CrewLinQ

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Authors:
(1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran;
(2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran;
(3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada
(4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Table of Links Abstract and Introduction Materials and Methods Results RQ 1: AI and scientometrics RQ 2: AI and webometrics RQ 3: AI and bibliometrics Discussion RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI Conclusion, Limitations, and References RQ 2: AI and webometrics In webometrics, AI can provide several specific benefits including Web Crawling and Data Collection, Web Link Analysis, Web Content Analysis, Social Media Analysis, Web Impact Analysis, and Recommender Systems as sketched in Figure 3, and e.g. it has been demonstrated through papers like [9, 10, 21, 36-45]. These 6 considerations point to the potential benefits and suggest focused strategies for utilizing AI capabilities in webometrics. The resulting findings highlight how AI can improve the quality, accessibility, and data collection processes in webometrics analyses, as outlined in Table 2. Indeed, algorithms based on artificial intelligence can automatically crawl and collect data from websites, including institutional websites, scientific research portals, and online repositories [39, 42]. This enables researchers to gather large amounts of web-based information for analysis, including publication data, author profiles, and citation patterns. In order to understand the relationship between publications, websites, and authors, artificial intelligence approaches can analyze hyperlink structures and web link patterns [9, 43]. By analyzing the link structure, AI algorithms can identify influential websites and authors, as well as detect communities, collaborations, and research networks within the web-based scientific ecosystem [17]. AI techniques, such as natural language processing and machine learning, can be employed to analyze the content of webpages and scientific publications available online [40, 41]. This enables researchers to extract key information, such as keywords, topics, and sentiments, from web-based documents, facilitating comprehensive analysis and understanding of research outputs. AI can analyze social media platforms, such as Twitter, to understand the online discussions, trends, and interactions related to scientific research [36, 38, 44]. By analyzing hashtags, mentions, and user behavior, AI algorithms can identify influential research topics, key opinion leaders, and potential collaborations within the online scientific community, as demonstrated in such previous works. AI can assess the impact and visibility of scientific research on the web [37, 46]. Indeed, by analyzing web traffic, page views, and social media metrics, AI algorithms can provide insights into the online visibility, dissemination, and engagement of scientific publications, authors, and research institutions. “Finally”, AI-powered recommender systems can assist researchers in discovering relevant scientific websites, online resources, and research collaborations [35, 45]. These papers, based on user preferences, reading behavior, and web usage data, show that personalized recommendations can be generated using AI algorithms, making it easier for researchers to explore the web-based scientific landscape and discover new opportunities for further research. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran; (2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran; (3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada (4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Authors: Authors: (1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran; (2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran; (3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada (4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Table of Links Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Results Results RQ 1: AI and scientometrics RQ 1: AI and scientometrics RQ 2: AI and webometrics RQ 2: AI and webometrics RQ 3: AI and bibliometrics RQ 3: AI and bibliometrics Discussion Discussion RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI Conclusion, Limitations, and References Conclusion, Limitations, and References RQ 2: AI and webometrics In webometrics, AI can provide several specific benefits including Web Crawling and Data Collection, Web Link Analysis, Web Content Analysis, Social Media Analysis, Web Impact Analysis, and Recommender Systems as sketched in Figure 3, and e.g. it has been demonstrated through papers like [9, 10, 21, 36-45]. These 6 considerations point to the potential benefits and suggest focused strategies for utilizing AI capabilities in webometrics. The resulting findings highlight how AI can improve the quality, accessibility, and data collection processes in webometrics analyses, as outlined in Table 2. Indeed, algorithms based on artificial intelligence can automatically crawl and collect data from websites, including institutional websites, scientific research portals, and online repositories [39, 42]. This enables researchers to gather large amounts of web-based information for analysis, including publication data, author profiles, and citation patterns. In order to understand the relationship between publications, websites, and authors, artificial intelligence approaches can analyze hyperlink structures and web link patterns [9, 43]. By analyzing the link structure, AI algorithms can identify influential websites and authors, as well as detect communities, collaborations, and research networks within the web-based scientific ecosystem [17]. AI techniques, such as natural language processing and machine learning, can be employed to analyze the content of webpages and scientific publications available online [40, 41]. This enables researchers to extract key information, such as keywords, topics, and sentiments, from web-based documents, facilitating comprehensive analysis and understanding of research outputs. AI can analyze social media platforms, such as Twitter, to understand the online discussions, trends, and interactions related to scientific research [36, 38, 44]. By analyzing hashtags, mentions, and user behavior, AI algorithms can identify influential research topics, key opinion leaders, and potential collaborations within the online scientific community, as demonstrated in such previous works. AI can assess the impact and visibility of scientific research on the web [37, 46]. Indeed, by analyzing web traffic, page views, and social media metrics, AI algorithms can provide insights into the online visibility, dissemination, and engagement of scientific publications, authors, and research institutions. “Finally”, AI-powered recommender systems can assist researchers in discovering relevant scientific websites, online resources, and research collaborations [35, 45]. These papers, based on user preferences, reading behavior, and web usage data, show that personalized recommendations can be generated using AI algorithms, making it easier for researchers to explore the web-based scientific landscape and discover new opportunities for further research. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

"In webometrics, AI can provide several specific benefits including Web Crawling, Data Collection...

"ওয়েবমেট্রিক্সে, AI ওয়েব ক্রলিং, ডেটা সংগ্রহ সহ বেশ কিছু নির্দিষ্ট সুবিধা প্রদান করতে পারে...

„In der Webometrie kann KI mehrere spezifische Vorteile bieten, darunter Web-Crawling und Datenerfassung …

"En webmetría, la IA puede proporcionar varios beneficios específicos, incluido el rastreo web, la recopilación de datos...

"En webométrie, l'IA peut apporter plusieurs avantages spécifiques, notamment l'exploration du Web, la collecte de données...

"वेबमेट्रिक्स में, एआई वेब क्रॉलिंग, डेटा संग्रह सहित कई विशिष्ट लाभ प्रदान कर सकता है...

「ウェブメトリクスでは、AI は Web クロール、データ収集など、いくつかの具体的なメリットを提供できます...」

"웹 측정학에서 AI는 웹 크롤링, 데이터 수집 등 여러 가지 구체적인 이점을 제공할 수 있습니다.

"Na webometria, a IA pode fornecer vários benefícios específicos, incluindo rastreamento da Web, coleta de dados...

«В вебометрике искусственный интеллект может предоставить несколько конкретных преимуществ, включая веб-сканирование, сбор данных…

"Webometride yapay zeka, Web Taraması, Veri Toplama gibi çeşitli belirli faydalar sağlayabilir...

"Trong webometrics, AI có thể mang lại một số lợi ích cụ thể bao gồm Thu thập thông tin trên web, Thu thập dữ liệu...

