Extracting large volumes of data from dynamic websites like Wikipedia requires more than just scraping tools — it demands a robust proxy solution that ensures speed, reliability, and stealth.

Piaproxy empowers developers, researchers, and data teams to perform large-scale Wikipedia scraping (scraping Wikipedia) without getting blocked or throttled, delivering cleaner and more complete data every time.

The Challenge: Web Scraping Wikipedia Without Getting Blocked

Wikipedia is open, but not without limits. To prevent abuse, it enforces rate limiting, temporary IP bans, and access restrictions. This creates major challenges for anyone trying to:

• Scrape Wikipedia pages or categories (web scraping Wikipedia)
• Download and update datasets beyond static Wikipedia dumps
• Extract wiki content in real-time for research or AI training

Common issues when scraping Wikipedia include:

• IP bans and CAPTCHAs
• Throttled or incomplete results
• Slower scraping speeds

Without residential proxies, even the best Wikipedia scraper will eventually hit a wall.

The Solution: Piaproxy — Residential Proxy Network for Scraping Wiki Data

Piaproxy provides a powerful foundation for any Wikipedia scraping workflow. Whether you’re using your own scraper or working with open-source tools, Piaproxy ensures seamless access with:

• A global pool of rotating residential IPs
• IPs from real devices, bypassing detection filters
• Geo-targeted IPs to test or extract region-specific wiki data
• Fast and reliable proxy rotation, reducing blocks and delays

Whether you're aiming to scrape Wikipedia, extract wiki revisions, or bypass limitations on web scraping wiki content, Piaproxy makes sure your jobs run without interruption.

Why It Works

Piaproxy is designed for developers and analysts who need to scrape Wikipedia at scale. It removes common technical bottlenecks:

• No manual proxy management
• No interruptions from bans or rate limits
• Clean, structured access to wiki data across categories, pages, and links

Compared to downloading static Wikipedia dumps, dynamic web scraping Wikipedia provides more flexibility and up-to-date information.

Conclusion

Whether you're building an AI dataset, researching historical edits, or gathering content for academic analysis, Piaproxy gives you the proxy power to scale your Wikipedia data extraction safely and efficiently.

Avoid IP bans. Skip the throttling. And focus on what matters most — clean, complete data.

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Browsing without leaving a trace: How to use a proxy to protect your online privacy?
In this digital age where almost all behaviors are "recorded", protecting personal online privacy is no longer an exclusive need for programmers or geeks. More and more ordinary users are beginning to realize that the "digital footprints" they leave when surfing the Internet are being quietly tracked by advertisers, platforms and even third-party services. And if you want to achieve "browsing without leaving a trace", a practical and easy-to-use tool is - Proxy.

Simply put, a proxy is a "middleman" that builds a buffer layer between you and the Internet. When you use a proxy to surf the Internet, your real IP address will not be directly exposed to the target website, but the request will be forwarded through the proxy server. In this way, the website records not your local IP, but the IP of the proxy server. This not only effectively hides your identity, but also bypasses geographical restrictions and accesses specific content.

Common proxy types on the market mainly include HTTP proxy, SOCKS proxy and transparent proxy. Among them, HTTP proxy is suitable for web browsing and video playback, SOCKS proxy is suitable for a wider range of network protocols, suitable for more complex application scenarios such as P2P downloads and email clients. Which type you choose depends on the device you use and your purpose, but their core functions are similar: hiding your true identity and protecting your movements on the Internet.

Another additional benefit of using a proxy service is that it can bypass the blockade of certain websites in certain countries or regions. For example, when visiting Google, YouTube or Reddit in China, using a highly anonymous proxy or a VPN is one of the most feasible technical solutions. However, it should be noted that not all proxies can guarantee privacy. Many free proxy servers actually have the risk of monitoring and data leakage. Therefore, if you care about data security, it is recommended to choose a reputable paid proxy service with an encrypted channel.

