DOGE is down 15%.

That makes it the biggest loser among the top 10 cryptocurrencies by market cap, outpacing Cardano with a 13% dip during that span, and Solana with a 10% correction. Bitcoin is the only asset in the top 10 that’s green on the week, up 0.7% as of this writing at a current price of $100,995.

Looking beyond the top 10, other leading meme coins in the top 100 cryptocurrencies have posted even sharper losses over the last week.

Dogwifhat (WIF) is the biggest loser in the top 100, down 28% during that span, while Bonk (BONK) has fallen 23%, Brett (BRETT) is down 22%, and Shiba Inu (SHIB) has matched the DOGE dip at 15%.

Overall, the crypto market has fallen by 3% over the last 24 hours, per data from CoinGecko.

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Monkey NFT: The Digital Revolution Swinging into the Spotlight – Web3oclock

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Monkey NFT: The Digital Revolution Swinging into the Spotlight – Web3oclock


Popular Monkey NFT Collections

Cultural and Market Significance

Market Trends and Financial Value

Cultural and Market Significance of Monkey NFT:



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High-End And Luxury Brands

High-End And Luxury Brands

In recent years, the intersection of technology and luxury has created a fascinating synergy, transforming the landscape of high-end brands. As consumers become more tech-savvy and demand personalized, seamless experiences, luxury brands are increasingly adopting cutting-edge technologies to stay relevant and enhance their offerings. This article explores the evolving role of technology in the realm of high-end and luxury brands, highlighting how this integration is reshaping the industry.

Technology’s New Role in High-End and Luxury Brands

Luxury brands have traditionally been synonymous with craftsmanship, exclusivity, and timeless elegance. However, in today’s digital age, these brands are harnessing the power of technology to redefine luxury for a new generation of consumers. From augmented reality (AR) and virtual reality (VR) experiences to artificial intelligence (AI) and blockchain, technology is becoming an integral part of the luxury experience. Brands are using AR and VR to create immersive shopping experiences, allowing customers to visualize products in their own environment or explore virtual boutiques from the comfort of their homes. AI is being leveraged for personalized marketing and customer service, offering tailored recommendations and enhancing the overall consumer journey. Meanwhile, blockchain technology is ensuring transparency and authenticity, addressing concerns about counterfeit goods and reinforcing the trust that luxury brands are built upon.

As luxury brands infuse technology into their operations, they are not only enhancing the customer experience but also optimizing their internal processes. Data analytics and machine learning are enabling brands to better understand consumer preferences and predict trends, allowing for more informed decision-making. This integration of technology is also streamlining supply chains, improving inventory management, and reducing waste, all of which contribute to more sustainable business practices. Additionally, the use of digital tools is facilitating greater collaboration and innovation, as designers and artisans can experiment with new techniques and materials in virtual environments before bringing their creations to life. By embracing technology, luxury brands are not only preserving their heritage but also ensuring their relevance in a rapidly changing world.

The infusion of technology into high-end and luxury brands marks a significant evolution in the industry, where tradition and innovation coexist harmoniously. As these brands continue to navigate the digital landscape, they are redefining what it means to be luxurious in the 21st century. By leveraging technology, luxury brands are not only enhancing their appeal to a new generation of consumers but also paving the way for a more sustainable, efficient, and personalized future. The journey of integrating technology is ongoing, and its impact on the luxury sector will undoubtedly continue to unfold in exciting ways.

Blockchain Basics and Beyond: Powering the Future of Decentralization  – Web3oclock

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Blockchain Basics and Beyond: Powering the Future of Decentralization  – Web3oclock


Applications of Blockchain Technology

Blockchain and its Cryptocurrencies

Emerging Blockchain Technologies and Trends

Tools and Resources for Blockchain

Introduction to Blockchain:

The list of successful transactions.

A hash for the current block.

