Interpreted high-level programming language for general-purpose programming
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.
In this tutorial, I’ll explain in simple terms what AI, AI agents, and workflows are, and then I’ll walk you through building your very first AI agent in Python using Google’s Agent Development Kit (ADK). By the end, you’ll understand the differences between these concepts and have a working content-assistant agent you can run from your terminal or a web interface.
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.
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 You’ll be tempted to reach for outdated, familiar tools, only to find modern reality throwing a curveball: the old formula of Key Collector + Google Ads + a few proxies simply doesn’t cut it anymore.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.
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.
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.
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.
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.
The Python language has two kind of functions — normal functions that you would use in most cases, and async functions. The latter functions are used when performing network IO in an asynchronous manner. The problem with this division is that async functions can only be called from other async functions. Normal functions, on the other hand, can be called from any functions — however, if you call a normal function that does a blocking operation from an async function, it will block the whole event loop and all your coroutines. These limitations usually mean that when writing an using Python`s asyncio, you can`t use any of the IO libraries that you use when writing a synchronous application, and vice versa, unless a library supports usage both in sync and async applications.
Now, the question is, in case you are developing a large and complex library, that, say, allows users to interact with relational databases, abstracting away (some of) the differences between the SQL syntax and other aspects of these databases, and abstracting away the differences between the drivers for that database, how do you support both sync and async usage of your library without duplicating the code of your library? The way sqlalchemy is organized is that regardless of what database and driver for it you are using, you will be calling functions and methods related to Engine, Connection, etc classes, which will do some general work independent of database, then apply the logic specific to your database and finally, call the functions of your database driver to actually communicate with the database. If you are using Python`s asyncio, the database driver will expose async functions and methods, but the rest of the library that is driver‑independent would ideally remain the same. However, the issue is that that you can`t call the async functions of the driver from the normal functions of the core of the library.
Introduction
Hello Habr! This is a translation of my first article, which was born due to the fact that I once played with the types of meterpreter payload from the Metasploit Framework and decided to find a way to detect it in the Windows OS family.
Analysis
I will try to present everything in an accessible and compact way without delving into all the work. To begin with, I decided to create the nth number of useful loads (windows/meterpreter/reverse_tcp, shell/bind_tcp, shell_hidden_bind_tcp, vncinject/reverse_tcp, cmd/windows/reverse_powershell) to analyze what will happen in the system after their injection.
Let’s imagine you need access to the real-time data of some smart contracts on Ethereum (or Polygon, BSC, etc.) like Uniswap or even PEPE coin to analyze its data using the standard data scientist/analyst tools: Python, Pandas, Matplotlib, etc. In this tutorial, I’ll show you more sophisticated data access tools that are more like a surgical scalpel (The Graph subgraphs) than a well-known Swiss knife (RPC node access) or hammer (ready-to-use APIs). I hope my metaphors don’t scare you ?.