“在网络计量学中，人工智能可以提供一些特定的好处，包括网络爬虫、数据收集……

Web crawling is the technique used to collect a huge amount of data from different websites and learn what every webpage on the website is all about. The collected data can help you to retrieve specific information that you need. A web crawler is typically operated by search engines such as Google, Bing, and Yahoo. The goal is to index the content of different websites all over the internet so that they can appear on the search engine result whenever a person tries to find something on the web. Web crawlers can receive a search query and apply a search algorithm to search and provide relevant information in response to the search queries by using search engines. A web crawler is sometimes known as a spider. Note: Why is Web Crawling Important to Data Science Everything we do nowadays generate data, therefore we are data agents. There are around the world that have created data bytes daily. 4.66 billion active internet users 2.5 quintillion The internet has a lot of data that can be used in the Data Science ecosystem to create different solutions that can solve business problems. Web crawling plays a big role in the data science ecosystem to discover and collect data that can be used in a data science project. Many companies rely on a web crawler to collect data about their customers, products, and services on the web. Data science project starts by formulating the business problem to solve and then followed by the second stage of collecting the right data to solve that problem. In this stage, you can use web crawlers to collect the data on the internet that you need for your data science project. Use Cases of Web Crawling in Data Science Projects Web crawling is an integral part of your data science project. The following are some of the use cases of using web crawling in different data science projects. 1. Collect Social Media Data for Sentiment Analysis Many companies use web crawling to collect posts and comments on various social media platforms such as Facebook, Twitter and Instagram. Companies use the collected data to assess how their brand is performing and discover how their products or services are reviewed by their customers, it can be a positive review, negative review or neutral. 2. Collect Financial Data for Stock Prices Forecasting The stock market is full of uncertainty, therefore stock price forecasting is very important in business. Web crawling is used to collect stock prices data from different platforms for different periods (for example 54 weeks, 24 months e.t.c). The stock prices data collected can be analyzed to discover trends and other behaviors. You can also use the data to create predictive models to predict future stock prices. This will help stockbrokers to make decisions for their business. 3. Collect Real Estate data for Price Estimation Evaluating and calculating the price of real estate is time-consuming. Many real-estate companies use data science to create a predictive model to predict the prices of properties by using historical data. These historical data are collected from multiple sources on the web and extracted useful information by using web crawlers. Companies also use these data to support their marketing strategy and make the right decisions. For example, an American online real estate company called Zillow has used data science to determine prices based on a range of publicly available data on the web. How To Build a Web Crawler by Yourself In this part, you will learn the steps required to find and collect data from Twitter using the Python library. Twint is an open-source python package that allows you to collect tweets from Twitter without Twitter’s API. Twint You can collect data from specific users, tweets relating to certain topics, hashtags, and trends. you can fetch almost all Tweets (Twitter API limits to last 3200 Tweets only). The easiest way to install Twint from PyPI is by using : Install Twint pip pip install twint the command above will automatically install all dependencies of Twint. Note: The first step is to import Twint into your Python file or notebook. You will also need to import and initialize net_asyncio to handle runtime errors from the notebook. Import Packages net_asyncio.apply() import twint import nest_asyncio You need to configure the Twint object to find and identify the types of data it will collect from twitter. In this example, Twint will search and extract tweets related to a Google product called “Google Home”. The data collected can be used for sentiment analysis. Configure Twint Object You can also configure the following:- Language of a tweet Number of tweets to collect Specify how you will store the data after being downloaded. For example, save tweets on a CSV file. Configure the name of the CSV file. c = twint.Config() ​​ # Configure c.Search = "google home" c.Lang = "en" c.Limit = 100 c.Store_csv = True c.Output = "/Users/davisdavid/Downloads/google_home_tweets.csv" Finally, you can run the Twint object to extract data from Twitter by using the method. It will extract tweets according to the configuration you have specified. Extract Tweets Data twint.run.Search twint.run.Search(c) # Run In this example, it will extract 100 tweets by using the search query called and save the data in a CSV file called . “Google Home” “google_home_tweets.csv” The CSV file will contain different fields of extracted data search as date, time, timezone, user_id, username, tweet itself, language, hashtags, link of the tweet, geo, and others. Recommended No-Code Web Crawling Tools Sometimes it is not feasible to manually collect data from the web for various reasons such as no time, too slow, or complex configuration. Fortunately, you can use different no-code web crawling tools to automate the collection of data from the web for your data science project. There are so many no-code web crawling tools available to choose but in the section, you will learn the top 3 free no-code tools you can use in your data science project. is a visual software tool that you can use to extract different types of data from the web without writing codes. It also has various features that make it easier to collect data within a short period. 1. Octoparse Octoparse If you are a beginner, Octoparse is the right no-code tool for you because it offers step-by-step instructions that you can follow to configure your own task and collect the data that you want. The free version of Octoparse offers: 2 concurrent tasks on a local machine. 10 crawlers to extract the data you want for your projects. Crawl an unlimited number of pages for your tasks. Extract different types of data such as links, text, and data from list/table pages. Store extracted data to the cloud platform. Download extracted data in CSV, HTML or TXT file. Octoparse is available for both Windows and macOS users. You can click to download Octoparse and start collecting data for your project. here is another easy-to-learn visual web crawling tool that is simple, friendly to use, powerful and flexible to extract data from the web. It offers an easy-to-use interface to set your run and automatically extract millions of data points from any website in minutes. 2. Parsehub Parsehub You can access the extracted data by using API, CSV/Excel files, Google Sheets, and Tableau. The free version of Parsehub offers the following features: Crawl 200 pages per run/task. You can create 5 public projects. You get limited support. Data retention for 14 days. Parsehub is available for Windows,macOS, and Linux users. You can click to download Parsehub and start collecting data for your project. here is a web crawling tool that does not require you to write code and it runs within the browser as an extension. You can use this tool to collect data from the web on an hourly, daily, or weekly basis. It can also automatically export data to Dropbox, Google sheets, or Amazon S3. 3. Webscraper Webscraper Webscraper.io offers the following features. Point and click interface to Configure scraper and extract the data you want. Extract data from dynamic websites with multiple levels of navigation. It can also navigate a website on all levels. Built for the modern web to handle full JavaScript execution, waiting for Ajax requests, pagination, and Page scroll down. Modular selector system to tailor data extraction to different site structures. Download data in CSV, JSON, and XLSX formats. The Webscraper extension is available in both the and . After installation, you should restart the browser to make sure the extension has fully loaded. Chrome store Firefox browser Add-ons Conclusion Data has become the basis to support decision-making in both profit and non-profit organizations. Therefore, web crawling has found its applications in the data science ecosystem and having that in mind, it is definitely recommended to have skills in web crawling if you plan to become a Data Scientist. In this article, you learned what is web crawling and why it plays an integral part in data science. You have also learned recommended no-code web crawling tools to extract data from the web in minutes. If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post! You can also find me on Twitter . @Davis_McDavid

How is Web Crawling Used in Data Science

The terms "web scraping" and "web crawling" are frequently interchanged. After all, aren’t both used for data mining? The truth is that although they are similar, they are not the same. In this article, we'll go through the fundamental distinctions between web crawling and web scraping, and we'll help you determine which one is right for you and your company. What is Web Crawling? Web crawling is essentially the use of an internet bot - also known as crawlers or spiders - that “crawls” the internet and gathers pages and data to index or create collections. In simple terms, a crawler visits a website, reads the page’s content, and then follows all the links within that page to crawl even more pages in order to create entries for search engine indexing. Resulting in a deep search for information extraction. Online crawling is done by well-known search engines like Google and Bing, and this information is used to index websites. How Does Crawling Work? Web crawling uses a spider (or crawling agent) that locates and obtains information from the deeper layers of the World Wide Web by crawling through every nook and cranny of the Internet.
If we were to crawl an eCommerce web page, the procedure would be as follows: The crawler navigates to the URL you specified, also known as a seed URL – . http://example.com 2. It gathers all the data from the page and follows all the links it can find in the navigation menu, the body of the page, and the footer. 3. Product, content, and category pages are discovered. 4. All the data inside the product pages (price, description, title, etc.), content pages, and category pages are gathered and indexed or stored in a database. So web crawlers sift through massive amounts of data and information to find and collect all the data from websites relevant to your project. When to Use a Web Crawler? More technical industries will most likely take advantage of web crawling vs scraping. If you want to look through massive data sets and crawl websites in-depth, you'll want to employ a web crawler. This means that you’ll be able to do what search engines do — go around the web seeking any information and clicking on every link available and indexing data in the same way that Google does, and collecting as much information as possible. The crawler will also sort the pages to organize the data the way you prefer, plus performing other functions that allow users to find what they’re looking for within the database. As you’ll see later on, it is also an essential component of web scraping. Tools for Web Crawling Scrapy is a must-have among the web crawling technologies available on the market. It is a high-performance web crawling and scraping framework that is commonly used for web crawling. It can be used for data mining, monitoring, and automated testing, among other things. Scrapy is rather simple to use if you are familiar with Python. It is compatible with Linux, Mac OS X, and Windows. Scrapy Heritrix is a popular, quick, and scalable Java web crawler that is free and open-source. You can crawl/archive a group of websites in a matter of minutes. It's also built to comply with robots.txt exclusion directives and META robots tags. Heritrix Apache Nutch is a fantastic web crawler software project that you can use to scale out your website. It is particularly well-known for its use in data mining, and it's a JAVA-based cross-platform solution. It is widely used by data analysts, data scientists, application developers, and web text mining engineers for a variety of applications. Apache Nutch What is Web Scraping? Web scraping is a technique for extracting specific data from websites and exporting it to a local workstation in XML, Excel, JSON, or SQL format. The scripts used to automate this process are called web scrapers which can extract data from any website in a fraction of the time a person would, base on the requirements provided. This task automation is quite beneficial for gathering data for machine learning and other purposes. How Does Scraping Work? The first step of web scraping is to request the contents of a specified URL from the target website. In exchange, the scraper receives the desired data in HTML format. Then, the crawler will parse* the HTML DOM to find the data you specified on your scripts using CSS or XPath selectors. Note: *HTML parsing is the process of processing HTML code and extracting relevant information such as the page title, links, headings, and so on. The final step involves downloading and saving the data in a CSV, JSON, or any other database format so that it can be retrieved and utilized manually or in any other software. We can break the web scraping process into four stages: The crawler sends an HTTP request to the server and downloads the target URL’s HTML DOM. 2. Then it will parse the DOM to find the specified elements within the page and ignore the rest of the content. 3. All elements are now extracted. 4. The data is added into the specified database. Of course, scraping one page isn’t exactly the most useful implementation. So we can add an extra step to this process by adding a web crawling functionality to the web scraper to find and follow specific links within the page. This will allow you to scrape an entire product category or catalog within a website using navigation links, for example. When to Use a Web Scraper? If you want to download the data acquired, web scraping is the way to go because it's a more targeted approach if you consider scraping vs crawling. For example, to work out how to place their new product in the market, a corporation might take product details from laptops offered on Amazon by . Using scraping proxies, you can alter the commands and scrape particular information from your target website. After that, you can save the results in a useful format (e.g., JSON, Excel). scraping Amazon’s data In some circumstances, however, web scraping vs web crawling isn’t a debate. In fact, you may want to use both web crawling and scraping to achieve a single goal. By effectively treating them like step one and step two in your process, you may use a crawler to gather vast amounts of data from prominent websites while using a scraper to extract and retrieve the precise data you require from each crawled page. Tools for Web Scraping Cheerio and Puppeteer is a powerful tool for parsing virtually any HTML or XML document implementing a subset of JQuery, designed specifically for the server. It allows you to use CSS and XPath selectors to quickly find elements within the markup, making it a perfect tool for . Cheerio web scraping with Node.js Because it does not render the HTML document nor execute JavaScript files, Cheerio’s performance is blazing fast. However, because of this, it’s not the best choice for scraping dynamic pages. That’s where can shine. By combining Puppeteer’s headless browser manipulation to execute JS, click on links/buttons, scroll down, and more, with the parsing capabilities of Cheerio, you’ll be able to scrape any information you want. Puppeteer Rvest is a library/package (inspired by frameworks like Beautiful Soup ) designed to simpler. Rvest make web scraping in R The beauty of this library is that not only it allows you to scrape specific data from web pages but it can also create high-quality data visualizations, import to several data formats with just a simple command, and manipulate that within the dataframe. Scraper API Scraper API is a sophisticated solution that uses third-party proxies, machine learning, enormous browser farms, and years of statistical data to ensure that bot security tactics never stop you. The web scraping API takes care of proxy rotation, geotargeting, and CAPTCHAs, allowing developers to scrape any page with only one API call. With over 20 million residential IPs scattered over 12 countries and software capable of rendering JavaScript and solving CAPTCHAs, you’re able to scrape massive amounts of data quickly and without the concern of being blocked by any servers. Differences Between Web Scraping and Web Crawling Web Scraping Web Crawling It's a tool for obtaining specific elements or data from web pages It's a tool for indexing web pages These types of scripts are called web scrapers These types of scripts are called web crawlers, spiders, or bots It looks for specific elements within a particular page or set of pages It goes over each and every page, looking for information till the very last line It can be done on a small or large scale It's primarily used in large-scale projects In most circumstances, a Web Scraper disregards robots.txt Robots.txt is always obeyed Web scrapers are mainly using data in retail, marketing, equity search, real estate, and machine learning Search engines mostly use web crawlers to find new websites or web pages, sort the data and provide users with search results All scrapers need a parser to locate the information and a crawler to scrape information at scale Crawlers just necessitate the use of a crawl agent Using Web Crawling and Scraping for Scalability Since crawling and scraping include related activities, it's easy to get them mixed up. However, web scraping and web crawling have drastic differences and are used to their own unique goals. By now, it should be obvious that web scraping is critical to a company's success, whether it's for client acquisition or revenue development. The future of web crawling and web scraping also looks advantageous with high scalability and effective data integration. As the internet becomes the primary source of intelligence for businesses, more publicly available data will be required to scrape in order to gain business insights and stay ahead of the competition. Don’t miss out on the efficiency of gathering data with web crawling and web scraping.