1. Public Blockchains vs. Private Blockchains: 

FeaturePublic BlockchainsPrivate BlockchainsAccessOpen to anyone to join and participateRestricted access, typically within an organizationAuthorityDecentralized, no single entity controls the networkCentralized, controlled by one organizationConsensus MechanismUses mechanisms like Proof of Work (PoW) or Proof of Stake (PoS)Can use customized, permission consensus mechanismsTransparencyFully transparent, all transactions are visible to the publicTransactions are visible only to authorized usersExamplesBitcoin, EthereumHyperledger, Corda

Ethereum, proposed in 2015 by the self-described geek Vitalik Buterin, is more than just an electronic currency. The Users can freely serve the ETH cryptocurrency, which is the default currency for Ethereum, but this is not what ETH is about. What makes Ethereum so different from the other blockchains is the smart contracts feature that simply allows the Ethereum blockchain to create contracts that are executed as codes on the blockchain. This allowed the emergence of dApps and many more uses including DeFi and NFTs. 

A blockchain whitepaper is a detailed document that outlines the technical and business aspects of a blockchain project. It’s often the first introduction to a new cryptocurrency or decentralized solution, offering potential investors or developers a deep dive into its mechanics, use case, and potential market impact. 

1. Trends and Predictions:

2. Challenges and Opportunities



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AI Penny Stocks to Watch for Big Gains – Web3oclock

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AI Penny Stocks to Watch for Big Gains – Web3oclock


What Are AI Penny Stocks?

Top AI Penny Stocks to Watch in 2024

Key Characteristics of AI Stocks

Why Consider AI Penny Stocks?

Benefits of Investing in AI Penny Stocks

How to Invest in AI Penny Stocks

Things to Watch Before Investing

Risks of Investing in AI Penny Stocks

NameSectorKey HighlightRecent PerformanceBigBear.ai (BBAI)Decision IntelligenceProvides AI solutions for supply chain, cybersecurity, and defense applications.Increased government contracts; market cap $495M​.Rekor Systems (REKR)Mobility Data AnalyticsAI-powered traffic monitoring and accident detection solutions.Market cap $190M; steady client expansion​.CXApp (CXAI)Workplace CollaborationAI and AR-based tools improving hybrid workplace connectivity.The stock gained over 83% YTD in 2024​.Evolv Tech (EVLV)Security ScreeningAI for advanced weapon detection at public venues and events.Market cap $695M; 7.0 software version released​.Predictive Oncology (POAI)Drug DiscoveryUses AI to predict drug responses; focuses on oncology research.Market cap $12M; niche player in predictive medicine​.Himax Tech (HIMX)SemiconductorsAI-enabled IoT products like the WiseEye platform for motion sensing and detection.Market cap $935M; solid performance in IoT market.Gaxos.AI (GXAI)Gaming & Health TechAI solutions for gaming and health, targeting mental well-being and longevity.Stock up 62% YTD with expanded health AI focus​.

Key Characteristics of AI Stocks:

Benefits of Investing in AI Penny Stocks:

How to Invest in AI Penny Stocks: 5 Key Steps

Look in for companies that cater to real-world kind of problems and with unique AI approaches.

Track the stock history and financial report using some tools like Yahoo Finance, Google Finance, or Morningstar.

Stay up to date on the current news related to the AI industry and the company as well.

Consider allocating smaller amounts to various stocks across different AI niches (e.g., AI healthcare, fintech, or robotics).

Clear-cut limits for speculation investments. 

Do not draw money planned for meeting essential needs or long-term purposes including retirement. 

Track performance consistently but focus on long-term growth potential.

Be prepared to hold onto promising stocks for a while to allow the company to scale and achieve success.

Things to Watch Before Investing:



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Crypto in the Metaverse: Forging a Revolutionary Digital Economy – Web3oclock

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Crypto in the Metaverse: Forging a Revolutionary Digital Economy – Web3oclock


What are Metaverse Cryptocurrencies?

The Use of Cryptocurrencies in Virtual Worlds

What are Metaverse Cryptocurrencies?

