Programming in Windows Courses Online

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During the course each participant will be able to perform Programming in Windows exercises on their remote desktop provided by Qwikcourse.

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Programming in Windows Training

Discover Embedded Control Systems Design / Design Patterns


Programming for real-time environments is hard and needs a good understanding of the complete system, hardware as well as software. In the ideal case, every part of the system is well described and easily understandable, but the real world seldom allows this ideal situation. Therefore, the system should be robust against the uncertainties in the system. design patterns are a useful support for all designers: they are generalized solutions to commonly occurring problems, based on experience of what has worked already in the past in a large number of systems. Patterns are also appropriate to create portable code that may be reused and adapted in several applications.

7 hours


Windows Programming


This course aims to be a comprehensive source for any developer who is interested in programming for the Windows platform. It starts at the lowest level, with the Win32 API (C and VB Classic) and then goes over to MFC (C++). Beyond these basic sections, it will cover COM, and the creation of ActiveX modules from a variety of languages. Next, it delves into the Windows DDK, and talk about programming device drivers for Windows platform. Finally, it moves on to the highest-level programming tasks, including shell extensions, shell scripting, and finally ASP and WSH. Other topics that will be discussed here are: Writing screen-savers, creating HTML help modules, and compiling DLL files. This course will focus on topics that are specific to Windows and avoids general programming topics. For related material the reader is encouraged to look into Wikibooks other works, they will cover general programming, ASM, C, C++, Visual Basic, and Visual Basic.NET, and other languages and concepts in greater detail. Appropriate links to these books are provided. The reader is assumed to have previous knowledge of the programming languages involved. Specifically, prior knowledge in C, C++, and Visual Basic is required for certain sections of this course.


Section 1: Windows Basics

  • Windows System Architecture

  • User Mode vs Kernel Mode

  • C and Win32 API

  • Handles and Data Types

  • Unicode

  • Dynamic Link Libraries (DLL)

  • Programming Windows With OSS Tools

  • Resource Scripts

Section 2: Win32 API and UI Controls

  • Message Loop Architecture

  • Interfacing (Mouse, keyboard, and timer messages)

  • Window Creation

  • User Interface Controls

  • GDI and Drawing

  • Dialog Boxes

  • Input-Output

  • File Management

  • Memory Subsystem (heaps, virtual memory)

  • Multitasking

  • Interprocess Communication

  • MDI Programs

  • Registry API

  • Security API

  • Winsock

Section 3: Microsoft Foundation Classes (MFC)

  • Microsoft Foundation Classes (MFC)

    • Classes Hierarchy

Section 4: Dynamic Data Exchange (DDE), ActiveX and COM

  • Dynamic Data Exchange (DDE)

  • COM and ActiveX

  • COM Programming

  • DCOM and COM+

  • Multi-language programming examples

  • OLE Programming

Section 5: Device Driver Programming

  • Device Driver Introduction

  • The DDK

  • Driver Structure

  • Driver API

  • Terminate and Stay Resident (TSR)

  • Virtual Device Drivers (VXD)

  • Windows Driver Model (WDM)