Web Crawling vs Scraping: What's the Difference Between Crawlers and Scrapers?

What's up Hackers! In this tutorial we are going to build price tracker application which will notify us for discounts. Creating complex projects is the key to learn fast! If you are following me for a while, you already know I like complex stuff, so we are going to use RabbitMQ + Celery + BeautifulSoup + Django for creating this app. Alright! Let's Start! Celery is the best choice for doing background task processing in the Python/Django ecosystem. It has a simple and clear API, and it integrates beautifully with Django. So, we are using Celery to handle the time-consuming tasks by passing them to queue to be executed in the background and always keep the server ready to respond to new requests. Celery requires a solution to send and receive messages; usually this comes in the form of a separate service called a message broker. We will be configuring celery to use the RabbitMQ messaging system, as it provides robust, stable performance and interacts well with celery. We can install RabbitMQ through Ubuntu’s repositories by following command: sudo apt- install rabbitmq- get server Then enable and start the RabbitMQ service: sudo systemctl enable rabbitmq-server
sudo systemctl start rabbitmq-server Well, create a new Django project named pricetracker and app named tracker Install RabbitMQ on Mac Install following dependencies: pip3 install beautifulsoup4 httplib2 Celery Once installation completed, add the CELERY_BROKER_URL configuration to the settings.py file: CELERY_BROKER_URL = 'amqp://localhost' Then, create celery.py inside your project. celery.py os celery Celery

os.environ.setdefault( , )

app = Celery( )
app.config_from_object( , namespace= )
app.autodiscover_tasks() import from import 'DJANGO_SETTINGS_MODULE' 'pricetracker.settings' 'pricetracker' 'django.conf:settings' 'CELERY' We are setting the default Django settings module for the 'celery' program and loading task modules from all registered Django app configs. Now inside your import the celery: __init__.py .celery app celery_app

__all__ = [ ] from import as 'celery_app' This will make sure our Celery app loaded every time Django starts. Now, Let's create our model models.py django.db models title = models.CharField(max_length= )
    url = models.CharField(max_length= )
    requested_price = models.IntegerField(default= )
    last_price = models.IntegerField(null= , blank= )
    discount_price = models.CharField(max_length= , null= , blank= )
    date = models.DateTimeField(auto_now_add= ) self.title from import : class Item (models.Model) 200 600 0 True True 100 True True True : def __str__ (self) return We are going to crawl . User will enter URL of specific item and requested price. eBay So, let's create a form for that: forms.py django forms = forms.CharField(max_length= )
    requested_price = forms.IntegerField() from import ( . ): class AddNewItemForm forms Form url 600 We will use BeautifulSoup to crawl price and title of item in given URL. After data crawled we have to convert price to float and create new object in database. views.py urllib.request urlopen, Request bs4 BeautifulSoup

def crawl_data(url):
    # User Agent is to prevent Forbidden req = Request(url, headers={ : })
    html = urlopen(req).read()
    bs = BeautifulSoup(html, )

    title = bs.find( , id= ).get_text().replace( , )
    price = bs.find( , id= ).get_text()
    clean_price = float(price.strip().replace( , ).replace( , )) { : title, :clean_price } from import from import 403 Error 'User-Agent' 'Mozilla/5.0' 'html.parser' 'h1' "itemTitle" "Details about" "" 'span' "prcIsum" "US" "" "$" "" return 'title' 'last_price' removes spaces at the beginning and at the end of the string and method replaces a specified phrase with another specified phrase. So, we used these methods to get clean price and title of item. strip() replace() Once data crawled successfully, it is time to create new object in database. We will use this function in form submission to crawl title and price of new item. views.py django.shortcuts render, get_object_or_404,HttpResponseRedirect .models Item .forms AddNewItemForm

def tracker_view(request):
    items = Item.objects.order_by( )
    form = AddNewItemForm(request.POST) request.method == : form.is_valid():
            url = form.cleaned_data.get( )
            requested_price = form.cleaned_data.get( )
            # crawling the data 
            crawled_data = crawl_data(url)
            Item.objects.create(
            url = url,
            title = crawled_data[ ],
            requested_price=requested_price,
            last_price=crawled_data[ ],
            discount_price= ,
            ) HttpResponseRedirect( ) :
            form = AddNewItemForm()
    context = { :items, :form,
    } render(request, , context) from import from import from import '-id' if 'POST' if 'url' 'requested_price' 'title' 'last_price' 'No Discount Yet' return '' else 'items' 'form' return 'tracker.html' Great! Now, we need to crawl the data for all objects continuously to be aware of discounts. If we do this without celery the server connection will timeout which means that a server is taking too long to reply to a data request and our application will crash. Create in your app and let's handle it with celery tasks. tasks.py time celery shared_task .models Vehicle tracker.views crawl_data

@shared_task
# something heavy
def track_for_discount():
    items = Item.objects.all() item items:
        # crawl item url
        data = crawl_data(item.url)
        # check discount data[ ] < item.requested_price:
            print(f )
            # update discount field to notify user
            item_discount = Item.objects.get(id=item.id)
            item_discount.discount_price = f item_discount.save() True:
    track_for_discount()
    time.sleep( ) import from import from import from import do for in for if 'last_price' 'Discount for {data["title"]}' 'DISCOUNT! The price is {data["last_price"]}' while 15 will create the independent instance of the task for each app, making task reusable. This makes the decorator useful for libraries and reusable apps, since they will not have access to the app of the user. @shared_task @shared_task We are simply crawling data every 15 seconds and comparing last price with requested price. If last price is smaller than requested price then we are updating the discount price field. What if price will increase again? @shared_task
def track_for_not_discount():
    items = Item.objects.all() item items:
        data = crawl_data(item.url) data[ ] > item.requested_price:
            print(f )
            item_discount_finished = Item.objects.get(id=item.id)
            item_discount_finished.discount_price = item_discount_finished.save() for in if "last_price" 'Discount finished for {data["title"]}' 'No Discount Yet' Great! Now, it will possible to track discounts properly. You can add one more function which will detect closer prices and notify user about it. For instance, if item price is 100$ and requested price is 97$. But let's keep it simple for now. Finally, we can create our template. tracker.html {% extends 'base.html' %}

{% block content %} {% csrf_token %}
    {{form.as_p}} Send Title Requested Price Last Price Discount Price Date Created {% for item in items %} {{item.title}} {{item.requested_price}} {{item.last_price}} {{item.discount_price}} {{item.date}} {% endfor %} {% endblock %} < = > form method "POST" < = = > button class "btn btn-primary" type "submit" </ > button </ > form < = > table class "table" < > thead < > tr < = > th scope "col" </ > th < = > th scope "col" </ > th < = > th scope "col" </ > th < = > th scope "col" </ > th < = > th scope "col" </ > th </ > tr </ > thead < > tbody < > tr < > td </ > td < > td </ > td < > td </ > td < > td </ > td < > td </ > td </ > tr </ > tbody </ > table Well, you can improve the project by adding email functionality so Django will send email about discounts. Take a look How to Send Email in a Django App You can clone this project from my GitHub repository below https://github.com/raszidzie/Price-Tracker-Application Mission Accomplished! That's it! Make sure you are following me on social media and see you in next post soon Hackers! Stay Connected!

