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Market reports age faster than teams expect. While companies wait weeks for paid research, competitors collect the same signals directly from the web — in real time. Prices change, creatives rotate, rankings shift, and static reports quickly lose relevance. In this video, we show how businesses collect market data faster and at lower cost using web scraping — and why proxies are the infrastructure that makes it work at scale. You’ll learn: - why traditional reports fail in fast-moving markets; - how APIs and scraping work together in modern pipelines; - why Valid Response Rate and stability matter more than raw speed; - how proxies turn scraping into a controlled, compliant process. This isn’t about more data. It’s about live, reliable data that supports real decisions. 🎥 Watch the full video on our YouTube channel

Building a web crawler is not about writing a script — it’s about designing a controlled data-collection process. In this article, we break down how a web crawler works and how to build one from scratch, step by step — from planning and tool selection to respectful crawling and data storage. You’ll learn: - what a web crawler is and how it differs from web scraping; - how to plan a crawler project around goals, targets, and update frequency; - which languages and libraries fit different crawler scales; - how a basic crawler handles requests, parsing, retries, and navigation; - why robots.txt, rate limits, and delays are critical for stable operation; - how to store collected data for further analysis. The guide focuses on fundamentals that matter in real projects: control, predictability, and extensibility — not shortcuts or one-off scripts. 👉 Read the full article: Step-by-Step Guide to Create a Web Crawler from Scratch

Most data-driven decisions are made on data that never fully arrived. Requests get blocked, misrouted, or return empty responses — but dashboards still look complete. As a result, teams analyze metrics, while part of the data pipeline silently fails. In this video, we explain why large-scale web data collection breaks down and how automation with proper observability helps regain control. We talk about: - how lack of endpoint-level visibility distorts analytics and inflates CPVR; - why geo and ASN misrouting leads to false market signals; - and how policy-driven proxy infrastructure turns data collection into a controlled system. This isn’t about scraping faster. It’s about knowing which data is valid, where it comes from, and why it behaves the way it does. 🎥 Watch the full video on our YouTube channel

Multi-account infrastructure starts with proper profile isolation. When platforms correlate browser fingerprints, IP signals, and behavioral patterns, basic account separation is no longer enough. Antidetect browsers address this at the environment level. In this overview, we analyze how AdsPower works in practice: • generation and customization of unique browser fingerprints (OS, User-Agent, timezone, geolocation generated based on the assigned IP); • isolated profile environments to prevent cross-account linkage; • automation tools (RPA, API, FB Auto) for routine workflows; • team collaboration features: permissions, synchronization, action logs; • step-by-step proxy integration inside profiles and why private proxies are recommended for stable operations. → Explore the full AdsPower overview and proxy setup guide

Buying data or collecting it yourself — where does real value come from? Companies spend thousands — sometimes millions — on data, but ready-made datasets often show the same picture to everyone. In this video, we compare data providers vs. web scraping from a business perspective — cost, speed, relevance, scalability, and control. We explain: - why provider data is fast but often outdated or shared; - how web scraping delivers real-time, customized insights — and what infrastructure it requires; - where each approach works best across e-commerce, fintech, and travel; - and why many companies use a **hybrid strategy** in practice. We also cover the role of proxies — the layer that makes large-scale data collection stable and predictable. If data drives your decisions, this video helps clarify which approach actually delivers value. 🎥 Watch the full video on our YouTube channel

AI agents are moving from pilots to production. Modern AI agents integrate with CRMs, ERPs, BI tools, internal APIs, and external data sources — executing tasks, coordinating workflows, and supporting decisions across teams. In our article, we explain what AI agents are in practice, how they differ from classic chatbots, and what enterprises need to consider when deploying them at scale. We cover: - how AI agent architecture works in real business systems; - the role of LLMs, orchestration, memory, and tooling; - common agent types and concrete B2B use cases; - where network infrastructure becomes a limiting factor; - and why proxies matter once AI agents interact with the web. 👉 Read the full article

AI models don’t collapse overnight. They slowly degrade when their data pipelines do. In 2025, the biggest risk for AI teams isn’t model architecture — it’s unstable, unverifiable, and uneven training data. As data sources fragment by region, regulation, and availability, quality drops long before teams notice. In this video, we look at how AI platforms lose accuracy when data collection lacks provenance, observability, and regional balance — and why regulations like the EU AI Act make this impossible to ignore. We break down a real case of an AI platform processing over 25 TB of data monthly, where incomplete access caused falling success rates, skewed training samples, and rising compute costs — until the data layer was rebuilt with controlled, consent-based access. This isn’t about scraping more. It’s about knowing where data comes from, how it’s collected, and whether every request is valid. Reliable data pipelines don’t just protect compliance. They protect model quality. 🎥 Watch the full video on our YouTube channel

Manual GitHub workflows don’t scale across teams and repositories. When projects grow, routine actions — tracking issues, monitoring activity, syncing repos — quickly turn into overhead. The Python GitHub API helps automate these tasks and keep workflows consistent. In our tutorial, we explain how to work with the GitHub API using Python — from initial setup to stable production use. Inside the guide: - when GitHub automation makes sense for teams; - how to authenticate safely with personal access tokens; - a PyGithub example for managing repositories and issues; - common API pitfalls and rate-limit handling; - best practices for secure tokens and request control. Automation reduces friction only when it’s implemented correctly. 👉 Read the full article

Most competitive analysis doesn’t fail because of bad strategy. It fails because the data is incomplete. In 2025, competitive analysis isn’t about price tables or a few screenshots. It’s about access to live, geo-specific data. In this video, we explain why most companies lose at competitive analysis — and how teams working with complete data avoid these mistakes. Key blind spots we break down: - relying only on public pages and top-10 results; - ignoring regional price and SERP differences; - missing ads, creatives, and catalog changes by GEO; - working with stale or partial data without observability. We also show how proxy infrastructure changes the picture: - access to local markets as real users see them; - higher valid response rates and fewer blind spots; - real-time monitoring instead of assumptions. Competitive analysis fails not because of tools — but because of incomplete access. 🎥 Watch the full video on our YouTube channel