  • Vista Driver Migration

Section 6: Shell Programming

  • Programming Shell Extensions

  • Extending IE

  • Programming Screen-savers

  • Programming Services

  • Programming CMD aka Windows Batch Programming

    • Sample FTP script

  • Control Panel Applets

  • Windows Script Host

  • ASP

    • JScript

    • VBScript

    • PerlScript

  • Compiled HTML Help and Help API

35 hours


Competitive Programming


This course is about programming competitions and what you need to know in order to be competitive.... The primary reason why people compete in programming contests is that they enjoy it. For many, programming is actually fun, at least until they get a job doing it and their desire is burnt out of them. It is also a good way to meet people with similar interests to your own. But for those of you who need additional incentive, it is also a good way to increase others awareness of you. Major programming competitions are always monitored by people looking for new talent for their organizations, sometimes these are the people who actually fund the contest. High school programming contests (such as the ones sponsored by the BPA) often are to help prepare students for college or careers in computer programming. These contests often attract scouts from colleges looking to award scholarships to these individuals. For example, IBM is currently funding the ICPC, a contest that costs them millions annually. Why would they pay so much for a programming contest? They view it as an investment. It lets them filter through the talent and get to those that could potentially make IBM much more money in the long run. Before IBM the contest was funded by Microsoft for the same reasons. Organizations that feel like they can't quite afford the huge price tag associated with ICPC have begun to fund cheaper contests, such as TopCoder, and in the case of Google, running their own contest through TopCoder's technology. The first thing needed to get started is proficiency in a programming language and familiarity with a text editor/development environment. The two languages common to all of the above programming competitions are C++ and Java. These languages will be used throughout This course. There are many books and tutorials available to learn these languages, in addition to an unending amount of freely available code on the internet.


  • What Is This Course About?
  • Where Can I Compete?
  • Why Should I Compete?
  • How Do I Get Started?
  • Which Language Should I Use?
  • What Are The Contests Like?

7 hours


First steps towards system programming under MS-DOS 7


First steps towards system programming under MS-DOS7. This course is for those who intend to seize the most intimate secrets of modern computer technology. In order to be understood easily, narration starts with some elementary explanations and examples, but its main part presents professional level data with all relevant latest updates. These data will be beneficial for both students and teachers on computer technology as well as for computer maintenance staff, system programmers and developers of x86-platform microprocessor equipment. Contents

7 hours


Learn Trained Linearization


Interpreting Neural Networks by Reducing Nonlinearities during Training

This repo contains a short paper and sample code demonstrating a simple solution that makes it possible to extract rules from a neural network that employs Parametric Rectified Linear Units (PReLUs). We introduce a force, applied in parallel to backpropagation, that aims to reduce PReLUs into the identity function, which then causes the neural network to collapse into a smaller system of linear functions and inequalities suitable for review or use by human decision makers. As this force reduces the capacity of neural networks, it is expected to help avoid overfitting as well.

7 hours


Clojure Workshop


A Clojure workshop intended for Clojure beginners. Participants are not required to have any prior experience with Clojure. The workshop materials are intended to guide participants through the whole language and eco system, from theory to deploying an actual web application. The goal of this workshop is to have a person, with no prior knowledge about Clojure, fully capable of writing production ready Clojure after one day. The workshop has successfully been organized in:



Version 1.8.0 or higher The command: java -version should output: [...] version "1.8.0_XXX"


The command: lein -v should output: Leiningen 2.9.1 on Java 1.8.0_XXX [...]


Workshop set up

The workshop is split into 6 sections

  1. Introduction in Clojure
  2. Basic Development - REPL
  3. Backend Programming
  4. Frontend Programming with Clojurescript and Reagent
  5. Database (Extra credit)
  6. Deploying (Extra credit)