Price Tracker Application with Django:  Crawling Discounts From Ebay

In Kubernetes, Ingress resources are frequently used as traffic controllers, providing external access to services within the cluster. Ingress is essential for routing incoming traffic to your service; however, there may be scenarios in which you want to prevent search engines from indexing your service's content: it might be a development environment or something else. This blog post will walk you through the process of blocking your site's indexing on Kubernetes Ingress using a robots.txt file, preventing search engine bots from crawling and indexing your content. Prerequisites To proceed with the tutorial, you should have a basic grasp of Kubernetes basic objects, Ingress resources, and the official HAProxy ingress controller. You will also need access to the Kubernetes cluster and the necessary permissions to make configuration changes. Keep in mind that for this article, I assume that the HAProxy ingress controller is set as the default controller. Otherwise, if you did not select HAProxy as the default controller, you must add the ingressClassName option to all Ingress code examples. Step 1: Create an Ingress Kubernetes Resource In the first part of our journey, we'll set up a small Ingress resource to expose our service outside of the Kubernetes cluster. Pay attention: for the time being, all web crawlers will have access to the service. To apply the code below, use the command kubectl apply -f ingress.yaml. # file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-close-site-indexing-haproxy-example
 annotations:
   cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
 rules:
   - host: hackernoon-close-site-indexing-haproxy-example.referrs.me
     http:
       paths:
         - path: /
           pathType: Prefix
           backend:
             service:
               name: hackernoon-close-site-indexing-haproxy-example-service
               port:
                 number: 80
 tls:
   - hosts:
       - hackernoon-close-site-indexing-haproxy-example.referrs.me
     secretName: hackernoon-close-site-indexing-haproxy-example Step 2: Modify the Ingress Configuration The robots.txt file is used to control how search engines index documents. Its file specifies which URLs search engine crawlers can access on your website. The most basic file that restricts access to the web service looks like this: User-agent: *
Disallow: / HAProxy does not require you to add this file to your web server or website. This can be achieved with the following configuration, which should be added to the backend section for the specific group of servers: acl robots_path path_beg /robots.txt
http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path K8S Annotations regulate all manipulations of the HAProxy frontend/backend configuration for a single ingress resource. The full list of HAProxy annotations can be found in the official documentation on GitHub. In our case, we need to use haproxy.org/backend-config-snippet with the HAProxy snippet for blocking any indexing. To do this, edit, open your Ingress resource YAML file, and add the following annotation to the metadata section: #file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-avoid-indexing-haproxy-example
 annotations:
   …
    haproxy.org/backend-config-snippet: |
      acl robots_path path_beg /robots.txt
      http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path
spec:
  ... Step 3: Apply the Configuration Changes After changing the Ingress YAML file, save it and apply it to the Kubernetes cluster with the kubectl command: kubectl apply -f ingress.yaml. The Ingress controller will detect the changes and update the configuration accordingly. Step 4: Verify the Configuration Inspect the generated robots.txt file to confirm that indexing prevention is working properly. The Ingress controller generates this file based on the annotation you supply. Retrieve the external IP or domain associated with your Ingress resource and add /robots.txt to the URL. Example: $ curl hackernoon-close-site-indexing-haproxy-example.referrs.me/robots.txt
User-agent: *
Disallow: / As we can see, the answer contains a robots.txt file that prevents any search indexing. Step 5: Test Indexing Prevention To verify that search engines are not indexing your site, you can run a search for your website on popular search engines. Keep in mind that search results sometimes take some time to reflect changes, so the indexing status may not be fully updated right away. Conclusion Annotations make it easy to avoid search engine indexing while using HAProxy Kubernetes Ingress. By adding the appropriate annotation to your Ingress resource, you can prohibit search engine bots from crawling and indexing your website's content. A similar approach can be used with other ingress controllers, such as Nginx, Traefic, and others. A similar annotation can also be used for K8S Gateway API resources, which are actively replacing Ingresses. As a final note, robots.txt is a time-honored way for website creators to specify whether or not their sites should be crawled by various bots. However, it turns out that AI crawlers from large language model (LLM) companies frequently ignore the contents of robots.txt and crawl your site regardless. To avoid such situations, utilize password security, noindex, or enterprise load balancer features like HAProxy AI-crawler, which may be also configured as a K8S annotation. In Kubernetes, Ingress resources are frequently used as traffic controllers, providing external access to services within the cluster. Ingress is essential for routing incoming traffic to your service; however, there may be scenarios in which you want to prevent search engines from indexing your service's content: it might be a development environment or something else. This blog post will walk you through the process of blocking your site's indexing on Kubernetes Ingress using a robots.txt file, preventing search engine bots from crawling and indexing your content. Prerequisites To proceed with the tutorial, you should have a basic grasp of Kubernetes basic objects, Ingress resources, and the official HAProxy ingress controller . You will also need access to the Kubernetes cluster and the necessary permissions to make configuration changes. Kubernetes official HAProxy ingress controller Keep in mind that for this article, I assume that the HAProxy ingress controller is set as the default controller. Otherwise, if you did not select HAProxy as the default controller, you must add the ingressClassName option to all Ingress code examples. ingressClassName Step 1: Create an Ingress Kubernetes Resource In the first part of our journey, we'll set up a small Ingress resource to expose our service outside of the Kubernetes cluster. Pay attention: for the time being, all web crawlers will have access to the service. To apply the code below, use the command kubectl apply -f ingress.yaml . kubectl apply -f ingress.yaml # file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-close-site-indexing-haproxy-example
 annotations:
   cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
 rules:
   - host: hackernoon-close-site-indexing-haproxy-example.referrs.me
     http:
       paths:
         - path: /
           pathType: Prefix
           backend:
             service:
               name: hackernoon-close-site-indexing-haproxy-example-service
               port:
                 number: 80
 tls:
   - hosts:
       - hackernoon-close-site-indexing-haproxy-example.referrs.me
     secretName: hackernoon-close-site-indexing-haproxy-example # file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-close-site-indexing-haproxy-example
 annotations:
   cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
 rules:
   - host: hackernoon-close-site-indexing-haproxy-example.referrs.me
     http:
       paths:
         - path: /
           pathType: Prefix
           backend:
             service:
               name: hackernoon-close-site-indexing-haproxy-example-service
               port:
                 number: 80
 tls:
   - hosts:
       - hackernoon-close-site-indexing-haproxy-example.referrs.me
     secretName: hackernoon-close-site-indexing-haproxy-example Step 2: Modify the Ingress Configuration The robots.txt file is used to control how search engines index documents. Its file specifies which URLs search engine crawlers can access on your website. robots.txt The most basic file that restricts access to the web service looks like this: User-agent: *
Disallow: / User-agent: *
Disallow: / HAProxy does not require you to add this file to your web server or website. This can be achieved with the following configuration, which should be added to the backend section for the specific group of servers: acl robots_path path_beg /robots.txt
http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path acl robots_path path_beg /robots.txt
http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path K8S Annotations regulate all manipulations of the HAProxy frontend/backend configuration for a single ingress resource. The full list of HAProxy annotations can be found in the official documentation on GitHub . in the official documentation on GitHub In our case, we need to use haproxy.org/backend-config-snippet with the HAProxy snippet for blocking any indexing. To do this, edit, open your Ingress resource YAML file, and add the following annotation to the metadata section: haproxy.org/backend-config-snippet #file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-avoid-indexing-haproxy-example
 annotations:
   …
    haproxy.org/backend-config-snippet: |
      acl robots_path path_beg /robots.txt
      http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path
spec:
  ... #file: ingress.yaml
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
 name: hackernoon-avoid-indexing-haproxy-example
 annotations:
   …
    haproxy.org/backend-config-snippet: |
      acl robots_path path_beg /robots.txt
      http-request return status 200 content-type "text/plain" lf-string "User-agent: *\nDisallow: /\n" if robots_path
spec:
  ... Step 3: Apply the Configuration Changes After changing the Ingress YAML file, save it and apply it to the Kubernetes cluster with the kubectl command: kubectl apply -f ingress.yaml . The Ingress controller will detect the changes and update the configuration accordingly. kubectl apply -f ingress.yaml Step 4: Verify the Configuration Inspect the generated robots.txt file to confirm that indexing prevention is working properly. The Ingress controller generates this file based on the annotation you supply. Retrieve the external IP or domain associated with your Ingress resource and add /robots.txt to the URL. Example: $ curl hackernoon-close-site-indexing-haproxy-example.referrs.me/robots.txt
User-agent: *
Disallow: / $ curl hackernoon-close-site-indexing-haproxy-example.referrs.me/robots.txt
User-agent: *
Disallow: / As we can see, the answer contains a robots.txt file that prevents any search indexing. robots.txt Step 5: Test Indexing Prevention To verify that search engines are not indexing your site, you can run a search for your website on popular search engines. Keep in mind that search results sometimes take some time to reflect changes, so the indexing status may not be fully updated right away. Conclusion Annotations make it easy to avoid search engine indexing while using HAProxy Kubernetes Ingress. By adding the appropriate annotation to your Ingress resource, you can prohibit search engine bots from crawling and indexing your website's content. A similar approach can be used with other ingress controllers, such as Nginx, Traefic, and others. A similar annotation can also be used for K8S Gateway API resources, which are actively replacing Ingresses. K8S Gateway API As a final note, robots.txt is a time-honored way for website creators to specify whether or not their sites should be crawled by various bots. However, it turns out that AI crawlers from large language model (LLM) companies frequently ignore the contents of robots.txt and crawl your site regardless. To avoid such situations, utilize password security, noindex, or enterprise load balancer features like HAProxy AI-crawler , which may be also configured as a K8S annotation. HAProxy AI-crawler

