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At Altenar, we often use RabbitMQ as an entry point for our products. How to protect the TCP load balancer with a ManagedSSL certificate is described further.

What if you could build an entire Web App just by describing it? Today, I'll show you how Vercel's V0 revolutionizes web development. In about 5 minutes, you'll learn how to turn text into a production-ready Web App. Let's dive in!

Gen Z – a generation of young people born between the last few years of the 20th century and the first ten years of the 21st, are the primary users of a modern-day Internet. They started using technology since their early childhood, being almost constantly glued to their smartphones and tablets, which led to them being very demanding users that have their own requirements in UX. At the same time, they are known for their sense of humor and straightforwardness – which means they love using products that are both easy to navigate but full of interactive elements. Below we’ve collected a few things you should consider when creating a UX design aimed at a young audience.

Why might a feature not get to production on time? What prevents the team from releasing before the deadline? Can the product owner influence the timeline?

In this article, I will try to answer these questions and suggest solutions for the processes we are implementing and improving within the product team at EXANTE.

In my previous article, Puppeteer CAPTCHA bypass: Tokens or Clicks? Let’s Break It Down (which I also published on Dev.to), I compared two CAPTCHA bypass methods (clicks and tokens) using Puppeteer. I also announced that in the next article (this one), I would conduct a practical comparison of the same methods using Selenium. This will complete the CAPTCHA bypass picture, so to speak. Well, let’s not waste time and get straight to the point.

In my work, I often encounter various services designed to simplify tasks across different areas. I’m not talking about tools like GSA or A-Parser but about Zennoposter or BAS. I am no professor in automation, so I’ll explain in layman's terms: these services are essentially complex, multi-layered platforms that allow the creation of bots and scripts to perform almost any task without human intervention—a sort of “basic neural network.” By the way, such services existed long before neural networks became mainstream for everyday use.

While exploring BAS, I encountered a situation where many developers creating BAS scripts (ironically, developers developing) idealize CAPTCHA solving using clicks.

SSH (Secure Shell) is the backbone of remote system administration and secure remote access, serving millions of developers and system administrators daily. However, when SSH connections fail, the cryptographic nature of the protocol can make debugging challenging. The complex interplay between authentication mechanisms, encryption algorithms, and network layers often obscures the root cause of connection issues. This complexity is further compounded by the protocol's security-first design, where error messages are intentionally vague to prevent potential attackers from gathering system information. Whether we're dealing with key authentication failures, network connectivity issues, or configuration mismatches, understanding the underlying SSH architecture becomes critical for effective troubleshooting.

In today's interconnected development world, secure authentication is not just a luxury—it's a necessity. Whether you're a seasoned DevOps engineer or a junior developer just starting your journey, understanding SSH key pairs is crucial for your daily workflow. They're the unsung heroes that keep our git pushes secure, our server access protected, and our deployments safe from prying eyes.

But let's be honest: SSH keys canbe confusing. With terms like “public key infrastructure,” “cryptographic algorithms,” and “key fingerprints” floating around, it's easy to feel overwhelmed. This guide aims to demystify SSH key pairs, breaking down complex concepts into digestible pieces that will help you make informed decisions about your security setup.

Napkin AI is an innovative artificial intelligence tool that transforms text descriptions into visual sketches and diagrams. Launched in 2023, this toolallows users to quickly convert their ideas into visual representations, similar to drawing on a napkin — hence the name. In this tutorial, I will walk you through its top features and provide a video demonstration.

When we go abroad for vacation or meet a foreigner on the street who doesn’t speak Russian but is trying to ask, “Where is the restroom? How do I get to…”, we wonder how to explain things to them in Russian in a way they would understand.

I asked myself a similar question when trying to explain something to a colleague using SQL while they were working with Java. The main goal of my work was to create a quality test model. Without it, there would be no proper regression testing later on.

I started by building a framework filling it with test cases. We held a meeting where we discussed priority of positive and negative test cases briefly. When developing the test scenarios, I used the incremental model, but as practice showed, this approach also required an iterative method. For example, it is like having the outline of the Mona Lisa first, then adding colors, painting the background, and so on.

It’s better to maintain the checklist in Excel format to add columns, write notes, and more. And let’s not forget that, as we take on the role of Leonardo da Vinci, we use different colors and get creative.

I am a manager by profession specializating in Production Management. My motivator is the Theory of Constraints (TOC) methodology, which focuses on identifying and managing the key constraint of a system to determine the efficiency of the entire system as a whole:

1. The purpose of the IT product Telepathy – the brain-computer interface of  Nueralink

The UN General Assembly Resolution of 22.03.2024 No. A/78/L.49 “Harnessing the Potential of Safe, Secure and Trustworthy Artificial Intelligence Systems for Sustainable Development” [1] confirms (paragraph 5) that"human rights and fundamental freedoms must be respected, protected and encouraged throughout the life cycle of artificial intelligence systems"and to all Member States of the United Nations and, where appropriate, In such cases, other interested parties should"refrain from using or stop using artificial intelligence systems that cannot be operated in accordance with international law or that create undue risks to the enjoyment of human rights, particularly for those in vulnerable situations,”at the same time"The rights that people have offline must also be protected online, including throughout the life cycle of artificial intelligence systems".

The artificial intelligence (“AI”) systems currently being created, tested and deployed are covered by the broader concept of information technology product (“IT product”).

In turn, the author of the term artificial intelligence, Stanford University professor John McCarthy, defined intelligence as"computing part the ability to achieve goals in the world", and AI is like"science and technology of creation intelligent machines, especially intelligent computer programs". At the same time, the same author predicts or laments that"Once this works, no one else will will not call it AI."[20]

The explicit reparameterization trick is often used to train various latent variable models due to the ease of calculating gradients of continuous random variables. However, due to its peculiarities, explicit reparameterization trick is not applicable to several important continuous standard distributions, such as mixture, Gamma, Beta and Dirichlet.

An alternative method for calculating reparameterization gradients relies on implicit differentiation of cumulative distribution functions. The implicit reparameterization trick is much more expressive and applicable to a wider class of distributions

This article provides an overview of various reparameterization tricks and announces a new Python library, irt.distributions, for sampling from various distributions using the implicit reparameterization trick.

This article is the first in the series about the upcoming PostgreSQL 18 release. Let us take a look at the features introduced in the July CommitFest.

Planner: Hash Right Semi Join support
Planner: materializing an internal row set for parallel nested loop join
Planner support functions for generate_series
EXPLAIN (analyze): statistics for Parallel Bitmap Heap Scan node workers
Functions min and max for composite types
Parameter names for regexp* functions
Debug mode in pgbench
pg_get_backend_memory_contexts: column path instead of parent, new column type
Function pg_get_acl
pg_upgrade: pg_dump optimization
Predefined role pg_signal_autovacuum_worker

Have you ever felt overwhelmed trying to make sense of dense notes or wished you could listen to them like a podcast while on the go? In this article, I’ll share the tool that could help you turn text into engaging audio podcasts; I believe it could help you learn new things faster and more effectively.

Mobile developers can use Apple Wallet and Google Wallet integrations for different types of apps.

Once we already wrote our own library for Apple Wallet in React Native because we couldn't find anything ready to use. But now that Google Wallet is fully released, it's time to update it.

Now, this library provides integration with both Apple Wallet on iOS and Google Wallet on Android. It allows you to add, remove, and check for existing passes on iOS, and add passes to Google Wallet on Android.

In today’s article, I’m going to show you a simple, free tool that can help you create great prompts for any AI model you use—whether it’s ChatGPT, Midjourney, or something else.

Dear Colleagues and Friends, 

Since I am only an IT college student so I have not been involved in any serious project in the IT field. However, I hope that my student membership in AСM allows me to share my views with established professionals and thus join a broad and constructive discussion on AI issues. 

Over the past couple of years, the topic of harmonious development of technologies based on the fruits of AI has attracted everyone's close attention. In an effort to understand the current stage of development of this technology and its perception by society and the government, I attended a lot of online events (including extremely enriching ACM Talks webinars), read dozens of articles in the professional and business press, studied regulations AI issues by EU, US and UN.

However, I do not see or feel that this collective search has harmonized the views on AI development expressed by inventors, representatives of the business community, IT specialists, and current and future users of AI products.

As a future IT professional, I would like to see less confrontation and more solidarity regarding the global technological breakthrough.

As an accomplished legal professional, I see that now is an important moment to move from honing the principles of working with AI to formulating and implementing (somewhat proactively) specific mechanisms for the implementation of AI products, as well as establishing fair and responsible regulation of their social and legal consequences. 

In the context of the above, I offer for consideration my views on two aspects of the development and implementation of AI technologies:

OpenStreetMap (OSM) is a global project formed around a geographic information database which is being filled by all comers — both enthusiasts and interested companies. Anybody can contribute, but the openness has its downside: incorrect edits often get into the database. Hence plenty of validators of OSM data have been written which allow to maintain the data quality at an acceptable level.

Since 2016 there exists an open source subway preprocessor that validates (generates error reports) rapid transit routes in OSM for completeness and logical/topological errors, and converts them into formats that are suitable for routing and rendering, e.g. GTFS. Besides OSM data it takes a list of public transport (PT) networks which contains the checking information about the number of lines, stations etc. per a PT network. The preprocessor has successfully proven itself in the preparation of PT data for applications such as Maps.me and Organic Maps.

In this article, I would like to share an approach to detecting one of the types of errors that occur quite often in OSM data and automatic detection of which is somewhat challenging. It's an accidental loss of a station from a route. The source code of the validator and the described algorithm are open source. But first, let's define the concepts used to represent PT data in OpenStreetMap.

Issues of cooperation in the field of regulation of computing power, joint insurance of AI products, and determining the social status and protection of persons with a hybrid nervous system (hybrid people) 

Dear Colleagues and Friends,

In furtherance of the discussion on the topic of "Key technology policy issues will be grappling with in 2025", let me outline some mullings at the intersection of the theory and practice of computer science, law, and neuroethics.

«Our society and thus each individual has the possibility to (help) decide how the world, in which we want to live with artificial intelligence in the future, should look. Philosophy, law, and technology play a central role in the discourse that has to be conducted for this purpose» (from Fraunhofer IAIS’s report «Trustworty use of AI»).

 Essential idea of that proposals - drawing from the deep technical expertise of the computing community, to provide to policy leaders and stakeholders nonpartisan theses on policy gaps in the field of neuroethics, development and implementation of ML/AI tools. It is preferable that this regulation be a harmonious symbiosis of legal norms both at the state level and at the level of socially responsible professional communities.

The approach mentioned in paragraph 6.2 of The CEN-CENELEC Focus Group Report: Road Map on Artificial Intelligence (AI) and, in fact, focused on autonomous self-regulation of AI tools (systems) seems inappropriate to current risks and treats: “An alternative approach is that the system itself ensures that modifications of its functionality due to self-learning have no negative impact on assessment topics like safety or fairness”.  Further, in the same place, the potential ineffectiveness of the certification system for AI tools is actually recognized: “For artificial intelligence systems that learn as they are used, the problem is that the behavior of the system changes continuously and will require a new conformity assessment each time.”