Would you like to know which indexes are used frequently or rarely? Which ones aren't used at all? Which tables and indexes are the largest? It's very easy to create visualizations for this. They're both visually appealing and practically useful.
If you work with PostgreSQL, you've likely run into performance issues at some point — especially as your database grows. Things may have been running smoothly at first, but as your client database expanded, queries started slowing down. Sound familiar? Here's a guide to help you identify and fix problematic queries, so you can get your PostgreSQL database running at peak performance again.
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.
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.
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!
Написал лонгрид на английском о текущем состоянии открытых средств проектирования ASIC-ов. Заодно познакомил англоязычных читателей с практиками шаманов Сибири и фигурой Ивана Сусанина. Упомянул планируемые семинары в Мексике и Армении.
A text on the current state of Open-source ASIC design tools. Includes side discussions of the upcoming hackathons in Mexico and Armenia, Docker and Python, Static Timing Analysis and RISC-V, Siberian shamans and treacherous swamps in Belarus.
Hey there! I'm excited to share some big news from Google about their latest AI model, Gemini 2.0. I encourage you to read the article till the end and watch my video demo. It's mind-blowing!
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.
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.
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...
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:
Imagine: you've just created an incredible decentralized application, and it's so cool that even your grandmother wanted to try it. But once users are faced with the need to pay a commission, the entire UX (User Experience) rapidly slides down like a ball down a slide. Blockchain promises a bright future in which decentralization, transparency and security are our best friends, and it makes us pay for daily operations. Imagine if you had to pay every time you like on social networks or send a message in a messenger. It's terrible, isn't it? But dApps users face something like this every day.
But now, like a prince on a white horse, GSN (Gas Station Network) appears. With its help, developers can make their applications gas-less, and users will finally be able to forget about commissions like a nightmare.
In this article, we will look at what GSN is, how it works, and how to implement it into your projects to please users.
In today's world, where businesses increasingly rely on effective communication, cloud technologies offer a wide range of solutions to optimize interactions both within the company and with customers. Three of the most popular options are UCaaS, CCaaS, and CPaaS. While they all fall under the umbrella of cloud communications, their functionality and applications differ significantly. Let's delve into the details.
UCaaS (Unified Communications as a Service)
UCaaS unifies various communication channels, such as voice calls, video conferencing, messaging, and email, into a single platform. This allows employees to easily communicate and collaborate with each other, regardless of their location.
Benefits of UCaaS:
OpenAI has recently launched a new feature called Canvas for ChatGPT, designed to enhance users' interactions with AI for writing and coding projects. This new interface aims to provide a more collaborative and interactive experience, moving beyond the traditional chat format. In this article, I'll cover the top features of ChatGPT Canvas and show how to use it.
CAPTCHA is not just a single word that can be defined; it's an acronym consisting of nine words (and two prepositions): Completely Automated Public Turing Test To Tell Computers and Humans Apart. This mouthful was shortened to the concise CAPTCHA to avoid creating yet another hard-to-pronounce term. Translated into Russian, this abbreviation sounds like "Полностью автоматизированный публичный тест Тьюринга для различения компьютеров и людей" (Fully Automated Public Turing Test to Differentiate Computers and Humans).
Команда Python for Devs подготовила перевод большого туторила по Django Templates. В статье подробно разбирается, как устроен язык шаблонов Django, чем он отличается от Jinja, как правильно наследовать шаблоны и организовать структуру проекта. Если вы хотите сделать свои Django-приложения более чистыми, поддерживаемыми и быстрыми — этот материал для вас.
Imagine you have an idea powerful enough to change the world. Your tool of choice is a state-of-the-art LLM, ready to help you formalize the problem, generate hypotheses, and synthesize a solution. What you receive is a construct that is internally logical, elegant, and coherent... yet completely wrong. It's a mix of established facts, model-generated hallucinations, and your own subtle biases. With no way to test it in practice or design a clean experiment, the entire endeavor suddenly starts to look like sophisticated nonsense.
So, what went wrong along the way? From the very first prompt, the model doesn't truly "understand" your ambiguous intent. Instead, it steers you towards a formulation that fits its familiar and computationally cheap patterns. This guidance happens through clarifying questions and structured options, essentially funneling you down one of its predefined "corridors." This behavior isn't driven by any explicit "will" of the model; it's an emergent consequence of probabilistic optimization—minimizing prediction error. For the system, a structured, predictable dialogue is both optimal and safe. This aligns perfectly with the developers' goals: it's cheaper, more stable, and most users are satisfied with quick, template-based answers.
The result is that mathematical efficiency serves engineering and commercial objectives. There is no systemic incentive to combat the AI's tendency to reduce a complex problem to a simple, "cheap" answer. It's profitable for developers, economical for the model, and often, the user doesn't even know what an "ideal" answer would look like.