The Use of Cryptocurrencies in Virtual Worlds:

1. Facilitating Virtual Commerce:

2. Buying and Selling Virtual Real Estate:

3. Governance and Voting:

4. Revolutionizing Gaming:

5. Reward Mechanisms and Microtransactions:

6. Tokenizing Digital Art and Collectibles:

7. Royalties and Fair Compensation:

8. Reducing Volatility with Stablecoins:

9. Cross-Platform Interoperability:



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Linea Association unveils plan for decentralized governance with LINEA token

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Linea Association unveils plan for decentralized governance with LINEA token

At Devcon in Bangkok, the Linea Association announced its formation to oversee the development and governance of Linea’s open-source technology and ecosystem. The Swiss non-profit aims to decentralize the Linea Network—the zkEVM Layer-2 solution designed to scale Ethereum—by launching the LINEA token by the end of Q1 2025, enabling community-driven governance.

The Association’s mission includes supporting the growth of Linea Mainnet to build a fast, affordable, and secure network accessible worldwide. It plans to advance decentralization through new governance mechanisms and implement decentralized sequencing and proving. Empowering developers to create decentralized applications with enhanced user experiences and fostering strong, engaged communities are also key priorities.

Nicolas Liochon, founder of Linea and board member of the Linea Association, said.

“Decentralization is at the core of Linea’s vision. Linea must be owned and governed openly by all as a public good, just as Layer 1 Ethereum is.”

The governance structure will feature a Board of Directors, a General Assembly, an Executive Director, and a token governance body. The LINEA token will allow holders to participate in governance, with details on token design and utility to be shared before the token generation event. More than 1.3 million verified addresses have joined the network, reflecting Linea’s focus on organic community growth.

Since its mainnet launch in August 2023, Linea has processed over 230 million transactions, making it one of the fastest-growing zkEVMs on Ethereum. The ecosystem has also expanded to over 420 ecosystem partners. The technology is publicly available under the Apache license, allowing users to view, fork, and modify the code.

The Association operates independently of Consensys, aligning with CEO Joseph Lubin’s vision to decentralize core innovations progressively. Lubin said,

“As Consensys progresses toward decentralization, Linea represents a foundational step in our vision of creating a Network State for the emerging decentralized global economy.”

Linea has integrated long-term Ethereum contributors like Status, developers of the Nimbus client that secures 10% of Ethereum’s proof-of-stake network. The Association plans to decentralize core protocol development and governance further, ensuring social and technical alignment within the community.

The Swiss Association structure allows token holders to have governance over managing IP and a treasury supporting Linea’s mission. The focus remains on furthering the growth and development of the open-source LINEA technology and the Linea Network.

Linea aims to empower users and businesses to manage valuable on-chain data, including identity and property. Per the announcement, the Linea Association seeks to be a significant step toward decentralizing the network and fostering collaborative, transparent governance. The initiative aims to empower the global community to shape the future of Linea and contribute to the broader Ethereum ecosystem.

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Decode Dynamic Solidity Structs with Hyperledger Web3j

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Decode Dynamic Solidity Structs with Hyperledger Web3j


In Solidity, dynamic structs are complex data types that can store multiple elements of varying sizes, such as arrays, mappings, or other structs. The system encodes these dynamic structs into binary format using Ethereum’s ABI (Application Binary Interface) encoding rules. The system encodes the structs whenever it stores or passes them in transactions.

Decoding this binary data is crucial for interpreting the state or output of a smart contract. This process involves understanding how Solidity organizes and packs data, particularly in dynamic types, to accurately reconstruct the original struct from its binary representation. This understanding is key to developing robust and interoperable decentralized applications.

Decoding dynamic structs in an external development environment that interacts with a blockchain network is challenging. These structs can include arrays, mappings, and nested structs of different sizes. They require careful handling to keep data accurate during encoding and decoding. In Hyperledger Web3j, we addressed this by creating object classes that match the expected struct format in the blockchain environment.

These object classes are designed to inherit from the org.web3j.abi.datatypes.DynamicStruct class, which is part of the ABI module. The developers designed this class to handle the complexities of encoding and decoding dynamic structs and other Solidity data types. The ABI module leverages Hyperledger Web3j’s type-safe mapping to ensure easy and secure interactions with these complex data structures.

