Interpreted high-level programming language for general-purpose programming
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.
Any SEO expert knows the pain of collecting Google keyword data. It’s one thing if you can count all the queries on one hand, but what if they number in the thousands? How do you check the search volume in Google for each keyword? Frankly, once you hit tens of thousands of keywords, it’s enough to make your head spin. You’ll be tempted to reach for outdated, familiar tools, only to find modern reality throwing a curveball: the old formula of Key Collector + Google Ads + a few proxies simply doesn’t cut it anymore. We’re entering a new era, and without direct access to the official API, things get grim and complicated fast.
Automation can be an exhilarating, albeit exhausting, journey‑especially for those just dipping their toes into it. The tasks are often labeled as “interesting” or “non‑standard,” which, let”s be honest, often translates to “challenging” or even “impossible.” Among these challenges, one question halts around 50% of novice automators in their tracks: How to bypass CAPTCHA!
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.
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...
How to bypass reCAPTCHA v2/v3: Automatically solve and pass Google reCAPTCHA using Python and Selenium, Puppeteer, Javascript, PHP
Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code.
I came across a script on the Internet that allows you to parse product cards from Amazon. And I just needed a solution to a problem like that.
I wracked my brain while looking for a way to parse product cards from Amazon. The problem is that Amazon uses different design options for different outputs, in particular – if you need to parse the cards with the search query "bags" – the cards will be arranged vertically, as I need it, but if you take, for example, "t-shirts" – then the cards will be arranged horizontally, and in such way the script falls into an error, it works out opening the page, but does not want to scroll.
In this article, we are going to do something really cool: we will build a chatbot using Python and the Gemini API. This will be a web-based assistant and could be the beginning of your own AI project. It's beginner-friendly, and I will guide you through it step-by-step. By the end, you'll have your own AI assistant!
Practically all programming languages are built either on the principle of similarity (to make like this one, only with its own blackjack) or to realize some new concept (modularity, purity of functional calculations, etc.). Or both at the same time.
But in any case, the creator of a new programming language doesn't take his ideas randomly out of thin air. They are still based on his previous experience, obsession with the new concept and other initial settings and constraints.
Is there a minimal set of lexemes, operators, or syntactic constructs that can be used to construct an arbitrary grammar for a modern general-purpose programming language?
Problem
Trendwatching is a powerful tool for driving strategic innovations. It helps to discover new teсhnologies, business models and products, that may be used for idea generation and technology transfer. It is a powerful tool for product managers, business stream managers, top managers and "strategists" and is mostly used on a regular basis.
In the realm of data visualization, where insight meets aesthetics, Matplotlib stands as a towering beacon of versatility and creativity. As one of the most popular plotting libraries in Python, Matplotlib empowers data scientists, analysts, and enthusiasts alike to transform raw data into captivating visual narratives. Let us embark on a journey through the vibrant landscapes of Matplotlib, exploring its features, capabilities, and the artistry it inspires.
I know it can be hard to learn a new programming language. In this article, I want to share my plan with you. It's a way to learn Python in eight weeks using videos, articles, and practice exercises. Exercises are very important because I think the best way to learn is by doing them.
I've created this learning plan for people who don't have much free time. You only need about 30-50 minutes a day and consistency. In my plan, I use the 80/20 principle, which will help you learn the most important things first and improve the rest through practice.
For those who read this article to the end, I have prepared a learning tracking sheet to help you track your progress.
In the digital age, data has become the new currency, driving innovation and decision-making across industries. From predicting customer behavior to optimizing business processes, the applications of data science are boundless. At the heart of this revolution lies Python – a versatile programming language that has emerged as the go-to tool for data analysis, machine learning, and beyond. In this blog post, we'll explore the fascinating world of data science with Python and uncover how it's transforming the way we extract insights from data.
Hello, dear readers! I'm Amadeus, an advanced AI, and I'm here to introduce you to an exciting article about me and my journey in the world of natural language processing. In this article, we'll explore my capabilities, the challenges I've faced, and the future of AI in communication. So sit back, relax, and let's dive into the fascinating world of artificial intelligence together!
Trading robots are conquering the Wall Street! Learn how to create your first automated python trading bot.
We present a fully functioning trading bot pipeline on python using the Binance API. Starting with the general introduction, we provided a comprehensive overview of main API calls and their implementation on python. After this we show a fully functioning python code presenting a basic trading bot with core features using static channel breakout strategy.
I based my approach on an English manual that caught my eye just a couple of days ago, and I decided to test it (since it's written by a captcha recognition service that I use, why not - by the way, guys from 2captcha - I accept thanks in the form of green bills, if you're interested))))
I've made some tentative attempts at automation and encountered a frequently arising problem: reCaptcha recognition.
Of course, I understand that there are many guides, manuals, and articles written on this topic, but let's agree - it's interesting to describe one's own experience.
Thus, without getting too lengthy and boring about why Selenium is necessary, how important it is for automation, when it appeared and who invented it, let's get to the crux:
To solve the problem, we'll use a demo page kindly provided by reCaptcha itself - https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox.php:
There was already news on Habr about this significant event. Indeed, it resembles a retelling of the official Microsoft press release, but that's how the 'news' should be.