Data Collection

Web scraping is a powerful way to collect publicly accessible data for research, monitoring, and analysis, but the tools you choose can greatly influence the results. In this article, we review six of the most popular programming languages for web scraping, breaking down their key characteristics, strengths, and limitations. To make the comparison practical, each section also includes a simple code example that highlights the language’s syntax and overall approach to basic scraping tasks.

In recent years, there has been a significant shift in the way content creators, influencers, and artists connect with their audience and monetize their talents. OnlyFans, a subscription-based social media platform, has emerged as a website that allows creators to share exclusive content directly with their dedicated followers for a subscription fee.

OnlyFans scraping, which involves extracting publicly available data from the website, has sparked an interest. In this blog post, we’ll delve into this scraping world, its possible use cases, and the benefits it offers. Excited to learn more? Buckle up, and let’s begin!

Manually copying data from websites? That's what interns are for – except you don't have interns. Good news: C# can automate the tedious stuff. While Python dominates the web scraping conversation, C# has matured into a legitimate contender with robust libraries, type safety, and performance that actually matters in production. Let's learn more about it.

The Go vs Python comparison is a key discussion among developers. Go (Golang), created at Google, excels in performance, scalability, and concise syntax for distributed systems. Meanwhile, Python prioritizes readability and rapid development with a vast library ecosystem. Understanding these core differences is crucial for developers choosing tech stacks in 2026 and beyond. Let's dive in!

There’s no reliable Python application that doesn’t have a built-in failed HTTP request handling. You could be fetching API data, scraping websites, or interacting with web services, but unexpected failures like timeouts, connection issues, or server errors can disrupt your workflow at any time. This blog post explores strategies to manage these failures using Python’s requests library, including retry logic, best practices, and techniques like integrating proxies or custom retry mechanisms.

Residential proxies hide your online activity by routing traffic through real home IPs, making you look like a regular user. They’re more anonymous than datacenter proxies and great for web scraping, ad verification, and dodging geo-blocks. This guide helps you select the right solution while maintaining undetectable online operations.

Getting your hands on limited-edition Labubu figures like The Monsters, Big Into Energy, or Labubu Exciting Macaron has gotten absolutely wild lately. Once Pop Mart announced that they would no longer sell these figures in physical stores, the online shop got some serious traffic. But we're also talking about these Pop Mart drops selling out in literal seconds – forget about trying to cop manually, you'll never make it past the cart page. And thousands of users complain that they can’t cop a mystery box for themselves. Luckily, there’s an automated solution that’ll help you to get at least a couple of Labubus. The trusty bot, tuned with residential proxies, will handle CAPTCHAs, geo-blocks, and IP bans, helping you reach the checkout page as quickly as possible. This guide breaks down all the tactics that actually work, straight from people who've been crushing these drops consistently.

GitHub is one of the most significant sources of technical knowledge on the internet, especially for developers building intricate applications. Follow this guide to learn how to extract this valuable data and effortlessly follow the latest tech trends.

TikTok has become a goldmine of user-generated content and social media insights. With over 1 billion active users creating millions of videos daily, the platform offers unprecedented opportunities for data analysis, trend monitoring, and business intelligence. This comprehensive guide shows you how to scrape TikTok data effectively using Python.

With all the buzz about AI taking over our jobs, why not flip the script and employ AI to help you write efficient code and boost your productivity instead? In 2026, the best AI tools for coding are designed to be your new sidekick, helping you code smarter, faster, and with less stress. Let's explore how you can make AI work for you before the inevitable machine world domination.

Web scraping has become increasingly complex as websites deploy sophisticated anti-bot measures and dynamic content loading. While traditional scraping approaches require extensive manual coding and maintenance, artificial intelligence offers a transformative solution. Claude, Anthropic's advanced language model, brings unique capabilities to the web scraping landscape that can dramatically improve both efficiency and effectiveness.

More and more industries now depend on data to make informed choices, which means having a fast, reliable way to collect structured web data is no longer optional. It’s a core need. In this overview, we’ll examine the top web scraping services of 2026, covering what they provide, their pricing models, the users they serve best, and their unique strengths. Whether you’re growing your data infrastructure or moving on from outdated tools, this guide is here to help you make a smart, well-matched choice.

Ever wished you could make the web work for you? JavaScript web scraping allows you to gather valuable information from websites in an automated way, unlocking insights that would be difficult to collect manually. In this guide, you'll learn the key tools, techniques, and best practices to scrape data efficiently, whether you're a beginner or a developer looking to streamline data collection.

Do you want to unlock the power of invisible browsing? A headless browser works like a regular browser but without the visual interface. It runs invisibly, automatically visiting websites to test pages or collect data. Faster and lighter than regular browsers, it's perfect for developers. In this guide, we’ll explain how headless browsers work, their uses, pros/cons, and top tools to choose from.

Large language models (LLMs) are universal tools that improve text understanding and generation across different tasks. However, they often lack specific industry knowledge. Training a model on your own data is important for adjusting, accurate, and efficient responses. This article will guide you through the training process, best practices, and challenges to help you get started with confidence.

With the rapid improvements in artificial intelligence technologies, it seems that 2026 will present some new challenges for web scraping enthusiasts and professionals. Over the years, anti-bot systems have become increasingly sophisticated, which makes extracting valuable data from websites a true challenge. As businesses intensify their efforts to protect against automated bots, traditional web scraping methods are being put to the test. The surge in anti-bot measures is not only due to heightened cybersecurity awareness but also signifies a shift in the digital ecosystem and growing competition. As a result, those who want to leverage publicly available data need to recalibrate their strategies to navigate and circumvent anti-bot systems.

If CAPTCHAs and IP bans were not on your bingo card for 2026, our comprehensive guide is a must-read. We’ve sat down with our scraping gurus and discussed the best practices, gathered all the pro tips, and summarized what’s coming next for anti-bot systems and scrapers. As 2026 approaches, it demands a proactive approach to understanding, outsmarting, and ultimately thriving in the face of escalating anti-bot measures, so grab a cup of coffee and dive into our guide.

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AI web scraping with Python lets you extract data from websites without relying on fragile parsing rules. AI helps handling page inconsistencies and dynamic content, while Python continues to manage fetching. In this guide, you'll see how models extract data from unstructured pages, reduce manual parsing rules, support automation, and scale into reliable pipelines.

Wikipedia has over 60 million articles, making it a valuable resource for machine learning training data, research datasets, and competitive intelligence. This tutorial guides you through extracting your first article to building crawlers that navigate Wikipedia's knowledge graph. You'll learn to extract titles, infoboxes, tables, and image references, then scale up to crawling entire topic clusters.