However, when the goal is to extract a specific value from encoded data, creating a dedicated object can add unnecessary complexity. This approach can also use up extra resources. To address this, our contributors, calmacfadden and Antlion12, made significant improvements by extending the org.web3j.abi.TypeReference class.

Their enhancements allow dynamic decoding directly within the class, removing the need to create extra objects. This change simplifies the process of retrieving specific values from encoded data. This advancement reduces overhead and simplifies interactions with blockchain data.

Decoding dynamic struct before enhancement

To clarify, here’s a code example that shows how you could decode dynamic structs using Hyperledger Web3j before the enhancements.

/**
* create the java object representing the solidity dinamyc struct
* struct User{
* uint256 user_id;
* string name;
* }
*/
public static class User extends DynamicStruct {
public BigInteger userId;

public String name;

public Boz(BigInteger userId, String name) {
super(
new org.web3j.abi.datatypes.generated.Uint256(data),
new org.web3j.abi.datatypes.Utf8String(name));
this.userId = userId;
this.name = name;
}

public Boz(Uint256 userId, Utf8String name) {
super(userId, name);
this.userId = userId.getValue();
this.name = name.getValue();
}
}
/**
* create the function which should be able to handle the class above
* as a solidity struct equivalent
*/
public static final org.web3j.abi.datatypes.Function getUserFunction = new org.web3j.abi.datatypes.Function(
FUNC_SETUSER,
Collections.emptyList(),
Arrays.<typereference<?>>asList(new TypeReference() {}));

</typereference<?>

Now as the prerequisite is done, the only thing left is to call do the decode and here is an example:

@Test
public void testDecodeDynamicStruct2() {
String rawInput =
“0x0000000000000000000000000000000000000000000000000000000000000020”
+ “000000000000000000000000000000000000000000000000000000000000000a”
+ “0000000000000000000000000000000000000000000000000000000000000040”
+ “0000000000000000000000000000000000000000000000000000000000000004”
+ “4a686f6e00000000000000000000000000000000000000000000000000000000
“;

assertEquals(
FunctionReturnDecoder.decode(
rawInput,
getUserFunction.getOutputParameters()),
Collections.singletonList(new User(BigInteger.TEN, “John”)));
}

In the above test, we decoded and asserted that the rawInput is a User struct having the name John and userId 10.

Decoding dynamic struct with new enhancement

With the new approach, declaring an equivalent struct object class is no longer necessary. When the method receives the encoded data, it can immediately decode it by creating a matching reference type. This simplifies the workflow and reduces the need for additional class definitions. See the following example for how this can be implemented:

public void testDecodeDynamicStruct2() {
String rawInput =
“0x0000000000000000000000000000000000000000000000000000000000000020”
+ “000000000000000000000000000000000000000000000000000000000000000a”
+ “0000000000000000000000000000000000000000000000000000000000000040”
+ “0000000000000000000000000000000000000000000000000000000000000004”
+ “4a686f6e00000000000000000000000000000000000000000000000000000000
“;

TypeReference dynamicStruct =
new TypeReference(
false,
Arrays.asList(
TypeReference.makeTypeReference(“uint256”),
TypeReference.makeTypeReference(“string”))) {};

List decodedData =
FunctionReturnDecoder.decode(rawInput,
Utils.convert(Arrays.asList(dynamicStruct)));

List decodedDynamicStruct =
((DynamicStruct) decodedData.get(0)).getValue();

assertEquals(decodedDynamicStruct.get(0).getValue(), BigInteger.TEN);
assertEquals(decodedDynamicStruct.get(1).getValue(), “John”);}

In conclusion, Hyperledger Web3j has made great progress in simplifying the decoding of dynamic Solidity structs. This addresses one of the most challenging parts of blockchain development. By introducing object classes like org.web3j.abi.datatypes.DynamicStruct and enhancing the org.web3j.abi.TypeReference class, the framework now provides a more efficient and streamlined method for handling these complex data types.

Developers no longer need to create dedicated struct classes for every interaction, reducing complexity and resource consumption. These advancements not only boost the efficiency of blockchain applications but also make the development process easier and less prone to errors. This ultimately leads to more reliable and interoperable decentralized systems.

 



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