In addition, if you want to achieve a higher level of "anonymous browsing", you can also combine the proxy with other tools, such as combining the proxy with the browser's incognito mode, or combining it with the Tor network, encrypted DNS, anti-tracking plug-ins, etc., to build a multi-layer privacy protection system. In this way, even if one layer is breached, the overall privacy structure still has a certain degree of protection.

It is worth emphasizing that although the proxy is very effective in privacy protection, it is not a universal shield. For example, the personal information you leave on social media and the account password you use to log in to certain websites may still expose your identity. Protecting privacy is a systematic project. In addition to technical means, it also requires good usage habits, such as not arbitrarily authorizing App access rights, not using public Wi-Fi easily, and not clicking on links from unknown sources.

In general, proxies are an important part of network privacy protection and are suitable for all users who want to surf the Internet more safely and reduce the risk of being tracked. It is simple to operate and flexible, and is an ideal starting point for achieving "browsing without leaving traces". In today's increasingly transparent information, learning to use proxies to arm your network identity is also a digital literacy that modern people should have.

How to choose an enterprise proxy IP solution? Full analysis of performance, price, and compliance (2025 version)

In a business environment where data-driven has become the mainstream, enterprises' demand for proxy IPs continues to rise. Whether it is large-scale data collection, advertising verification, brand monitoring, or multi-account management, proxy IPs have long become key infrastructure in automation systems. Compared with individual users, when choosing proxy IP solutions, enterprises should pay more attention to multiple dimensions such as performance stability, price system, technical compatibility, and compliance and security.

This article will start from a practical business perspective to help you fully understand how enterprises should scientifically choose appropriate proxy IP solutions to avoid stepping on thunder.

Why do enterprises need proxy IPs?

Enterprises use proxy IPs mainly to break through access restrictions, improve concurrency capabilities, simulate global user behavior, and protect account security. For example:

When performing large-scale web crawling, proxy IP can prevent IP from being blocked and improve collection efficiency;
When placing digital advertisements, the proxy helps verify whether the advertisements are actually displayed in the target country and device;
When conducting brand monitoring and public opinion analysis, access search results and social platform content in different regions through IP switching;
When operating multiple social accounts and e-commerce stores across borders, the proxy can help bypass the IP duplication identification mechanism and reduce the risk of account blocking.

What are the common types of proxy IP?

The proxy IPs commonly used by enterprises are roughly divided into the following types:

Data center IP is a proxy allocated from the data center, which is fast, low-cost, and suitable for large-scale crawling in a short time. However, because it is easy to be identified, the blocking rate is relatively high.

Residential IP is an IP obtained from a real home network. It has strong camouflage and is not easily blocked by the target website. It is suitable for projects with high stability requirements, such as account login, advertising verification, etc. Residential IP can be divided into static residential IP and dynamic residential IP. The former keeps the IP unchanged and is suitable for long-term login; the latter is constantly changed and is more suitable for circumventing anti-crawling mechanisms.

Mobile IP comes from 3G, 4G or 5G mobile networks. It is highly anonymous and almost the most difficult to be blocked, but it is also the most expensive and suitable for highly sensitive operating environments.

Rotating Proxy is a service form that automatically changes IPs. It automatically switches proxy IPs at regular intervals or for each request. It is particularly suitable for high-frequency concurrent tasks and saves the trouble of manually maintaining IP pools.

How to evaluate the performance of proxy IP solutions?

For enterprise users, performance is directly related to efficiency and business costs. It is recommended to focus on the following aspects:

Connection stability: Does the IP frequently drop offline? Does it support long-term sessions?

Concurrency: Does the proxy service support multi-threaded requests? Is there a limit on the number of connections?

IP quality: Is it a clean IP (not blacklisted, not a spam source)? Is the blocking rate high?

Access speed: Does the latency in different regions meet business needs? Is the bandwidth sufficient?

Compatibility: Does it support your existing crawler framework, browser automation, API calls, etc.?