How to Block Search Engine Indexing in Kubernetes with HAProxy

Artificial Intelligence is not merely transforming industries but reconstituting the fundamental infrastructure that underpins our digital realm. Grandview Research indicates that AI is poised to transform multiple industries, with a projected annual growth rate of 36.6% from 2023 to 2030, achieving a whopping $1,811.75 billion. However, as AI applications become more intricate and data-driven, the need for scalable, efficient, and accessible computing solutions has surged in 2024. Koii stands out as a pioneer in this revolution, offering a decentralized physical infrastructure network (DePIN) that employs community-driven computing with a strong emphasis on AI capabilities.To learn more about this innovative platform, I have the honor of speaking with Al Morris, the founder of Koii and an early researcher among Ethereum's cofounders. We’ll examine the story behind this platform, its novel approach to decentralized computing, and potential developments that could revolutionize the AI and decentralized landscape. Let’s dive in! Ade: Can you introduce yourself and tell us about the origins of Koii? As a research analyst under one of the early Ethereum founders, what inspired you to establish this platform? Al Morris: In 2015, I got a glimpse of the future with AI while I was working as an industrial consultant, automating factories. I saw the writing on the wall for white-collar work, too, and wanted to find a way to include everyone in the future, so I became very interested in distributed networks like Ethereum. I started attending and helping organize meetups in Chicago, and quickly, I was recruited to help launch the Blockchain Institute, one of the first research schools for decentralization. After going deep on many of the leading protocols, in 2020, I noticed a massive gap in the market- no one was onboarding consumer devices directly. In 2017 and on I had watched how over-reliance on custom hardware was putting a lot of sell pressure on new token launches, and with a focus on making the industry more economically self-sustaining, I proposed Koii as an alternative solution. In 2021 we launched the first prototype of the network, which I built from a cabin in the woods in Canada during COVID, and we quickly assembled a team of dedicated people who wanted to use this network to begin archiving the internet, a task we decided on as a good test for our new compute network. Since then, things have grown remarkably, and what was once an R&D project has become a global network of over 90,000 community members, now boasting advanced technology for launching new crypto-backed communities. Ade: Koii has established itself as a platform centered around artificial intelligence. Could you explain how Koii's decentralized physical infrastructure network (DePIN) model promotes AI development and the benefits it offers compared to conventional cloud services? Al Morris: The biggest moat that exists in the AI industry is hardware. The leaders in the space (OpenAI, Anthropic, etc.) have built their products by first crawling the web and then running vast models on the enormous computing capacity. Once the models are trained, hosting them for user queries requires yet again more computing resources. This is why Sam Altman has famously said that we need new Nuclear Reactors and $$$ billions of dollars of GPUs to get the job done. Koii is the first network that crowdsourced consumer devices, providing a massive underutilized resource that can provide for this enormous hardware need without the major electrical load overhead. Koii is distributed across thousands of devices in the homes of our community, and grows every time a new person installs the app. Ade: Koii possesses a remarkable network of nodes and computational resources. Can you provide an analysis of the current state of the Koii network regarding nodes, bandwidth, computational capacity, and storage? Al Morris: Every few minutes, someone tells a friend about Koii. It's been that way most of this year, which means we have a rapid and consistent flow of new devices joining the network. Most have fiber optic bandwidth, and they're often quite high-capacity devices. One major advantage of Koii is the self sovereignty of the nodes, which allows them to also participate as data aggregators across social networks and other systems that a node operator might have access to, so it's about a lot more than just the compute capacity. Ade: Koii provides a range of tools. Can you describe the functions of Koii Data Services and Orca in the artificial intelligence development process? Al Morris: The big advantage of Koii is to incorporate many devices as one large cloud, a service we offer as an autoscaling kubernetes environment. The standard Orca environment on the node provides a docker runtime and allows the use of custom tokens for rewards and penalties, helping ensure that compute providers are accountable. Ade: Koii has collaborated with multiple companies to establish the Koii Compute Alliance. What benefits does this alliance provide for AI developers? Al Morris: One of the best things about distributed computing is that you don't need to pay up front, you can issue your own token as rewards and people will trade their capacity for a slice of the future pie. The bad thing about this, however, is that it can sometimes mean there isn't a lot of supply for new projects, who might not have the trust of the node operators. In these cases, node operators typically Ade: The Koiisador Program was launched on September 24th. What benefits and opportunities are available to participants? Al Morris: Koii Ambassadors, known as Koiisadors, form the elite core of the Koii community, combining top tech talent with strategic initiative. Participants start as Trial Koiisadors, contributing for 8 weeks before advancing to full or Senior Koiisador roles. They collaborate closely with the Koii team, earn KOII tokens based on their contributions, and lead key initiatives while driving growth. Additional benefits include exclusive access to the core team, special perks on Telegram and Discord, and unique Koiisador merchandise. Ade: What future advancements or milestones can we anticipate from Koii, specifically concerning AI capabilities? Al Morris: Anyone, anywhere in the world should have access to AI. It's a necessity in 2024, and it will be required for survival in 2030. The compute resources required can be expensive, so we hope to offer revenue sharing to enable individuals easy access to these networks. The key is to build together, sharing pooled resources, and sharing profits as well. Tokens are the key to all of this. By the end of 2024, anyone in the world will be able to launch token-backed AI on Koii, allowing them to tap into thousands of devices, and automate revenue sharing via token distributions. This future is now, and anyone can help. The upcoming release of our EZWay web app will allow anyone, no matter their technical abilities, to launch tokens, build products, and capture markers using distributed computing. Artificial Intelligence is not merely transforming industries but reconstituting the fundamental infrastructure that underpins our digital realm. Artificial Intelligence is not merely transforming industries but reconstituting the fundamental infrastructure that underpins our digital realm. Grandview Research indicates that AI is poised to transform multiple industries, with a projected annual growth rate of 36.6% from 2023 to 2030, achieving a whopping $1,811.75 billion. However, as AI applications become more intricate and data-driven, the need for scalable, efficient, and accessible computing solutions has surged in 2024. Grandview Research Grandview Research Koii stands out as a pioneer in this revolution, offering a decentralized physical infrastructure network (DePIN) that employs community-driven computing with a strong emphasis on AI capabilities. To learn more about this innovative platform, I have the honor of speaking with Al Morris, the founder of Koii and an early researcher among Ethereum's cofounders. We’ll examine the story behind this platform, its novel approach to decentralized computing, and potential developments that could revolutionize the AI and decentralized landscape. Let’s dive in! Ade: Can you introduce yourself and tell us about the origins of Koii? As a research analyst under one of the early Ethereum founders, what inspired you to establish this platform? Ade: Can you introduce yourself and tell us about the origins of Koii? As a research analyst under one of the early Ethereum founders, what inspired you to establish this platform? Al Morris: In 2015, I got a glimpse of the future with AI while I was working as an industrial consultant, automating factories. I saw the writing on the wall for white-collar work, too, and wanted to find a way to include everyone in the future, so I became very interested in distributed networks like Ethereum. I started attending and helping organize meetups in Chicago, and quickly, I was recruited to help launch the Blockchain Institute, one of the first research schools for decentralization. After going deep on many of the leading protocols, in 2020, I noticed a massive gap in the market- no one was onboarding consumer devices directly. Al Morris: In 2017 and on I had watched how over-reliance on custom hardware was putting a lot of sell pressure on new token launches, and with a focus on making the industry more economically self-sustaining, I proposed Koii as an alternative solution. In 2021 we launched the first prototype of the network, which I built from a cabin in the woods in Canada during COVID, and we quickly assembled a team of dedicated people who wanted to use this network to begin archiving the internet, a task we decided on as a good test for our new compute network. Since then, things have grown remarkably, and what was once an R&D project has become a global network of over 90,000 community members, now boasting advanced technology for launching new crypto-backed communities. Ade: Koii has established itself as a platform centered around artificial intelligence. Could you explain how Koii's decentralized physical infrastructure network (DePIN) model promotes AI development and the benefits it offers compared to conventional cloud services? Ade: Koii has established itself as a platform centered around artificial intelligence. Could you explain how Koii's decentralized physical infrastructure network (DePIN) model promotes AI development and the benefits it offers compared to conventional cloud services? Al Morris: The biggest moat that exists in the AI industry is hardware. The leaders in the space (OpenAI, Anthropic, etc.) have built their products by first crawling the web and then running vast models on the enormous computing capacity. Once the models are trained, hosting them for user queries requires yet again more computing resources. This is why Sam Altman has famously said that we need new Nuclear Reactors and $$$ billions of dollars of GPUs to get the job done. Al Morris: Koii is the first network that crowdsourced consumer devices, providing a massive underutilized resource that can provide for this enormous hardware need without the major electrical load overhead. Koii is distributed across thousands of devices in the homes of our community, and grows every time a new person installs the app. Ade: Koii possesses a remarkable network of nodes and computational resources. Can you provide an analysis of the current state of the Koii network regarding nodes, bandwidth, computational capacity, and storage? Ade: Koii possesses a remarkable network of nodes and computational resources. Can you provide an analysis of the current state of the Koii network regarding nodes, bandwidth, computational capacity, and storage? Al Morris: Every few minutes, someone tells a friend about Koii. It's been that way most of this year, which means we have a rapid and consistent flow of new devices joining the network. Most have fiber optic bandwidth, and they're often quite high-capacity devices. Al Morris: One major advantage of Koii is the self sovereignty of the nodes, which allows them to also participate as data aggregators across social networks and other systems that a node operator might have access to, so it's about a lot more than just the compute capacity. Ade: Koii provides a range of tools. Can you describe the functions of Koii Data Services and Orca in the artificial intelligence development process? Ade: Koii provides a range of tools. Can you describe the functions of Koii Data Services and Orca in the artificial intelligence development process? Al Morris: The big advantage of Koii is to incorporate many devices as one large cloud, a service we offer as an autoscaling kubernetes environment. Al Morris: The standard Orca environment on the node provides a docker runtime and allows the use of custom tokens for rewards and penalties, helping ensure that compute providers are accountable. Ade: Koii has collaborated with multiple companies to establish the Koii Compute Alliance. What benefits does this alliance provide for AI developers? Ade: Koii has collaborated with multiple companies to establish the Koii Compute Alliance. What benefits does this alliance provide for AI developers? Al Morris: One of the best things about distributed computing is that you don't need to pay up front, you can issue your own token as rewards and people will trade their capacity for a slice of the future pie. Al Morris: The bad thing about this, however, is that it can sometimes mean there isn't a lot of supply for new projects, who might not have the trust of the node operators. In these cases, node operators typically Ade: The Koiisador Program was launched on September 24th. What benefits and opportunities are available to participants? Ade: The Koiisador Program was launched on September 24th. What benefits and opportunities are available to participants? Al Morris: Koii Ambassadors, known as Koiisadors, form the elite core of the Koii community, combining top tech talent with strategic initiative. Participants start as Trial Koiisadors, contributing for 8 weeks before advancing to full or Senior Koiisador roles. They collaborate closely with the Koii team, earn KOII tokens based on their contributions, and lead key initiatives while driving growth . Additional benefits include exclusive access to the core team, special perks on Telegram and Discord, and unique Koiisador merchandise. Al Morris: driving growth driving growth Ade: What future advancements or milestones can we anticipate from Koii, specifically concerning AI capabilities? Ade: What future advancements or milestones can we anticipate from Koii, specifically concerning AI capabilities? Al Morris: Anyone, anywhere in the world should have access to AI. It's a necessity in 2024, and it will be required for survival in 2030. Al Morris: The compute resources required can be expensive, so we hope to offer revenue sharing to enable individuals easy access to these networks. The key is to build together, sharing pooled resources, and sharing profits as well. Tokens are the key to all of this. By the end of 2024, anyone in the world will be able to launch token-backed AI on Koii, allowing them to tap into thousands of devices, and automate revenue sharing via token distributions. This future is now, and anyone can help. The upcoming release of our EZWay web app will allow anyone, no matter their technical abilities, to launch tokens, build products, and capture markers using distributed computing.

