Development

We continue the series of articles about new patches coming to PostgreSQL 18, this one covering the news of the November CommitFest.

If you missed the previous reviews, you can check them out here: 2024-07, 2024-09.

Modern services (websites) ubiquitously deploy CAPTCHAs for protection, complicating life for developers, SEO specialists, and automation engineers (although the original aim was not to frustrate automators but rather to minimize excessive load on resources from such actors). Among the multitude of CAPTCHAs, Google reCAPTCHA Enterprise stands out as one of the most advanced bot protection systems in this line-up. It is capable of analyzing user behavior and blocking suspicious activity, thereby significantly hindering the recognition of reCAPTCHA Enterprise and its automated bypass. Let’s break down what reCAPTCHA Enterprise is, how it differs from versions v2/v3, and why it is so difficult to bypass (or is it?).

Typically, I use the following structure for global styles in any project. This setup allows for a highly flexible, easy-to-read, and maintainable style system, which is crucial for large projects that may last several months or even years.

Let’s review each of these folders:

Common: contains typography styles, imported fonts, icons, and core styles used across the project, such as container.scss or helpers.scss.

This world needs a new theory — a theory that could describe all the theories on the planet. A theory that could easily describe philosophy, mathematics, physics, and psychology. The one that makes all kinds of sciences computable.

This is exactly what we are working on. If we succeed, this theory will become the unified meta-theory of everything.

A year has passed since our last publication, and our task is to share the progress with our English-speaking audience. This is still not a stable version; it’s a draft. Therefore, we welcome any feedback, as well as your participation in the development of the links theory.

As with everything we have done before, the links theory is published and released into the public domain — it belongs to humanity, that means, it is yours. This work has many authors, but the work itself is far more important than any specific authorship. We hope that today it can become useful to more people.

We invite you to become a part of this exciting adventure.

There are several ways to integrate a CSS library into your project. By “CSS library,” I mean any modern CSS framework or UI library, such as Bootstrap, NG-Zorro, PrimeNG, etc. While all these methods work, many do not offer great flexibility or support deep customization of the chosen framework.

To build a robust foundation for your design system and ensure easy future customization, it’s best to use the source code styles (SASS or LESS files) instead of the minified version. This approach provides a more convenient workflow, reduces unnecessary code, and increases flexibility.

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 keyword unsafe for Rust or std::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 the pgpro_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 the Energomera 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 how discrete-analog solutions were used to achieve high accuracy. The Energomera is a fascinating example of engineering and ingenuity, giving us a unique perspective on the evolution 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 of Gemini Code Assist? It’s an AI-powered coding assistant from Google that helps with writing, completing, and debugging code. The best part? It’s now free for individuals, freelancers, and students!

In this article, I’ll show you how to set up and use Gemini Code Assist inside VS 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!

An anti-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.