7 hours


Basic Sentiment Analysis


Created, trained, and evaluated a Neural Network model that, after the training, was able to predict movie reviews as either positive or negative reviews - classifying the sentiment of the review text. Dataset: The imported dataset is easily accessible in keras. After unloading I unpacked it to populate the training set and the test set. Both the training and test set have 25,000 examples each. When loading the dataset I set the number of words to 10,000.This means that only the most common 10,000 words from the bag of words were used and the rest were ignored. The developers at Keras already did some pre-processing in the data and had assigned unique numeric values to each word Decoding the Reviews: Decode numeric representation of the examples back into text. Decoding is just for my reference so that I can read a couple of reviews and see if their labels seem to make sense. For the decoding, I created a dictionary with key value pairs like the word index imported. Except this new dictionary had the word index values as keys and keys as values. Padding the Examples: The maximum length of 256 words was set for a review and 'the' as added to the reviews with fewer words to expand it's length to 256. 'the' was used as it is just an article and holds no inherent meaning. The input features are a bag of words and the model will make the predictions based on these features. If a particular set of features is a negative review or positive review. So, as it trains, it will start to assign some meaning to certain words which occur often in certain types of reviews. Maybe a word like wonderful will influence the model into thinking that the review is more positive, maybe a word like terrible will influence the network into thinking that the review is more negative. So, as it trains, it will assign how much influence and what influence various words in our vocabulary will have on the output. Word Embedding: An embedding layer will try to find some relation between various words. We are looking to find an embedding for 10,000 words, and we are trying to learn 16 features from those words. Then, all the words are represented as these feature vectors. The embedding layer will learn a 10000 by 16 dimensional Word Embedding where each word has a feature representation of 16 values. Creating and Training the Model: I used the sequential class from Keras. I also imported a few layers that were needed. We know, I needed an Embedding layer (used 16 dimension for the feature representations), needed a pooling layer which converted feature representations of 10,000 by 16 to a 16 dimension vector for each batch that was fed into a Dense layer with a rectified linear unit activation. Finally.Another dense layer with 'sigmoid' activation function to gives a binary classification output for the two classes. number of Epochs was set to 20. Prediction and Evaluation: We split the training set into sets; training set and validation set (20%) . Display the accuracy of our model during training for both the training and the validation set. after predictig the classes in testing set the model came out with the accuracy of 84.175%.

7 hours


Security Plus Training


A set of exercises to help you learn about using computer security tools, and maybe help pass the Security+ exam. No promises though.


The CompTIA Security+ certification body of knowledge covering a number of concepts and tools. In the (section) titled "Technologies and Tools", you are introduced to a number of command line tools for Windows, Linux, and MacOS that can help you explore and troubleshoot local networks as well as remote systems and hosts.

The Questions

It is my hope that I can provide a set of questions relating to each of the recommended tools that will give you real world examples and hands-on experience working with these tools. It is recommended you get familiar with these tools before you take the Security+ exam, according to Mike Chappel.

  • Using Ping

7 hours




inntt: Interactive NeuralNet Trainer for pyTorch

Finding the right hyperparameters when training deep learning models can be painful. The practitioner often ends up applying a trial/error approach to set them, based on the observation of some indicators (tr_loss, val_loss, etc.). Each little modification typically entails retraining from scratch. Interactive NeuralNet Trainer for pyTorch (INNTT) allows you to modify many parameters on the fly, interacting with the keyboard. Some routines/features currently supported: The inntt works currently only on the Linux's terminal; i'll fix that. I would also like to show some examples with growing-nets, this time controlled by the user.

7 hours




LSTM Training Tutorial for Tesseract 4 In order to successfully run the Tesseract 4 LSTM Training Tutorial, you need to have a working installation of Tesseract 4 and Tesseract 4 Training Tools and also have the training scripts and required traineddata files in certain directories. For running Tesseract 4, it is useful, but not essential to have a multi-core (4 is good) machine, with OpenMP and Intel Intrinsics support for SSE/AVX extensions. Basically it will still run on anything with enough memory, but the higher-end your processor is, the faster it will go.

7 hours


Mask Detection


A real-time facial mask detector implemented by training deep learning model with PyTorch Lightning. Detecting face masks from images can be achieved by training deep learning models to classify face images with and without masks. This task is actually a pipeline of two tasks: first we have to detect if a face is present in an image/frame or not. Second, if a face is detected, find out if the person is wearing a mask or not. For the first task, I have used MTCNN to detect human faces. There are other approaches as well, we can use a simple cascade classifier to achieve this task. For the second task, I used a pretrained mobilenet_v2 model by modifying and training its classifier layers to classifiy face images with/without masks. For the dataset, I collected some face images with masks from the internet and some from the RWMFD dataset. For the images of faces, I used real face images from the 'real and fake face' dataset. A mobilenet_v2 model was trained on 1700 images in total with PyTorch Lightning. The aim of this project was of course to implement a face mask detector but I wanted to give PyTorch Lightning a try. It definetely makes your PyTorch implementation more organised and neat but implementation of certain tasks may feel a bit complicated at first, at least for me.