The AI-Powered DePIN Model for Decentralized Computing: Interview with Al Morris, Founder Koii

I boosted the Domain Rating of gone.domains from 0 to 26 in just a few months. I found and bought 5 trusted expired domains with backlinks, then set up 301 redirects to my main site. It’s important to make sure the expired domains are in the same niche as your main site. How much it cost? I bought these expired domains and spent a total of $26. Here is a list of the purchased domains along with their metrics. – athulcyriac.xyz – keywordking.co – expiredomains.today – google-colab.com – datadrifter.xyz How I found these domains? I created a service that crawls popular and trusted websites to find expired domains. Right now, I’m crawling the DEV.TO website and have already found over 5,000 expired domains from it. Keep in mind that Domain Rating is just one of many SEO metrics. While it’s important for SEO, Google doesn’t use DR directly to rank websites. I also share tips about domains, SEO, promotion, and marketing on my Twitter, so feel free to follow me for regular updates! I boosted the Domain Rating of gone.domains from 0 to 26 in just a few months. gone.domains I found and bought 5 trusted expired domains with backlinks, then set up 301 redirects to my main site. It’s important to make sure the expired domains are in the same niche as your main site. How much it cost? I bought these expired domains and spent a total of $26. Here is a list of the purchased domains along with their metrics. – athulcyriac.xyz athulcyriac.xyz – keywordking.co keywordking.co – expiredomains.today expiredomains.today – google-colab.com google-colab.com – datadrifter.xyz datadrifter.xyz How I found these domains? I created a service that crawls popular and trusted websites to find expired domains . to find expired domains Right now, I’m crawling the DEV.TO website and have already found over 5,000 expired domains from it. DEV.TO Keep in mind that Domain Rating is just one of many SEO metrics. While it’s important for SEO, Google doesn’t use DR directly to rank websites. I also share tips about domains, SEO, promotion, and marketing on my Twitter , so feel free to follow me for regular updates! on my Twitter