Ideally, proxy service providers should provide free trials or real-time testing tools so that enterprises can evaluate the quality of IP resources in advance.

How do enterprise users measure price and cost-effectiveness?

When enterprises choose proxy services, they should not only look at the unit price, but also calculate the cost-effectiveness in combination with the usage scenario and data requirements.

If your business requires a large number of web pages, pictures, videos and other content to be transmitted, it is recommended to choose the "pay by traffic" plan, which is usually settled in GB. Suitable for scenarios such as regular crawlers and advertising verification.

If the number of requests is large but the amount of data is small (such as API crawling, verification code query), "pay by number of requests" is more cost-effective.

For businesses that need to maintain IP stability for a long time (such as account management), "by number of channels" or "static IP monthly subscription" is a more economical choice.

Some service providers also provide annual or customized enterprise plans, which are suitable for medium and large customers with stable and continuous needs. Be sure to pay attention to whether there are hidden fees, such as API call limit, connection time limit, etc.

Compliance is the key to selecting an agent for an enterprise

In 2025, data compliance has become a high-voltage line for international business. GDPR, CCPA, and China's Personal Information Protection Law have all put forward higher requirements for the use of data.

When choosing an agent IP service, enterprises must confirm the following points:

Are all agent IPs from legitimate sources? Have they been authorized by real users?

Does the service provider have compliance qualifications or passed security certifications such as ISO 27001 and SOC2?

Does it support encrypted channels (HTTPS) and prevent man-in-the-middle attacks?

Does it provide KYC verification, sign compliance agreements, and issue formal invoices?

Using an unidentified free agent is likely to cause data leakage, brand risks, and even legal disputes. Compliance is not only a legal issue, but also a guarantee of corporate reputation and sustainable operations.

How can enterprises choose a reliable agent service provider?

An excellent enterprise-level proxy service provider should at least meet the following conditions:

Provide a stable IP resource pool with wide IP distribution and flexible regions
Support API control, automatic rotation mechanism, and real-time monitoring of failure rate
Provide 24-hour technical support, account manager, and SLA guarantee
Transparent prices, flexible packages, and customizable on demand
Have a good industry reputation and real customer cases

It is recommended that companies try 2-3 mainstream service providers before making a final choice, compare their speed, ban rate, availability, and customer service response efficiency, and choose the solution that best suits their business.

Written at the end

Proxy IP is no longer an "optional tool" for companies, but a key component of data-driven business growth. Choosing the right proxy service can not only improve crawling efficiency and account security, but also help you better carry out international operations and marketing strategies.

But choosing the wrong service may lead to account closure, budget waste, and even compliance risks. Therefore, scientifically evaluating performance, understanding price logic, and strictly adhering to compliance bottom lines are the three core principles of enterprise selection.

The future data competition will only belong to those companies that understand how to "seek speed while maintaining stability and put compliance first."

A Complete Guide to AI Training Data Sources and Tools: The Key to Improving Model Performance

High-quality data is the cornerstone of successful artificial intelligence (AI) model training. Whether it’s natural language processing (NLP), computer vision, or speech recognition, the performance of AI models heavily depends on the source and quality of training data. Additionally, selecting the right data processing and annotation tools can significantly boost training efficiency and final results.

In this article, we will explore the main sources of AI training data, introduce commonly used data collection and annotation tools, and share practical tips to improve data quality—helping you build more accurate and efficient AI models.

Main Sources of AI Training Data

1. Public Datasets
Public datasets are the most common source for AI training data and include text, images, audio, and video. Examples include ImageNet, COCO, OpenWebText, and LibriSpeech. These datasets are professionally curated with high quality and diversity, ideal for quickly kickstarting model training.

2. Enterprise Internal Data
Many companies possess rich business data such as user behavior logs, customer service transcripts, and product images. Training models on internal data allows better alignment with real-world business scenarios, enhancing accuracy and usefulness.

