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SERM has become harder: stricter rate limits, CAPTCHAs, and changes in Google SERPs reduce access to real, localized results. Pagination is unstable, results vary by GEO, and automated collection is increasingly restricted. Without the right infrastructure, brands lose control over how they appear in SERPs. In our article, we explain how proxies support modern SERM workflows and help teams maintain accurate, scalable reputation monitoring. Inside the guide: - what SERM is and how it works beyond classic SEO; - why recent Google changes complicate reputation tracking; - how proxies enable stable, localized SERP collection; - which tools and parsers teams use in practice; - and which proxy types fit different SERM tasks. Reputation management now depends on technical resilience as much as strategy. 👉 Read the full article

Shared proxies are chosen for speed and scale — not for perfection. When teams need a large IP pool quickly and at a lower cost, shared proxies are often the first option. They fit tasks where throughput matters more than individual IP reputation. In this video, we explain how shared proxies work, where they perform best, and where they create risks instead of value. What we cover: — how traffic is distributed across shared proxy pools; — differences between IPv4, mobile, and semi-dedicated proxies; — typical use cases: scraping, automation, testing, geo checks; — trade-offs between cost, stability, and IP reputation. Shared proxies are neither universal nor specialized. They work when chosen for the right tasks. 🎥 Watch the full video on our YouTube channel

Email scraping becomes a problem only when it’s done manually, inconsistently, or without structure. When outreach, research, or CRM updates need to scale, manual collection stops working. Python-based email scraping helps extract and structure public contact data programmatically. In our guide, we explain how to build an email scraper from scratch and adapt it to real-world websites. Inside the article: – what email scraping is and where it’s used; – which Python tools work for static and dynamic pages; – core scraping logic and crawler structure; – handling obfuscation and JavaScript-rendered content; – preparing data for marketing and business workflows. The focus isn’t just extraction — it’s stability, structure, and reuse. 👉 Read the full article

Data breaches don’t start with hacks — they start with exposed traffic. In our video, we explain how proxies actually work and why companies use them as part of their cybersecurity stack — not just to change IPs. In the video: - what proxies really do beyond anonymity; - which business risks they help reduce — from data leaks to unsafe employee traffic; - why free proxies increase exposure instead of protecting it; - and how proxies fit alongside VPNs, firewalls, and DLP systems. Proxies aren’t a shortcut — they’re an infrastructure layer that helps control access, monitor traffic, and reduce attack surfaces. 🎥 Watch the full video on our YouTube channel

Anonymous proxy detected isn’t a bug — it’s a signal. When a website blocks access with this message, it usually means your traffic failed one of several trust checks: IP reputation, headers, DNS/WebRTC leaks, traffic patterns, or proxy type. In our article, we break down why this error appears and how to fix it without compromising stability or compliance. Inside the guide: - what “anonymous proxy detected” actually means from a security perspective; - the most common technical causes — from burned public IPs to exposed headers; - how browser settings (cookies, cache, WebRTC) affect detection; - why datacenter proxies are flagged more often than residential or mobile IPs; - when IP rotation and proxy chaining make sense — and when they don’t. Detection usually isn’t about using proxies — it’s about how they’re configured. 👉 Read the full article

In 2026, working with data without proxies means working with blind spots. Web platforms adapt fast: geo-based content, dynamic blocking, IP reputation checks. Without the right proxy setup, teams see distorted results and make decisions on incomplete data. In our video, we walk through proxy tools and solutions that help keep data collection, marketing, and analytics stable in real-world conditions. You’ll learn: — how to check how your traffic is actually perceived; — why speed and IP reputation matter in automation; — how to validate and organize large proxy pools; — and how to keep traffic attribution clean across channels. Covered in the video: — Anonymity Check — IP Speed Test — IP Blacklist Check — Proxy Checker & Proxy List Filter — UTM Builder Modern teams don’t guess — they validate and control their infrastructure. 🎥 Watch the full video on our YouTube channel

⚠️ Official Security Notice We have recently received reports of a Telegram bot impersonating our service and misleading users. Please note that our only official Telegram bot is: @ProxySellerio_bot Any other bots claiming to represent our service are unauthorized and fraudulent. Do not provide personal information or engage in transactions with unofficial bots. Sincerely, Proxy-Seller team.

📘 How to Scrape Bing Results with Python — Key Takeaways Why Bing? Different ranking signals = extra SERP insights, donor domains, long-tail opportunities What you can extract: • Title, URL, snippet, position • PAA, related queries, media blocks Methods covered: Requests + BeautifulSoup — clean HTML parsing, pagination, delays, optional proxy API approach in 2025 — Bing’s public API is sunset; Microsoft now offers Grounding with Bing Search via Azure Agents (doesn’t return raw SERP JSON) Selenium — for JS-rendered or dynamic SERP elements Practical setup: • Python example with pagination, random User-Agent, delays • CSV export, locale controls (cc, setlang), proxy support • Stable selectors for li.b_algo Compliance: Follow ToU, robots.txt, privacy rules. Azure Grounding is the official path, but not for raw SERP data 👉 Full guide: How to Scrape Bing Search Results with Python

Parsing Wikipedia Data with Python — Step-by-Step Guide In our latest video, we show how to scrape and structure Wikipedia data using Python, Requests, and BeautifulSoup — safely and at scale. You’ll learn how to: - extract text and metadata from Wikipedia pages; - configure proxies for stable, multi-IP scraping; - and integrate results into your data workflows. For engineers and analysts building AI pipelines, data enrichment, or research automation — this guide walks through practical setup, code, and proxy configuration examples. 🎥 Watch the full video on our YouTube channel: How to Parse Wikipedia Data with Python