How I Increased My Website's Domain Rating From 0 to 26 in a Few Months

In recent weeks, webmasters have raised urgent concerns regarding Facebook's aggressive crawling practices, particularly highlighting the behavior of its user agent, “facebookexternalhit.” Many site owners report that these bots are creating significant strain on their web hosting servers, leading to alarming spikes in traffic that threaten site reliability. Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. One webmaster recounted their situation, said, “Our website gets hammered every 45 to 60 minutes with spikes of approximately 400 requests per second from 20 to 30 different IP addresses within Facebook’s netblocks. Between these spikes, the traffic is manageable, but the sudden load is risky.” This sentiment resonates with many, as webmasters are advocating for a more balanced distribution of requests from Facebook’s bots, akin to the behavior exhibited by Googlebot and other reputable search engine crawlers. The consequences of these excessive requests extend beyond mere inconvenience; they disrupt the user experience and lead to costly resource consumption for site owners. Smaller websites, in particular, have found themselves severely impacted. In response to the relentless onslaught, some webmasters have taken proactive measures by implementing stricter rules in their robots.txt files to shield their servers from the overwhelming traffic. However, because Facebook’s bot functions as a scraper rather than a traditional crawler, it disregards these instructions, further complicating the situation. This growing issue has sparked widespread discussions within the web development community, with experts urging Facebook to reconsider its crawling strategies. The collective voice of these webmasters underscores a critical need for a more sustainable approach to web scraping and crawling practices. In a bid to manage the excessive requests, many webmasters are turning to tools like Cloudflare, which provides robust features for managing traffic and implementing rate limiting. By configuring a rate-limiting WAF rule, webmasters can effectively throttle the number of requests originating from Facebook’s bots, alleviating server strain during peak traffic periods. One webmaster expressed their perspective on the necessity of a balanced approach, stating, “I don’t want to block the bot entirely, but the current pattern is unsustainable. Using Cloudflare’s rate limiting has allowed us to protect our site while still enabling Facebook to access our content for link previews.” Concerns regarding Facebook’s crawling practices have been echoed on various platforms. In a post on Cloudflare, one user articulated their frustrations: “I am writing to express my concern about the excessive crawling activity of Facebook’s crawler. “This excessive crawling is causing significant performance issues and potential downtime for our website.” They went on to detail, “Our web server logs indicate that Facebook’s crawler (facebookexternalhit/1.1 – 2a03:2880:22ff:7::face is making multiple requests to our WordPress website every second, even during off-peak hours,” “During peak hours, the crawler’s activity spikes to tens of thousands of requests per minute. This excessive crawling is overwhelming our servers and causing them to slow down or even crash.” While webmasters recognize the necessity of Facebook’s crawler for indexing purposes and making their content accessible to users, they firmly believe that the current level of crawling is excessive and unreasonable. As a result, many remain vigilant, closely monitoring their server performance and adjusting settings to mitigate the challenges posed by Facebook’s bots. The unfolding situation highlights a critical juncture for the web development community, with potential implications for how major tech companies manage web scraping and crawling practices in the future. As webmasters advocate for a more equitable solution, the outcome of this discussion could set important precedents in the industry, influencing the relationship between webmasters and tech giants moving forward. In recent weeks, webmasters have raised urgent concerns regarding Facebook's aggressive crawling practices , particularly highlighting the behavior of its user agent, “facebookexternalhit.” Many site owners report that these bots are creating significant strain on their web hosting servers, leading to alarming spikes in traffic that threaten site reliability. Facebook's aggressive crawling practices Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. One webmaster recounted their situation, said, “Our website gets hammered every 45 to 60 minutes with spikes of approximately 400 requests per second from 20 to 30 different IP addresses within Facebook’s netblocks. Between these spikes, the traffic is manageable, but the sudden load is risky.” This sentiment resonates with many, as webmasters are advocating for a more balanced distribution of requests from Facebook’s bots , akin to the behavior exhibited by Googlebot and other reputable search engine crawlers. Facebook’s bots The consequences of these excessive requests extend beyond mere inconvenience; they disrupt the user experience and lead to costly resource consumption for site owners. Smaller websites, in particular, have found themselves severely impacted. In response to the relentless onslaught, some webmasters have taken proactive measures by implementing stricter rules in their robots.txt files to shield their servers from the overwhelming traffic. However, because Facebook’s bot functions as a scraper rather than a traditional crawler, it disregards these instructions, further complicating the situation. This growing issue has sparked widespread discussions within the web development community, with experts urging Facebook to reconsider its crawling strategies. The collective voice of these webmasters underscores a critical need for a more sustainable approach to web scraping and crawling practices. In a bid to manage the excessive requests, many webmasters are turning to tools like Cloudflare, which provides robust features for managing traffic and implementing rate limiting. By configuring a rate-limiting WAF rule, webmasters can effectively throttle the number of requests originating from Facebook’s bots, alleviating server strain during peak traffic periods. One webmaster expressed their perspective on the necessity of a balanced approach, stating, “I don’t want to block the bot entirely, but the current pattern is unsustainable. Using Cloudflare’s rate limiting has allowed us to protect our site while still enabling Facebook to access our content for link previews.” Concerns regarding Facebook’s crawling practices have been echoed on various platforms. In a post on Cloudflare, one user articulated their frustrations: “I am writing to express my concern about the excessive crawling activity of Facebook’s crawler. “This excessive crawling is causing significant performance issues and potential downtime for our website.” They went on to detail, “Our web server logs indicate that Facebook’s crawler (facebookexternalhit/1.1 – 2a03:2880:22ff:7::face is making multiple requests to our WordPress website every second, even during off-peak hours,” “During peak hours, the crawler’s activity spikes to tens of thousands of requests per minute. This excessive crawling is overwhelming our servers and causing them to slow down or even crash.” While webmasters recognize the necessity of Facebook’s crawler for indexing purposes and making their content accessible to users, they firmly believe that the current level of crawling is excessive and unreasonable. As a result, many remain vigilant, closely monitoring their server performance and adjusting settings to mitigate the challenges posed by Facebook’s bots. The unfolding situation highlights a critical juncture for the web development community, with potential implications for how major tech companies manage web scraping and crawling practices in the future. As webmasters advocate for a more equitable solution, the outcome of this discussion could set important precedents in the industry, influencing the relationship between webmasters and tech giants moving forward.

FaceBook Bots, Crawlers And User Agents Causing Resource Drains On Websites And Hosting Accounts

A brief understanding of the difference between C# and JavaScript web scraping As a compiled language, C# provides a wealth of libraries and frameworks, such as HtmlAgilityPack, HttpClient, etc., which facilitate the implementation of complex web crawling logic, and the code is concise and efficient, with strong debugging and error handling capabilities. At the same time, C# has good cross-platform support and is suitable for a variety of operating systems. However, the learning curve of C# may be relatively steep and requires a certain programming foundation. In contrast, JavaScript, as a scripting language, is more flexible in web crawling and can be run directly in the browser without the need for additional installation environment. JavaScript has a rich DOM operation API, which is convenient for direct operation of web page elements. In addition, JavaScript is also supported by a large number of third-party libraries and frameworks, such as Puppeteer, Cheerio, etc., which further simplifies the implementation of web crawling. However, JavaScript’s asynchronous programming model may be relatively complex and requires a certain learning cost. Summary of C# vs JavaScript for web scraping Differences in language and environment‌ C#:‌ Requires .NET environment,‌ suitable for desktop or server-side applications. ‌
JavaScript:‌ Built-in in the browser,‌ suitable for front-end and Node.js environment. ‌ Crawl tools and libraries‌:‌ C#: Commonly used HttpClient,‌ combined with HtmlAgilityPack parsing. ‌
JavaScript:‌ Libraries such as Axios can be used,‌ with Cheerio parsing. ‌ Execution environment and restrictions‌ C#:‌ Executed on the server or desktop,‌ less restricted by browsers. ‌
JavaScript:‌ Executed in the browser,‌ restricted by the same-origin policy, etc. ‌ Processing dynamic content‌ Both require additional processing,‌ such as Selenium assistance. ‌ JavaScript has a natural advantage in the browser environment. ‌ Summary‌ Choose based on project requirements,‌ development environment and resources. ‌ Which one is better for crawling complex dynamic web pages, C# or JavaScript? For crawling complex dynamic web pages, C# and JavaScript each have their own advantages, but C# combined with tools such as Selenium is usually more suitable. ‌ JavaScript‌:‌ As a front-end scripting language, JavaScript is executed in a browser environment and naturally supports processing dynamic content. ‌However, when JavaScript is executed on the server side or in desktop applications, it requires the help of tools such as Node.js, and may be limited by the browser’s homology policy, etc. ‌ ‌