3. Web Scraping
Using web crawlers to automatically collect data from websites, social media, and public documents can gather large volumes of data. However, it’s important to comply with site policies and data privacy laws.

4. Crowdsourced Annotation
Platforms like Amazon Mechanical Turk and Figure Eight enable recruiting large-scale human annotators to label, classify, and correct raw data, improving its structure and accuracy.

5. Synthetic Data
Computer-generated data (using GANs, data augmentation, etc.) helps supplement scarce datasets or balance class distributions.

Common Tools for AI Data Processing and Annotation

LabelImg / LabelMe
Open-source image annotation tools supporting bounding boxes and segmentation, widely used in computer vision projects.

Prodigy
An interactive data labeling tool that supports active learning, ideal for NLP tasks to speed up annotation.

SuperAnnotate / Scale AI
Professional annotation platforms offering multi-modal data support, suitable for enterprise-level projects.

Snorkel
A data programming framework that automatically generates weak labels through rules, reducing manual annotation costs.

OpenRefine
A powerful data cleaning tool that helps remove duplicates, fix errors, and unify formats for better data quality.

Practical Tips to Improve Training Data Quality

Ensure Diversity and Representativeness
Cover as many scenarios and sample types as possible to avoid overfitting.

Clean and Preprocess Data
Remove duplicates, irrelevant or incorrect data, normalize formats, and handle missing values.

Balance Data Distribution
Augment minority classes to prevent bias toward majority classes.

Continuous Updating and Iteration
Collect and annotate new data based on model feedback to maintain accuracy and relevance.

Conclusion

High-quality training data is the foundation for building high-performance AI models. By selecting the right data sources and employing scientific annotation and processing tools, you can significantly enhance your model’s accuracy and generalization. As AI applications continue to expand, effective data management will become a core competitive advantage for enterprises.

Want to learn more about AI training data management best practices and tool recommendations? Feel free to leave a comment and join the discussion!

How Unlimited Traffic Proxy Enables LLM Training

With the rapid development of artificial intelligence, large language models (LLM) have become the core technology to promote breakthroughs in natural language processing, content generation, machine translation, and intelligent question and answer. In order to improve the accuracy and generalization ability of the model, LLM must rely on massive, real, and diverse training data. The scale and quality of the data directly determine the upper limit of the model's performance.

However, in the real environment, it is not easy to build a high-quality training corpus. Developers usually face problems such as IP blocking, geographic restrictions, anti-crawler strategies, bandwidth bottlenecks, and high traffic billing. In order to solve this series of challenges, Unlimited Traffic Proxy is becoming a key data collection infrastructure in LLM training.

Why does LLM training require unlimited traffic proxy?

Training large language models requires not only well-structured text corpora, but also multimodal data such as images, videos, and audio from multiple sources. This type of data is often distributed on different websites and platforms around the world, such as YouTube, GitHub, Wikipedia, news media, forums, social platforms, etc.

However, in large-scale, high-frequency data collection, many platforms will block access through rate limits, IP bans or regional restrictions, which seriously affects the stability and efficiency of the crawling task. At this time, unlimited traffic proxies can provide:

Residential/data center IP resources from all over the world, bypassing regional restrictions;
Unlimited traffic, unlimited bandwidth proxy channels, support high-concurrency crawling;
Support automatic IP rotation, avoid bans, and ensure continuous operation of tasks.

This allows developers to continuously and stably crawl all kinds of data around the world and provide sufficient training materials for LLM.

Core advantages of unlimited traffic proxies

1. Global IP coverage (Global Proxy Access)

High-quality unlimited traffic proxies can cover more than 90 countries and regions around the world, support the acquisition of multilingual data, and help models build training corpora with cultural and contextual understanding capabilities. Such proxies can also automatically adjust IP locations to adapt to the access needs of specific regions or platforms.