7 hours


Docker Project IIEC Rise


DevOps is a set of practices that combines software development (Dev) and information-technology operations (Ops) which aims to shorten the systems development life cycle and provide continuous delivery with high software quality. Demand for the development of dependable, functional apps has soared in recent years. In a volatile and highly competitive business environment, the systems created to support, and drive operations are crucial. Naturally, organizations will turn to their in-house development teams to deliver the programs, apps, and utilities on which the business counts to remain relevant."

Docker is a set of platform as a service (PaaS) products that uses OS-level virtualization to deliver software in packages called containers. It is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. Docker is one of the tools that used the idea of the isolated resources to create a set of tools that allows applications to be packaged with all the dependencies installed and ran wherever wanted. Docker has two concepts that is almost the same with its VM containers as the idea, an image, and a container. An image is the definition of what is going to be executed, just like an operating system image, and a container is the running instance of a given image. Differences between Docker and VM: Docker containers share the same system resources, they dont have separate, dedicated hardware-level resources for them to behave like completely independent machines. They dont need to have a full-blown OS inside. They allow running multiple workloads on the same OS, which allows efficient use of resources. Since they mostly include application-level dependencies, they are pretty lightweight and efficient. A machine where you can run 2 VMs, you can run tens of Docker containers without any trouble, which means fewer resources = less cost = less maintenance = happy people.


  • Docker Commands

  • Docker search httpd

  • Docker run -i -t --name centos_server centos:latest i - interactive t - Terminal

  • Docker Install Apache Webserver - Dockerfile

7 hours




SMART is an open source application designed to help data scientists and research teams efficiently build labeled training datasets for supervised machine learning tasks. If you use SMART for a research publication, please consider citing: Development

The simplest way to start developing is to go to the envs/dev directory and run the rebuild script with ./ This will: clean up any old containers/volumes, rebuild the images, run all migrations, and seed the database with some testing data. The testing data includes three users root, user1, test_user and all of their passwords are password555. There is also a handful of projects with randomly labeled data by the various users.

Docker containers

This project uses docker containers organized by docker-compose to ease dependency management in development. All dependencies are controlled through docker.

Initial Startup

First, install docker and docker-compose. Then navigate to envs/dev and to build all the images run: docker-compose build Next, create the docker volumes where persistent data will be stored. docker volume create --name=vol_smart_pgdata docker volume create --name=vol_smart_data Then, migrate the database to ensure the schema is prepared for the application. docker-compose run --rm smart_backend ./

Workflow During Development

Run docker-compose up to start all docker containers. This will start up the containers in the foreground so you can see the logs. If you prefer to run the containers in the background use docker-compose up -d. When switching between branches there is no need to run any additional commands (except build if there is dependency change).

Dependency Changes

If there is ever a dependency change than you will need to re-build the containers using the following commands: docker-compose build docker-compose rm docker-compose up If your database is blank, you will need to run migrations to initialize all the required schema objects; you can start a blank backend container and run the migration django management command with the following command: docker-compose run --rm smart_backend ./

Custom Environment Variables

The various services will be available on your machine at their standard ports, but you can override the port numbers if they conflict with other running services. For example, you don't want to run SMART's instance of Postgres on port 5432 if you already have your own local instance of Postgres running on port 5432. To override a port, create a file named .env in the envs/dev directory that looks something like this:

Default is 5432


Default is 3000

EXTERNAL_FRONTEND_PORT=3001 The .env file is ignored by .gitignore.

