Automatic data scraping (parsing) has become an essential practice for developers, analysts, and automation specialists. It is used to extract massive amounts of information from websites—from competitors’ prices and reviews to social media content. To achieve this, numerous “scrapers” have been developed—libraries, frameworks, and cloud services that enable programmatic extraction of web data. Some solutions are designed for rapid parsing of static pages, others for bypassing complex JavaScript navigation, and yet others for retrieving data via APIs.

In this article, I will review the top scraping tools—both open source libraries and commercial SaaS/API services—and compare them according to key metrics: • Speed and scalability; • Ability to bypass anti-bot protections; • Proxy support and CAPTCHA recognition; • Quality of documentation; • Availability of APIs and other important features.

The most common types of software bugs are memory management bugs. And very often they lead to the most tragic consequences. There are many types of memory bugs, but the only ones that matter now are memory leaks due to circular references, when two or more objects directly or indirectly refer to each other, causing the RAM available to the application to gradually decrease because it cannot be freed.

Memory leaks due to circular references are the most difficult to analyze, while all other types have been successfully solved for a long time. All other memory bugs can be solved at the programming language level (for example, with garbage collectors, borrow checking or library templates), but the problem of memory leaks due to circular references remains unsolved to this day.

But it seems to me that there is a very simple way to solve the problem of memory leaks due to circular references in a program, which can be implemented in almost any typed programming language, of course, if you do not use the all-permissive keywordunsafe for Rust orstd::reinterpret_cast in the case of C++.

If you’re building a multilingual React Native (or web) app, you’ve probably tried react-i18next, i18n-js, LinguiJS, or similar libraries.

But in every project, the same issues come up:

❌ Unused key-value pairs are never removed
❌ Content gets duplicated
❌ Ensuring format consistency across languages is painful
❌ i18next doesn’t generate TypeScript types by default – so t("my.key") won’t throw even if it’s been deleted
❌ Localization platforms like Lokalise or Locize get expensive fast

Frustrated by these challenges, I waited for a better solution... then decided to build one myself:Intlayer.

As part of my scientific and research interests, I decided to experiment with bypassing complex types of CAPTCHAs. Well, by “experiment” I mean testing the functionality and verifying that my electronic colleague can write code on my behalf. Yes, there was a lot of extra stuff—follow ethical norms, blah blah blah… But the simple fact remains: dude, I’m doing this solely as part of research, and everyone agreed.

If your code has many nested executions of stored procedures, you can benefit from building popular "flame diagram" of the execution time which is de facto standard for performance profiling.

Storing all your data in one place might seem convenient, but it’s often impractical. High costs, database scalability limits, and complex administration create major hurdles. That’s why smart businesses rely on Information Lifecycle Management (ILM) — a structured approach that automates data management based on policies and best practices.

With Postgres Pro Enterprise 17, ILM is now easier than ever, thanks to thepgpro_ilm extension. This tool enables seamless data tiering, much like Oracle's ILM functionality. Let’s dive into the challenges of managing large databases, how ILM solves them, and how you can implement it in Postgres Pro Enterprise 17.

Author: Sergey Lukyanchikov,C-NLTX/Open-Source

Disclaimer: The views expressed in this document reflect the author's subjective perspective on the current and potential capabilities of jBPM.

This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM’s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:

How did engineers in the past manage to measure electrical power without modern microchips and DSPs? This article explores theEnergomera CE6806P, a device created in 2006 for verifying electricity meters, yet built using 1980s-era technology.

We’ll take a closer look at its design, principles of operation, and howdiscrete-analog solutionswere used to achieve high accuracy. The Energomera is a fascinating example of engineering and ingenuity, giving us a unique perspective on theevolution of electrical measurement devices.

While pg_probackup 3 is still in the works and not yet available to the public, let’s dive into what’s new under the hood. There’s a lot to unpack — from a completely reimagined application architecture to long-awaited features and seamless integration with other tools. 

A lot of people around me spend time trading on the stock market. Some trade crypto, some trade stocks, others trade currencies. Some call themselves investors, others call themselves traders. I often see random passersby in various cities and countries checking their trading terminals on their phones or laptops. And at night I sometimes write analytical or backtesting software—well, I did up until recently. All these people share a common faith and a set of misconceptions about the market.

In this article, I'll take you through everything you need to know about Hugging Face—what it is, how to use it, and why it's a game-changer in the ever-evolving landscape of artificial intelligence. Whether you're a seasoned data scientist or an enthusiastic beginner eager to dive into AI, the insights shared here will equip you with the knowledge to Hugging Face's full potential.

Often at work, I encounter services that provide offerings such as resident proxies. Yet, I have never delved deeply into the topic. I have always simply consumed the product “as is,” as some lazy authors like to say.