2. Truly Unlimited Bandwidth

In LLM training scenarios, data traffic may reach TB or even PB levels every day. Traditional proxies that charge by traffic are prone to uncontrollable costs. Unlimited traffic proxies usually provide fixed-fee services. No matter how much data is downloaded, no additional fees will be incurred, which greatly reduces the overall training budget.

3. Multimodal content support (Text, Image, Video, Audio)

In addition to text web pages, LLM training also requires a large amount of image and audio and video data for multimodal learning. Unlimited traffic proxies can support high-speed video and audio downloads, especially for crawling large file content from YouTube, podcast sites, etc.

4. High concurrency and high availability (Concurrency & Reliability)

Support hundreds or thousands of concurrent connections, so that data crawling tasks can complete large-scale collection in a short time. This is especially important for projects with tight training cycles. At the same time, automatic IP rotation and intelligent scheduling mechanisms can significantly improve the success rate of crawling.

5. Easy integration and support for multiple tools

Mainstream unlimited traffic proxy services usually support protocols such as HTTP, HTTPS, SOCKS, etc., and can be easily integrated into commonly used data crawling tools such as Scrapy, BeautifulSoup, Python requests, crawler scripts or distributed crawling systems, with strong compatibility and convenient deployment.

What AI applications are suitable for unlimited traffic proxy?

Build LLM training dataset (LLM Dataset Collection)
Massive web pages, social platform crawling (High-Volume Web Scraping)
Multilingual corpus construction (Multilingual Corpus Creation)
Video/audio transcription training data collection (Speech and Video-to-Text Dataset)
Deep learning scenarios such as sentiment analysis, dialogue systems, multimodal tasks, etc.

Controllable costs and simple deployment

Unlimited traffic proxy services generally use fixed price billing. Users only need to subscribe to services on a daily, weekly or monthly basis without worrying about excess traffic charges or hidden costs. For enterprises or research teams that want to run data collection tasks for a long time and stably, this model is more economical and efficient.

In addition, it is easy to use. Usually, you only need to replace the proxy parameters to quickly integrate it into the existing system without additional development costs.

Summary: Unlimited traffic proxy is the core accelerator of LLM training

To train a powerful and intelligent large language model, you need to rely on a large amount of real data from around the world. Unlimited traffic proxy is a key tool to help developers break through data barriers, improve crawling efficiency, and reduce operating costs.

In the era of large models, whether you are an AI startup, a scientific research institution, or a large factory engineer, if you want to improve the quality of model training and obtain more structured corpus, you might as well incorporate unlimited traffic proxy into your AI infrastructure. This will not only make your data collection system more stable, but also make your model training process faster and more controllable.

Why are SEO experts using proxy IPs?
In the field of digital marketing, search engine optimization (SEO) is a key strategy to increase website visibility and traffic. However, SEO experts often encounter various challenges when conducting data collection and competitive analysis. The application of proxy IP technology provides an effective solution to these problems.
The core value of proxy IP in SEO
As a bridge between user devices and the Internet, proxy IP brings new possibilities to SEO work. Here are seven reasons why proxy IP has become a must-have tool for SEO:
1. Ensure the continuity of data collection
Search engines and websites usually take protective measures against frequent access requests. By using proxy IP, SEO experts can use different addresses to collect data in turn, so as to ensure that the monitoring of important indicators such as keyword rankings and backlinks will not be interrupted.
2. Achieve accurate regionalized SEO analysis
Companies targeting specific regional markets need to understand the performance of websites in different regions. Proxy IP allows SEO experts to simulate the search experience of users in various places, obtain accurate regionalized data, and formulate optimization strategies that are more in line with local search habits.
3. In-depth insight into competitor dynamics
In the fierce market competition, it is crucial to understand the optimization strategies of competitors. Proxy IP helps SEO personnel to comprehensively analyze their keyword layout, content strategy and backlink construction without alerting competitors, providing reference for their own optimization.
4. Improve the accuracy of keyword research
Keyword performance often varies from region to region. Proxy IP enables SEO experts to obtain keyword ranking data in different markets, ensure the selection of search terms that best suit the target audience, and improve the pertinence of content optimization.
5. Efficient management of multi-account operations
Enterprises often need to operate multiple social media accounts or advertising accounts at the same time. Proxy IP can disperse the activities of these accounts to different IP addresses, avoid risks caused by centralized operations, and maintain the normal operation of the accounts.
6. Optimize website performance testing
Page loading speed directly affects search rankings. Proxy IP with caching function can temporarily store website data, speed up repeated visits, and help SEO personnel perform website performance testing and optimization more efficiently.
7. Expand global market vision
For multinational companies, proxy IP provides a window into search behavior in various places. By simulating the search experience of users in different countries, companies can develop more targeted global SEO strategies.
How to choose a proxy IP service suitable for SEO
High-quality proxy IP services should have the following characteristics:
Large IP resource library: ensure smooth IP rotation
Wide geographical coverage: obtain accurate regional data
Stable and high-speed connection: ensure research efficiency