7 hours


Face Detection And Recognition


Face Recognition: Understanding LBPH Algorithm Human beings perform face recognition automatically every day and practically with no effort. Although it sounds like a very simple task for us, it has proven to be a complex task for a computer, as it has many variables that can impair the accuracy of the methods, for example: illumination variation, low resolution, occlusion, amongst other. In computer science, face recognition is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current videos/cameras. Face recognition is different of face detection: Face Detection: it has the objective of finding the faces (location and size) in an image and probably extract them to be used by the face recognition algorithm. Face Recognition: with the facial images already extracted, cropped, resized and usually converted to grayscale, the face recognition algorithm is responsible for finding characteristics which best describe the image. The face recognition systems can operate basically in two modes: Verification or authentication of a facial image: it basically compares the input facial image with the facial image related to the user which is requiring the authentication. It is basically a 1x1 comparison. Identification or facial recognition: it basically compares the input facial image with all facial images from a dataset with the aim to find the user that matches that face. It is basically a 1xN comparison. There are different types of face recognition algorithms, for example: Eigenfaces (1991) Local Binary Patterns Histograms (LBPH) (1996) Fisherfaces (1997) Scale Invariant Feature Transform (SIFT) (1999) Speed Up Robust Features (SURF) (2006) Each method has a different approach to extract the image information and perform the matching with the input image. However, the methods Eigenfaces and Fisherfaces have a similar approach as well as the SIFT and SURF methods. Today we gonna talk about one of the oldest (not the oldest one) and more popular face recognition algorithms: Local Binary Patterns Histograms (LBPH). Objective The objective of this post is to explain the LBPH as simple as possible, showing the method step-by-step. As it is one of the easier face recognition algorithms I think everyone can understand it without major difficulties. Introduction Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. It was first described in 1994 (LBP) and has since been found to be a powerful feature for texture classification. It has further been determined that when LBP is combined with histograms of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some datasets.

7 hours


Rapid Object Detection Using Cascaded CNNs


The purpose of this course is to demonstrate the advantages of combining multiple CNNs to a common cascade structure. In contrast to training a single CNN only, the resulting classifier can be faster and more accurate at once. So far, the provided code has been applied successfully to the problem of face detection. It should be straight forward to adapt it to similar use cases though. This course is about binary(!) classification / detection only. Furthermore, the cascade gets especially fast for highly-unbalanced class distributions.

7 hours


Reinforcement Trading


Training of NNs to analyze Stock prices and of an RL agent that simulates trading based on the models provided


This whole endevour is not about making money trading stocks or currencies but is about having a practical application for learning about the following concepts:

  • Increase proficiency with git, writing docs and structuring as well as managing projects
  • Use a web APIs to acquire data
  • Reinforcement Learning (Probably Q-Learning since this has been applied with reasonable results)
  • RNN, LSTM, GRU and maybe attention based models
  • Baselines: Prophet, ARIMA,
  • Using AWS to scale up my computational power
  • Implement adequate ways of visualizing data
  • Document code to ensure reproducability
    • Sphinx
  • Deployment of the code
  • Speed up Python
    • Parallel computing
    • Profiling
    • Pypy
    • Cython
    • numba, dask
    • C-extensions

The goal is not to make money trading stocks and I don't claim that it would be wise to use anything from this project to do so

Optional goals:

  • Set up a SQL server to store data even if that is not necessary for the amounts of data used
  • Write a Kalman filter / boosting / ensemble to see if that could be a good idea

7 hours


Workplace Training


A simple set of cheatsheets for training people working in companies that build software. These cheatsheets are derived from training provided by Pablo Bawdekar which he developed at Quidnunc New York. They are intended for the following audiences: Each cheatsheet is intended to be used to prepare for and complete a task until the person is comfortable with the steps and ideas. It is useful to go over the cheatsheets periodically, particularly when training someone new.

