I recently tried using Firebase Studio, and it has been an interesting experience that I want to share with you. It's a free, browser-based tool from Google that allows you to build full-stack web apps with AI assistance. Want to know more? Then read this article until the end.
Let’s say you’ve created your own USTRUCT in C++ and now you want to serialize it.
Usually, it’s enough to simply mark the desired fields with SaveGame. But here’s the problem — in order for this to work, the fields themselves must support serialization.
Unfortunately, one of our variables doesn’t.
In my case, it’s a struct called FNonSerializableStruct. Because of that, only the second struct gets serialized, even though we marked both with SaveGame.
Armenia is a lucky country. Back in the 1990s somebody lobbied Synopsys, the #1 leader in the Electronic Design Automation market, to create a division there. It was joined by Mentor Graphics / Siemens EDA, another EDA leader, then NVidia. Synopsys Armenia became the largest Synopsys division outside the US Silicon Valley and Boston, although the Taiwanese may tell you otherwise. Since Synopsys and Mentor make software used by chip designers in Apple, Samsung, AMD and all other electronic companies, Armenia has an unfair advantage over all their neighbors in Caucasus and Central Asia.
In addition, Armenia has friendly relations with Russia, and most Armenians speak Russian as well. This has facilitated the move of many Russian companies to Armenia, for example, a RISC-V semiconductor IP provider Syntacore. Finally, as we can see from the recent conference EDA Connect, Armenia is attracting the attention of electronic and EDA companies from China.
EDA Connect featured not only academic and industrial papers but also a hackathon on FPGA design, attended by local students from Yerevan State University, the American University of Armenia, the Russian-Armenian University, the French University in Armenia and others.
If you’ve been working with Unreal Engine for a while, you’ve probably come across nodes that let you call a function now and get the result later — while also continuing execution once the function completes.
I needed to create a category panel for placeable items in the UI — like the ones you see in city-building games. As a legacy, I inherited a ready-made UENUM, which may be modified in the future.
Naturally, I didn’t want to manually create and tweak every single widget — especially knowing that the list of categories might change later. I wanted something simple and reusable: just run a For each loop, generate everything on the fly, and ideally make it work for any enum, not just this one.
And there was a way! When you define a UENUM, Unreal automatically generates all the metadata and creates a corresponding UEnum object, which is a UObject. All you have to do is use that data properly.
Historically, we’ve ended up using a few rather obscure PHP extensions—written and barely (if at all) maintained by their original authors—that aren’t available in standard Debian package sources.
We stick to the principle of “do it right, and it’ll work right,” which means Slackware-style dropping binaries into the system outside of package managers is frowned upon.
So instead, we’ll be building proper .deb packages for PHP extensions—without breaking compatibility with the existing environment.
The Cisco IOS/IOS-XE operating system is a source of inspiration for many other vendors. The internet is full of guides on how to configure a typical Cisco router for SOHO scenarios. However, unlike consumer-grade routers, configuring something like Cisco IOS requires caution. If you don’t think things through, the router may start "living its own life" and end up, for instance, as a DDoS amplification tool.
Setting up a v3 user on the server-side agent for the default Linux snmpd (net-snmp package). Out of scope: SNMP traps and read-write (rw) users.
SNMP version 3 allows packet transmission in encrypted form, making it safe to transfer telemetry over public networks without the risk of exposing either authentication information (analogous to a community string) or the data stream itself, which is encrypted using a symmetric algorithm with a shared key.
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:
