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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:

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.

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.

Every SEO professional has encountered CAPTCHA. If not, they are either not professionals, misunderstand the term SEO (possibly confusing it with SMM or CEO), or are very new to this challenging field.

One could endlessly deny and argue that CAPTCHA is overrated and does not deserve significant resources. But these arguments end the moment one needs data from a search engine result page, like Yandex, without knowledge of XML requests… Or, say, a client wants to scrape the entire Amazon (just because they do) and offers good pay...

Two powerful AI tools are making waves in the world: NEW ChatGPT Search and Perplexity AI. But which one is better for you? I've tested them and want to share my results.

I recently explored two powerful AI tools — NotebookLM and Perplexity Spaces — and want to share my comprehensive review of these research assistants.

In the last couple years Artificial Intelligence has taken the world by storm, starting to revolutionize the tech industry in many aspects, including UX. AI can help UX designers to automate some routine tasks that take a lot of time, providing more space for creativity and product quality improvement. It’s not likely that AI might replace human designers in the near future, but it can definitely ease the work of designers in some ways. 

Let’s discuss how AI can be applied in UX Design these days:

So let’s talk about the word yes.  Another way to say it is that, we need to give some positive statements to make a sale. 

I said, in the last article that Russians and Eastern European people are much more reserved with giving the answer of yes. So in this culture, it is normal to wait to have more facts about what the work will be for giving a positive answer. But this creates real problem and an absolute block when it comes to selling your IT software and project development to western cultures!

Let’s break this down into understandable pieces. 

In the world of blockchain, where the word "gas" is most often associated not with gas stations, but with fees for transactions in Ethereum, the search for ways to minimize these costs is becoming increasingly relevant. Less heartache for an empty wallet and more time to solve really important issues... for example, what to cook for lunch =)

Similar to the epic Batman vs Superman battle, compare GSN and Account Abstraction. As in any superhero story, each of them has its own unique abilities and disadvantages, which we will look at in detail.