7 hours




MTR combines the functionality of the 'traceroute' and 'ping' programs in a single network diagnostic tool. As mtr starts, it investigates the network connection between the host mtr runs on and a user-specified destination host. After it determines the address of each network hop between the machines, it sends a sequence of ICMP ECHO requests to each one to determine the quality of the link to each machine. As it does this, it prints

7 hours


Has Scope


Has scope allows you to map incoming controller parameters to named scopes in your resources. Imagine the following model called graduations: class Graduation < ActiveRecord::Base scope :featured, -> { where(featured: true) } scope :by_degree, -> degree { where(degree: degree) } scope :by_period, -> started_at, ended_at { where("started_at = ? AND ended_at = ?", started_at, ended_at) } end You can use those named scopes as filters by declaring them on your controller: class GraduationsController < ApplicationController has_scope :featured, type: :boolean has_scope :by_degree end Now, if you want to apply them to an specific resource, you just need to call apply_scopes: class GraduationsController < ApplicationController has_scope :featured, type: :boolean has_scope :by_degree has_scope :by_period, using: %i[started_at ended_at], type: :hash def index @graduations = apply_scopes(Graduation).all end end Then for each request: /graduations

=> acts like a normal request


=> calls the named scope and bring featured graduations


=> brings graduations in the given period


=> brings featured graduations with phd degree

You can retrieve all the scopes applied in one action with current_scopes method. In the last case, it would return: { featured: true, by_degree: 'phd' }.

7 hours




Athame patches your shell to add full Vim support by routing your keystrokes through an actual Vim process. Athame can currently be used to patch readline (used by bash, gdb, python, etc) and/or zsh (which doesn't use readline). Don't most shells already come with a vi-mode? Yes, and if you're fine with basic vi imitations designed by a bunch of Emacs users, feel free to use them. ...but for the true Vim fanatics who sacrifice goats to the modal gods, Athame gives you the full power of Vim.


Setting up Athame Readline

Option 1: (Arch Linux only) Use the AUR

7 hours




Selecta is a fuzzy selector. You can use it for fuzzy selection in the style of Command-T, ctrlp, etc. You can also use it to fuzzy select anything else: command names, help topics, identifiers; anything you have a list of. It was originally written to select things from vim, but it has no dependency on vim at all and is used for many other purposes. Its interface is dead simple:

  • Pass it a list of choices on stdin.
  • It will present a pretty standard fuzzy selection interface to the user (and block until they make a selection or kill it with ^C).
  • It will print the user's selection on stdout. For example, you can say: cat $(ls *.txt | selecta) which will prompt the user to fuzzy-select one of the text files in the current directory, then print the contents of whichever one they choose. It looks like this: Selecta supports these keys:

    Theory of Operation

    Selecta is unusual in that it's a filter (it reads from/to stdin/stdout), but it's also an interactive program (it accepts user keystrokes and draws a UI). It directly opens /dev/tty to do the latter. With that exception aside, Selecta is a normal, well-behaved Unix tool. If a selection is made, the line will be written to stdout with a single trailing newline. If no selection is made (meaning the user killed Selecta with ^C), it will write nothing to stdout and exit with status code 1. Because it's just a filter program, Selecta doesn't know about any other tools. Specifically, it does not: The ranking algorithm is: They must be in order, but don't have to be sequential. Case is ignored. Some concrete examples: because the matching substring is shorter ("le" vs. "ladde").

7 hours




Stately is a symbol font that makes is easy to create a map of the United States using only HTML and CSS. Each state can be styled independently with CSS for making simple visualizations. And since it's a font, it scales bigger and smaller while staying sharp as a tack.


map.svg      - SVG map used to create the font
assets\font  - Folder containing the web-font files
assets\sass  - Folder containing basic Sass files, including both Stately setup and stately.html demo customizations
assets\css   - Folder containing compiled CSS files
stately.html - Basic Demo
stately.svg  - SVG font file
stately.ttf  - TrueType font file

What is Stately?

Each state is a glyph within the font. Each state is positioned and sized relative to the the rest of the states, so that when each character is stacked on top of one another, it creates a full map. The pertinent characters are uppercase A-Z and lowercase a-z with lowercase y generating the District of Columbia and lowercase z generating a full US map. For modern browsers ligatures are available and a state's abbreviation is its ligature. For example, "va" generates the glyph of the state of Virginia and 'dc' the District of Columbia. Additionally, the ligature "usa" produces a character of the full US map.

Basic Use Case

You can use stately however you like, but some base Sass/CSS and HTML is included.

7 hours




Ghostunnel is a simple TLS proxy with mutual authentication support for securing non-TLS backend applications. Ghostunnel supports two modes, client mode and server mode. Ghostunnel in server mode runs in front of a backend server and accepts TLS-secured connections, which are then proxied to the (insecure) backend. A backend can be a TCP domain/port or a UNIX domain socket. Ghostunnel in client mode accepts (insecure) connections through a TCP or UNIX domain socket and proxies them to a TLS-secured service. In other words, ghostunnel is a replacement for stunnel. Supported platforms: Ghostunnel is developed primarily for Linux on x86-64 platforms, although it should run on any UNIX system that exposes SO_REUSEPORT, including Darwin (macOS), FreeBSD, OpenBSD and NetBSD. Ghostunnel also supports

7 hours




Zeitwerk is an efficient and thread-safe code loader for Ruby. Given a conventional file structure, Zeitwerk is able to load your project's classes and modules on demand (autoloading), or upfront (eager loading). You don't need to write require calls for your own files, rather, you can streamline your programming knowing that your classes and modules are available everywhere. This feature is efficient, thread-safe, and matches Ruby's semantics for constants. Zeitwerk is also able to reload code, which may be handy while developing web applications. Coordination is needed to reload in a thread-safe manner. The documentation below explains how to do this. The gem is designed so that any project, gem dependency, application, etc. can have their own independent loader, coexisting in the same process, managing their own project trees, and independent of each other. Each loader has its own configuration, inflector, and optional logger. Internally, Zeitwerk issues require calls exclusively using absolute file names, so there are no costly file system lookups in $LOAD_PATH. Technically, the directories managed by Zeitwerk do not even need to be in $LOAD_PATH. Furthermore, Zeitwerk does at most one single scan of the project tree, and it descends into subdirectories lazily, only if their namespaces are used.


Main interface for gems:

lib/my_gem.rb (main file)

require "zeitwerk" loader = Zeitwerk::Loader.for_gem loader.setup # ready! module MyGem end loader.eager_load # optionally Main generic interface: loader = loader.push_dir(...) loader.setup # ready! The loader variable can go out of scope. Zeitwerk keeps a registry with all of them, and so the object won't be garbage collected. You can reload if you want to: loader = loader.push_dir(...) loader.enable_reloading # you need to opt-in before setup loader.setup loader.reload and you can eager load all the code: loader.eager_load It is also possible to broadcast eager_load to all instances: Zeitwerk::Loader.eager_load_all

File structure

To have a file structure Zeitwerk can work with, just name files and directories after the name of the classes and modules they define: lib/my_gem.rb -> MyGem lib/my_gem/foo.rb -> MyGem::Foo lib/my_gem/bar_baz.rb -> MyGem::BarBaz lib/my_gem/woo/zoo.rb -> MyGem::Woo::Zoo Every directory configured with push_dir acts as root namespace. There can be several of them. For example, given loader.push_dir(Rails.root.join("app/models")) loader.push_dir(Rails.root.join("app/controllers")) Zeitwerk understands that their respective files and subdirectories belong to the root namespace: app/models/user.rb -> User app/controllers/admin/users_controller.rb -> Admin::UsersController Alternatively, you can associate a custom namespace to a root directory by passing a class or module object in the optional namespace keyword argument. For example, Active Job queue adapters have to define a constant after their name in ActiveJob::QueueAdapters. So, if you declare require "active_job" require "active_job/queue_adapters" loader.push_dir("#{dir}/adapters", namespace: ActiveJob::QueueAdapters) your adapter can be stored directly in that directory instead of the canonical #{__dir__}/active_job/queue_adapters. Please, note that the given namespace must be non-reloadable, though autoloaded constants in that namespace can be. That is, if you associate app/api with an existing Api module, that module should not be reloadable. However, if the project defines and autoloads the class Api::V2::Deliveries, that one can be reloaded.

Implicit namespaces

Directories without a matching Ruby file get modules autovivified automatically by Zeitwerk. For example, in app/controllers/admin/users_controller.rb -> Admin::UsersController Admin is autovivified as a module on demand, you do not need to define an Admin class or module in an admin.rb file explicitly.

Explicit namespaces

Classes and modules that act as namespaces can also be explicitly defined, though. For instance, consider app/models/hotel.rb -> Hotel app/models/hotel/pricing.rb -> Hotel::Pricing There, app/models/hotel.rb defines Hotel, and thus Zeitwerk does not autovivify a module. The classes and modules from the namespace are already available in the body of the class or module defining it: class Hotel < ApplicationRecord include Pricing # works end An explicit namespace must be managed by one single loader. Loaders that reopen namespaces owned by other projects are responsible for loading their constants before setup.

Collapsing directories

Say some directories in a project exist for organizational purposes only, and you prefer not to have them as namespaces. For example, the actions subdirectory in the next example is not meant to represent a namespace, it is there only to group all actions related to bookings: booking.rb -> Booking booking/actions/create.rb -> Booking::Create To make it work that way, configure Zeitwerk to collapse said directory: loader.collapse("booking/actions") This method accepts an arbitrary number of strings or Pathname objects, and also an array of them. You can pass directories and glob patterns. Glob patterns are expanded when they are added, and again on each reload. To illustrate usage of glob patterns, if actions in the example above is part of a standardized structure, you could use a wildcard: loader.collapse("*/actions")

Nested root directories

Root directories should not be ideally nested, but Zeitwerk supports them because in Rails, for example, both app/models and app/models/concerns belong to the autoload paths. Zeitwerk detects nested root directories, and treats them as roots only. In the example above, concerns is not considered to be a namespace below app/models. For example, the file: app/models/concerns/geolocatable.rb should define Geolocatable, not Concerns::Geolocatable.

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Fundamentals of ClckBotCreator


Write bots without knowing programming languages

This program was originally conceived as an auto clicker but soon turned into a program to create bots for Windows. It has 4 commands(mouse-clicking, setting cursor position, timer, keyboard clicking) with which you can automate any process. Also, there are a couple of tools in it that will help you comfortably write bots. The version of the app is 0.2.1 App run as administrator. Syntax: 'left' or 'right' mouse button 2.setc: x - mouse position y - mouse position 3.timer: milliseconds 4.print: C++ code of key 

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Learn Macronize a macro builder and recorder


Macronize, a precise macro builder to automate your apps on Windows

Macronize, a macro builder, a mouse click and key press recorder with which you can automate your Windows programs and apps throught recording or adding precise steps (macro actions) and playing them back. A macro is a series of steps to automate your computer. With a macro you take over the control of your programs and apps so you don't have to do things manually like copying/pasting. Or recording a YouTube video and at the same time clicking to show the users what to do.

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Work around with AVRpal NET


A Windows-based GUI for popular avrdude command-line utility for AVR microcontroller Programming. It has got many features with a lightweight easy to understand interface. AVRdude is provided with it. It previously used LibUSBdotnet but now it uses LibUSBK for compatibility. Supports only USBasp programmer but if you can code, you can change it to work with your programmer. 

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Fundamentals of switchmap


Creates web pages that show information about Ethernet switches.

Switchmap is a Perl program that creates HTML pages that show information about a set of Ethernet switches. It uses SNMP to gather data from the switches. 

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Is learning Programming in Windows hard?

In the field of Programming in Windows learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.

Is Programming in Windows a good field?

For now, there are tremendous work opportunities for various IT fields. Most of the courses in Programming in Windows is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.

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