C#‌:‌ By combining libraries such as Selenium WebDriver, C# can simulate browser behavior and process JavaScript-rendered content, including login, click, scroll, and other operations. ‌This method can more comprehensively crawl dynamic web page data, and C#’s strong typing characteristics and rich library support also improve development efficiency and stability. ‌ Therefore,‌ in scenarios where complex dynamic web pages need to be crawled,‌ it is recommended to use C# combined with tools such as Selenium for development‌ What technologies and tools are needed for web scraping with C#? Web scraping with C# requires the following technologies and tools: ‌ ‌ HttpClient or WebClient class‌:‌ used to send HTTP requests and obtain web page content. ‌HttpClient provides more flexible functions and is suitable for handling complex HTTP requests. ‌ ‌
HTML parsing library‌:‌ such as HtmlAgilityPack,‌ used to parse the obtained HTML document and extract the required data from it. ‌HtmlAgilityPack supports XPath and CSS selectors, which is convenient for locating HTML elements. ‌ ‌
Regular expression‌:‌ used to match and extract specific text content in HTML documents, but attention should be paid to the accuracy and efficiency of regular expressions. ‌ ‌
Selenium WebDriver‌:‌ For scenarios that need to simulate browser behavior (such as logging in,‌ processing JavaScript rendered content),‌ Selenium WebDriver can be used to simulate user operations. ‌
JSON parsing library‌:‌ such as Json.NET,‌ used to parse JSON formatted data,‌ which is very useful when processing data returned by API. ‌
Exception handling and multithreading: In order to improve the stability and efficiency of the program, you need to write exception handling code and consider using multithreading technology to process multiple requests concurrently. ‌
Proxy and User-Agent settings: In order to bypass the anti-crawling mechanism of the website, you may need to set the proxy and custom User-Agent to simulate different access environments. ‌ The combination of these technologies and tools can efficiently implement the C# web crawling function. ‌ How to crawl dynamic web pages with C# combined with Selenium? ‌ How to use C# combined with Selenium to crawl dynamic web pages? ‌C# combined with Selenium to crawl dynamic web pages‌ Environment preparation‌:‌ Make sure that the C# development environment is installed. ‌
Install Selenium WebDriver, which is used to simulate browser behavior. ‌
Download and set up the browser driver, such as ChromeDriver, to ensure that it is consistent with the browser version. ‌ Usage steps‌:‌ Import Selenium-related external libraries, such as WebDriver, WebDriverWait, etc. ‌
Initialize WebDriver, set up the browser driver, and open the target web page. ‌
Use the methods provided by Selenium to simulate user behaviors, such as clicking, input, scrolling, etc., to handle operations such as dynamically loading content or logging in. ‌
Parse the web page source code and extract the required data. ‌
Close the browser and WebDriver instance. ‌ By combining C# with Selenium, you can effectively crawl dynamic web page content, handle complex interactions, and avoid being blocked by website detection. ‌‌ Conclusion In summary, C# and JavaScript each have their own advantages and disadvantages in web crawling. The choice of language depends on specific needs and development environment. A brief understanding of the difference between C# and JavaScript web scraping As a compiled language, C# provides a wealth of libraries and frameworks, such as HtmlAgilityPack, HttpClient, etc., which facilitate the implementation of complex web crawling logic, and the code is concise and efficient, with strong debugging and error handling capabilities. At the same time, C# has good cross-platform support and is suitable for a variety of operating systems. However, the learning curve of C# may be relatively steep and requires a certain programming foundation. In contrast, JavaScript, as a scripting language, is more flexible in web crawling and can be run directly in the browser without the need for additional installation environment. JavaScript has a rich DOM operation API, which is convenient for direct operation of web page elements. In addition, JavaScript is also supported by a large number of third-party libraries and frameworks, such as Puppeteer, Cheerio, etc., which further simplifies the implementation of web crawling. However, JavaScript’s asynchronous programming model may be relatively complex and requires a certain learning cost. Summary of C# vs JavaScript for web scraping Differences in language and environment‌ C#:‌ Requires .NET environment,‌ suitable for desktop or server-side applications. ‌ JavaScript:‌ Built-in in the browser,‌ suitable for front-end and Node.js environment. ‌ C#:‌ Requires .NET environment,‌ suitable for desktop or server-side applications. ‌ JavaScript:‌ Built-in in the browser,‌ suitable for front-end and Node.js environment. ‌ Crawl tools and libraries‌:‌ C#: Commonly used HttpClient,‌ combined with HtmlAgilityPack parsing. ‌ JavaScript:‌ Libraries such as Axios can be used,‌ with Cheerio parsing. ‌ C#: Commonly used HttpClient,‌ combined with HtmlAgilityPack parsing. ‌ JavaScript:‌ Libraries such as Axios can be used,‌ with Cheerio parsing. ‌ Execution environment and restrictions‌ C#:‌ Executed on the server or desktop,‌ less restricted by browsers. ‌ JavaScript:‌ Executed in the browser,‌ restricted by the same-origin policy, etc. ‌ C#:‌ Executed on the server or desktop,‌ less restricted by browsers. ‌ JavaScript:‌ Executed in the browser,‌ restricted by the same-origin policy, etc. ‌ Processing dynamic content‌ Both require additional processing,‌ such as Selenium assistance. ‌ JavaScript has a natural advantage in the browser environment. ‌ Summary‌ Choose based on project requirements,‌ development environment and resources. ‌ Which one is better for crawling complex dynamic web pages, C# or JavaScript? For crawling complex dynamic web pages, C# and JavaScript each have their own advantages, but C# combined with tools such as Selenium is usually more suitable. ‌ JavaScript‌:‌ As a front-end scripting language, JavaScript is executed in a browser environment and naturally supports processing dynamic content. ‌However, when JavaScript is executed on the server side or in desktop applications, it requires the help of tools such as Node.js, and may be limited by the browser’s homology policy, etc. ‌ ‌ C#‌:‌ By combining libraries such as Selenium WebDriver, C# can simulate browser behavior and process JavaScript-rendered content, including login, click, scroll, and other operations. ‌This method can more comprehensively crawl dynamic web page data, and C#’s strong typing characteristics and rich library support also improve development efficiency and stability. ‌ JavaScript‌:‌ As a front-end scripting language, JavaScript is executed in a browser environment and naturally supports processing dynamic content. ‌However, when JavaScript is executed on the server side or in desktop applications, it requires the help of tools such as Node.js, and may be limited by the browser’s homology policy, etc. ‌ ‌ JavaScript‌:‌ As a front-end scripting language, JavaScript is executed in a browser environment and naturally supports processing dynamic content. ‌However, when JavaScript is executed on the server side or in desktop applications, it requires the help of tools such as Node.js, and may be limited by the browser’s homology policy, etc. ‌ ‌ C#‌:‌ By combining libraries such as Selenium WebDriver, C# can simulate browser behavior and process JavaScript-rendered content, including login, click, scroll, and other operations. ‌This method can more comprehensively crawl dynamic web page data, and C#’s strong typing characteristics and rich library support also improve development efficiency and stability. ‌ C#‌:‌ By combining libraries such as Selenium WebDriver, C# can simulate browser behavior and process JavaScript-rendered content, including login, click, scroll, and other operations. ‌This method can more comprehensively crawl dynamic web page data, and C#’s strong typing characteristics and rich library support also improve development efficiency and stability. ‌ Therefore,‌ in scenarios where complex dynamic web pages need to be crawled,‌ it is recommended to use C# combined with tools such as Selenium for development‌ What technologies and tools are needed for web scraping with C#? Web scraping with C# requires the following technologies and tools: ‌ ‌ HttpClient or WebClient class‌:‌ used to send HTTP requests and obtain web page content. ‌HttpClient provides more flexible functions and is suitable for handling complex HTTP requests. ‌ ‌ HTML parsing library‌:‌ such as HtmlAgilityPack,‌ used to parse the obtained HTML document and extract the required data from it. ‌HtmlAgilityPack supports XPath and CSS selectors, which is convenient for locating HTML elements. ‌ ‌ Regular expression‌:‌ used to match and extract specific text content in HTML documents, but attention should be paid to the accuracy and efficiency of regular expressions. ‌ ‌ Selenium WebDriver‌:‌ For scenarios that need to simulate browser behavior (such as logging in,‌ processing JavaScript rendered content),‌ Selenium WebDriver can be used to simulate user operations. ‌ JSON parsing library‌:‌ such as Json.NET,‌ used to parse JSON formatted data,‌ which is very useful when processing data returned by API. ‌ Exception handling and multithreading: In order to improve the stability and efficiency of the program, you need to write exception handling code and consider using multithreading technology to process multiple requests concurrently. ‌ Proxy and User-Agent settings: In order to bypass the anti-crawling mechanism of the website, you may need to set the proxy and custom User-Agent to simulate different access environments. ‌ HttpClient or WebClient class‌:‌ used to send HTTP requests and obtain web page content. ‌HttpClient provides more flexible functions and is suitable for handling complex HTTP requests. ‌ ‌ HTML parsing library‌:‌ such as HtmlAgilityPack,‌ used to parse the obtained HTML document and extract the required data from it. ‌HtmlAgilityPack supports XPath and CSS selectors, which is convenient for locating HTML elements. ‌ ‌ Regular expression‌:‌ used to match and extract specific text content in HTML documents, but attention should be paid to the accuracy and efficiency of regular expressions. ‌ ‌ Selenium WebDriver‌:‌ For scenarios that need to simulate browser behavior (such as logging in,‌ processing JavaScript rendered content),‌ Selenium WebDriver can be used to simulate user operations. ‌ JSON parsing library‌:‌ such as Json.NET ,‌ used to parse JSON formatted data,‌ which is very useful when processing data returned by API. ‌ Json.NET Exception handling and multithreading: In order to improve the stability and efficiency of the program, you need to write exception handling code and consider using multithreading technology to process multiple requests concurrently. ‌ Proxy and User-Agent settings: In order to bypass the anti-crawling mechanism of the website, you may need to set the proxy and custom User-Agent to simulate different access environments. ‌ The combination of these technologies and tools can efficiently implement the C# web crawling function. ‌ How to crawl dynamic web pages with C# combined with Selenium? ‌ How to use C# combined with Selenium to crawl dynamic web pages? ‌C# combined with Selenium to crawl dynamic web pages‌ Environment preparation‌:‌ Make sure that the C# development environment is installed. ‌ Install Selenium WebDriver, which is used to simulate browser behavior. ‌ Download and set up the browser driver, such as ChromeDriver, to ensure that it is consistent with the browser version. ‌ Make sure that the C# development environment is installed. ‌ Install Selenium WebDriver, which is used to simulate browser behavior. ‌ Download and set up the browser driver, such as ChromeDriver, to ensure that it is consistent with the browser version. ‌ Usage steps‌:‌ Import Selenium-related external libraries, such as WebDriver, WebDriverWait, etc. ‌ Initialize WebDriver, set up the browser driver, and open the target web page. ‌ Use the methods provided by Selenium to simulate user behaviors, such as clicking, input, scrolling, etc., to handle operations such as dynamically loading content or logging in. ‌ Parse the web page source code and extract the required data. ‌ Close the browser and WebDriver instance. ‌ Import Selenium-related external libraries, such as WebDriver, WebDriverWait, etc. ‌ Initialize WebDriver, set up the browser driver, and open the target web page. ‌ Use the methods provided by Selenium to simulate user behaviors, such as clicking, input, scrolling, etc., to handle operations such as dynamically loading content or logging in. ‌ Parse the web page source code and extract the required data. ‌ Close the browser and WebDriver instance. ‌ By combining C# with Selenium, you can effectively crawl dynamic web page content, handle complex interactions, and avoid being blocked by website detection. ‌‌ Conclusion In summary, C# and JavaScript each have their own advantages and disadvantages in web crawling. The choice of language depends on specific needs and development environment.

Web Scraping: Is C# or JavaScript the Superior Choice?

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