Conclusion
In today's highly competitive digital environment, proxy IP has become an indispensable tool for SEO experts. It not only improves the efficiency and accuracy of data collection, but also provides a comprehensive and reliable basis for enterprises to formulate optimization strategies. Whether it is deepening the local market or expanding global business, the rational use of proxy IP technology can help enterprises gain a competitive advantage in the field of search engine optimization.
By integrating proxy IP into the SEO workflow, professionals can make more informed decisions, continuously improve the performance of the website in search results, and ultimately achieve a double increase in traffic and conversions.

When collecting SEO data, such as keyword rankings, backlink data, or competitor analysis, choosing a dynamic or static IP can make a significant difference. First, let’s clarify what these two concepts mean.

What is a static IP?
A static IP is a fixed network address that does not change, just like you have a permanent network residence. Businesses and servers often use static IPs because they provide a stable connection point.

What is a dynamic IP?
A dynamic IP is an address that changes periodically - it may change every few minutes, hours, or days. Internet service providers (ISPs) usually assign dynamic IPs to ordinary users because it is more efficient to manage.
With these basics in mind, let’s explore which type is better for SEO data collection.

When to choose a static IP for SEO data collection
Static IPs are ideal when:
You need consistency If you are tracking search rankings over a long period of time, static IPs ensure that your requests always come from the same location, avoiding the bias of localized results.
Need to maintain session connection Some tools (such as Google Search Console) require a stable connection, and changing IPs may cause unexpected exits.
Reliability is critical Static IPs have lower latency and are ideal for real-time SEO monitoring tools.

When to choose dynamic IPs
Dynamic IPs perform better in the following scenarios:
Large-scale data collection If you are crawling thousands of pages (such as SERP analysis or bulk backlink checking), rotating IPs can help avoid access restrictions.
Simulate real user behavior Since dynamic IPs change, it can make your requests look more natural and reduce the risk of being identified.
Global SEO research Need to get data from different countries? Dynamic IPs can simulate visits from multiple regions.

How to balance speed, reliability, and efficiency
Static IPs provide stability and fast response, suitable for long-term tracking.
Dynamic IPs provide flexibility and scalability, supporting high concurrent collection without overloading a single IP.

Best practices for SEO crawling

1. Test both options Conduct small-scale experiments to see which method works better on the target website.
2. Control request frequency Even with dynamic IPs, properly spacing requests can help maintain stable access.
3. Choose a quality IP A reliable IP provider can ensure a higher success rate, whether you choose a static or dynamic IP.

Final advice
For most SEO practitioners, a mixed strategy works best:
Static IP is used for critical long-term tracking (such as daily ranking monitoring)
Dynamic IP is used for large one-time projects (such as analyzing a competitor’s entire site)
By choosing the right IP type according to your needs, you can both efficiently obtain SEO data and maintain stable and smooth access.