Python *

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 😅.

Hi, my name is Mikhail Emelyanov, I’m a Python programmer and I would like to show you my pet project — Flywheel, a micro-platform for learning foreign languages, a mixture of Duolingo and Anki, an application that can teach you to properly write in Spanish (or any other language you’re studying). Flywheel’s source code is available on GitHub.

As you may know, generalized knowledge of a foreign language can be broken down into four relatively independent components: reading, writing, listening, and speaking. Unfortunately, training one of these abilities has no direct effect on the other components, so, for example, by developing our reading skills, the effect on our writing skills is quite indirect. Flywheel is a ‘sharpener’ specifically for written Spanish.

If you’ve ever used Duolingo, you should have some idea of the format in which you’ll be studying. The formula is simple: here’s a phrase, translate it into the other language; the app will remember the last time you translated a phrase and how successful you were at it; and depending on the accuracy of your answer, it will determine when you should do the same phrase again. In my opinion, Duolingo and its approach are brilliant. However… There are certain aspects that somewhat spoil the learning experience, and Flywheel was specifically designed to address them.

Explaining main algorithm.

For a while I’ve been thinking of writing a scientific article. I wanted it to have certain utility.

Morse code is binary: it takes only two values – either dot (short) or hyphen (long). I figured out that short (s) can stand for two-eye blinking whilst long (l) can indicate left-eye blinking. Another question emerged: how to understand when does one-symbol recording stop?

Empty space between two symbols can be presented by right-eye blinking – r. If I input singly symbol of short (dot) and long (hyphen), I will blink my right eye once to indicate the space between two symbols.

To separate independent words, one has to blink her right eye twice and get rr.

Hence, I have collected an ordered set of symbols – r, l, s, - that can be converted into a full-fledged text. Once I accomplish the transformation, I get an answer.

Task: To provide automation for transfer of large number of files.

Source - computer with autotest codebase.

Receiver - gateway for industrial data processing.

Test receiver - second PC with installed vsftpd service.

The point of this article is to explore Lambda functions, their dirrerences from regular functions and how they are implemented, based on C++, Python and Java programming languages.

Throughout this article I will be using godbolt.org to compile code and see machine code or byte code.

On the Internet and in non-fiction literature you can often find various mathematical tricks. The Collatz conjecture leaves all such tricks behind. At first glance, it may seem like some kind of a trick with a catch. However, there is no catch. You think of a number and repeat one of two arithmetic operations for it several times. Surprisingly, the result of these actions will always be the same. Or, may be not always?

Programming textbooks usually do not indulge us with variety of examples. In most manuals, exercises are similar to each other and not particularly interesting: create another address book, draw a circle using turtle, develop a website for a store selling some kind of "necessary" advertising nonsense. Too far from the authentic imitation of "The Matrix". Although…

How about taking over the control and starting to invent exercises yourself?

Would you like to write your own personal little "Matrix"? Of course, not the one with skyscrapers, stylish phones of the time, and the ubiquitous invincible Agent Smiths. We will need a couple of more months of learning for that. But any beginner programmer can write a model of the cult splash screensaver with the green streams of digits flowing down the screen. Let's try to creat it in the "great and mighty" Python.

GIMP (GNU Image Manipulation Program) is a free and open-source image editing software that provides users with a wide range of tools for editing and manipulating digital images. Python is a high-level programming language that is often used for scripting and automation tasks. The combination of GIMP and Python provides a powerful platform for users to create custom image editing plugins that can automate repetitive tasks, extend the functionality of GIMP, and customize the software to suit their specific needs.

Python provides a flexible and easy-to-learn language for writing GIMP plugins. GIMP provides an API (Application Programming Interface) that allows Python scripts to interact with the image editing program Python plugins for GIMP can be used for a wide range of tasks, including automating repetitive tasks, enhancing the functionality of GIMP, and customizing the software to suit specific needs. Some examples of tasks that can be automated using Python plugins include batch processing of images, resizing and cropping of images, and converting file formats.

Plugins can also add new features to GIMP, such as custom brushes, filters, and effects. Additionally, plugins can be used to create custom user interfaces that enable users to interact with GIMP in new and unique ways.

In this article, we will briefly review a technology that underlies ChatGPT — embeddings. Also we’ll write a simple intelligent search in a codebase of a project.

Video recording of our webinar about dstack and reproducible ML workflows, AVL binary tree operations, Ultralytics YOLOv8, training XGBoost, productionize ML models, introduction to forecasting ensembles, domain expansion of image generators, Muse, X-Decoder, Box2Mask, RoDynRF, AgileAvatar and more.

Unlock the power of Transformer Neural Networks and learn how to build your own GPT-like model from scratch. In this in-depth guide, we will delve into the theory and provide a step-by-step code implementation to help you create your own miniGPT model. The final code is only 400 lines and works on both CPUs as well as on the GPUs. If you want to jump straight to the implementation here is the GitHub repo.

Transformers are revolutionizing the world of artificial intelligence. This simple, but very powerful neural network architecture, introduced in 2017, has quickly become the go-to choice for natural language processing, generative AI, and more. With the help of transformers, we've seen the creation of cutting-edge AI products like BERT, GPT-x, DALL-E, and AlphaFold, which are changing the way we interact with language and solve complex problems like protein folding. And the exciting possibilities don't stop there - transformers are also making waves in the field of computer vision with the advent of Vision Transformers.

The goal of paper is to demonstrate non-trivial approaches to give statistical estimate for forecast result. Idea comes from probability cone concept. A probability cone is an indicator that forecasts a statistical distribution from a set point in time into the future. This acticle provide alternative approaches using machine learning, regression analysis.

InvokeAI 2.2 is now available to everyone. This update brings in exciting features, like UI Outpainting, Embedding Management and more. See highlighted updates below, or the full release notes for everything included in the release.

Hello! My name is Mikhail Emelyanov, I am embedded software engineer, and I was inspired to write this little roadmap on the capabilities of Python language by a certain commonality among the existing Python tutorials found on the web.

The usual suggestions to study, say, “Algorithms and Data Structures” or “Databases” are especially jarring. You can spend years studying these topics, and even after decades you'd still be able to find something you didn't know yet even without ever venturing outside the scope of Algorithms!

Using video game analogies, we can say that novice programmers often stand on the shore of the lake of boiling lava with an island with the ever-coveted jobs in the center, while the islands in between, which you have to jump on, gradually increasing your skills in successive mini-quests, are either missing, or arranged haphazardly, or their fairly smooth sequence breaks off, never having managed to get you any farther from the shore. Let's try to build a path of hint islands, a number of which, although not without effort, will finally allow us to reach our goal.

Great for seamless patterns, abstract drawings, and watercolor-styled images. How to use it and train a neural network on your own pictures?

Download the model here: https://huggingface.co/netsvetaev/netsvetaev-free

The InvokeAI team is excited to share our latest feature release, with a set of new features, UI enhancements, and CLI capabilities.

Author: Denis Zherdetskiy