I have a general understanding of how this type of service works at a layman’s level, and I became interested in exploring the topic more deeply and attempting to share the conclusions I reached through a deeper understanding of what resident proxies are. Let’s see what comes out of it. No recommendations here—just the subjective, evaluative opinion of yet another “specialist.”

Proxy servers are intermediaries between your device and the internet, allowing you to hide your real IP address and alter the appearance of your connection. Think of it as a white camouflage coat in snowy weather, if we speak in very simplistic terms. Let’s start from that—options for camouflage. However, comparing with camouflage coats would be rather dull; instead, let’s recall animals and insects that use camouflage and try to draw a parallel. In fact, I’ve already done so.

For many years, the PostgreSQL community was skeptical about using this database management system (DBMS) for high-transaction environments. While PostgreSQL worked well for lab tests, mid-tier web applications, and smaller backend systems, it was believed that for heavy transactional loads, you’d need an expensive DBMS designed specifically for such purposes. As a result, PostgreSQL wasn’t particularly developed in that direction, leaving a range of issues unanswered.

However, the reality has turned out differently. More and more of our clients are encountering problems that stem from this mindset. For example, in the global PostgreSQL community, it’s considered that 64 cores is the maximum size of a server where PostgreSQL can run effectively. But we’re now seeing that this is becoming a minimum typical configuration. One particular bottleneck that has emerged is the transaction counter, and this is a far more interesting issue. So, let’s dive into what the problem is, how we solved it, and what the international community thinks about it.

Have you ever heard ofGemini Code Assist? It’s an AI-powered coding assistant from Google that helps with writing, completing, and debugging code. The best part? It’s nowfree for individuals, freelancers, and students!

In this article, I’ll show you how to set up and use Gemini Code Assist insideVS Code. Whether you’re new to coding or an experienced developer, this tool can save you time and make coding easier. Let’s get started!

Anti-detect browsers emerged as a response to the spread of browser fingerprinting technologies – the covert identification of users based on a combination of their device’s parameters and environment. Modern websites, besides using cookies, track IP addresses, geolocation, and dozens of browser characteristics (such as Canvas, WebGL, the list of fonts, User-Agent, etc.) to distinguish and link visitors. As a result, even when in incognito mode or after changing one’s IP, a user can be detected by their “digital fingerprint” – a unique set of properties of their browser.

In fact, when I first started my journey in these internet realms, my expertise in digital security was evolving—and continues to grow—and I eventually came to understand browser fingerprints. At first, I believed cookies—collected by those pesky search engines that tracked what I viewed—were to blame, then I learned about browser fingerprints and long denied that I needed to learn to work with and understand them. Really, just when you finally figure out proxies, learn how to change and preserve cookies, here comes a new twist. Moreover, it turns out that fingerprints are also sold, and the price is not exactly low. In short, money is made on everything! But that’s beside the point now!

Ananti-detect browser is a modified browser (often based on Chromium or Firefox) that substitutes or masks these properties (fingerprints), preventing websites from unequivocally identifying the user and detecting multi-accounting.

Proxy servers have long become an integral part of the modern network. They are used to enhance anonymity, bypass blocks, balance loads, and control traffic. However, not everyone understands that there is a fundamental difference between HTTP(S) proxies and SOCKS proxies. In this article, I will attempt to examine in detail the technical aspects of both types, review their advantages and limitations, and provide examples of configuration and usage – though this part is more of an elective (optional, if you will, but I really feel like including it).

This article explores the concept ofequivalence classes from the perspective ofmathematical analysis and their application inQA testing. The author explains how properly defining equivalence classes helpsoptimize test design, reducing the number of test cases while maintaining thorough verification.

Using the example ofcurrency conversion from rubles to euros, the article demonstrates how to construct equivalence classes, verify their compliance with mathematical properties (reflexivity, symmetry, transitivity), and identify errors in data partitioning.

This article is useful forQA engineers, developers, and analysts who want to gain a deeper understanding of logical testing principles and improve the efficiency of their test strategies.

There have been many discussions about which programming language is better in terms of security and correctness of source code (by "correctness and security" we mean the absence of various errors in the program that manifest themselves at the stage of its execution and lead to the issuance of an incorrect result or unexpected behavior). And some programming languages, such as SPARK or OCaml, were even specially developed to facilitate the proof of program correctness.

Is it possible to write programs without errors at all?

Postgres Pro Enterprise 17 introduces major improvements in performance and scalability. The key feature of this new release is the proxima extension, which combines connection pooling, proxying, and load balancing within the database core. Developers also gain improved tools for managing message queues, optimizing queries, enhancing security, and utilizing smart data storage. Want to know how these and other features can impact your applications and simplify database administration?

This article provides a brief overview of the release, accompanied by the links to more detailed information.

The topic of proxies has always been approached (at least, that’s how the publications I encountered did) from the standpoint of complex terminology, which often remains unclear to the layman—someone not particularly versed in these internet matters. I decided to delve into the issue, and here is what I came up with: