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Snippets and Libraries Training


Know Loguru

About

Loguru is a library which aims to bring enjoyable logging in Python. Did you ever feel lazy about configuring a logger and used print() instead?... I did, yet logging is fundamental to every application and eases the process of debugging. Using Loguru you have no excuse not to use logging from the start, this is as simple as from loguru import logger. Also, this library is intended to make Python logging less painful by adding a bunch of useful functionalities that solve caveats of the standard loggers. Using logs in your application should be an automatism, Loguru tries to make it both pleasant and powerful.


7 hours

$1,990

Learn Coconut

About

Coconut

Coconut (coconut-lang.org) is a variant of Python_ that adds on top of Python syntax new features for simple, elegant, Pythonic functional programming. _ Coconut Coconut is developed on GitHub and hosted on PyPI. Installing Coconut is as easy as opening a command prompt and entering:: pip install coconut after which the entire world of Coconut will be at your disposal. To help you get started, check out these links for more information about Coconut: Credits +++++++ Contributors This project exists thanks to all the people who contribute! Become a contributor. Contributor Backers Thank you to all our backers! Become a backer. Backer Sponsors Support Coconut by becoming a sponsor. Your logo will show up here with a link to your website. Become a sponsor. _ Sponsor


7 hours

$1,990

Discover Simulator Remote Notifications

About

Simulator Remote Notifications

Simulator Remote Notifications is a library to send mock remote notifications to the iOS simulator. The library extends UIApplication by embedding a mini server that listen for UDP packets containing JSON-formated payload, and a service to send notifications to the mini server. This project includes the iOS Simulator Notifications MacOSX app to help you send the mock notifications. Note that SimulatorRemoteNotifications does not send notification through Apple's Push Service.


7 hours

$1,990

Explore LazyList

About

LazyList

A simple library to display images in Android ListView. Images are being downloaded asynchronously in the background. Images are being cached on SD card and in memory. Can also be used for GridView and just to display images into an ImageView. Originally published here.

Basic Usage

ImageLoader imageLoader=new ImageLoader(context); imageLoader.DisplayImage(url, imageView); Don't forget to add the following permissions to your AndroidManifest.xml:

<uses-permission android:name="android.permission.INTERNET"/>

 

<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>

Please create only one instance of ImageLoader and reuse it all around your application. This way image caching will be much more efficient.


7 hours

$1,990

Learn Jnativehook

About

JNativeHook is a library to provide global keyboard and mouse listeners for Java. This will allow you to listen for global shortcuts or mouse motion that would otherwise be impossible using pure Java. To accomplish this task, JNativeHook leverages platform-dependent native code through Java's native interface to create low-level system-wide hooks and deliver those events to your application. The following events are available via their respective listeners.

  • Key Press Events
  • Key Release Events
  • Key Typed Events
  • Mouse Down Events
  • Mouse Up Events
  • Mouse Click Events
  • Mouse Move Events
  • Mouse Drag Events
  • Mouse Wheel Events In addition to global event listners, this library has the ability to post native events back to the native operating system.

7 hours

$1,990

Discover UAObfuscatedString

About

UAObfuscatedString

UAObfuscatedString is a simple and lightweight category of NSMutableString that allows you to prevent sensitive strings from appearing in your compiled binary. Without some sort of obfuscation, strings like backend API methods and urls, API keys and other sensitive data can be extracted by utilizing various command-line tools such as strings.


7 hours

$1,990

Fundamentals of BreezySLAM

About

BreezySLAM

this course contains everything you need to start working with Lidar SLAM in Python. (There is also support for Matlab, C++, and Java; however, because of the popularity of Python for this kind of work, I am no longer updating the code for those languages.) BreezySLAM works with Python 3 on Linux and Mac OS X, and with C++ on Linux and Windows. By using Python C extensions, we were able to get the Python and Matlab versions to run as fast as C++. For maximum efficiency on 32-bit platforms, we use Streaming SIMD extensions (Intel) and NEON (ARMv7) in the compute-intensive part of the code.

BreezySLAM was inspired by the Breezy approach to Graphical User Interfaces developed by my colleague Ken Lambert: an object-oriented Application Programming Interface that is simple enough for beginners to use, but that is efficient enough to scale-up to real world problems; for example, the mapping of an entire floor of a house, shown in the image above-right, made by a BreezySLAM user. As shown in the following code fragment, the basic API is extremely simple: a constructor that accepts Lidar parameters and the size of the map (pixels) and mapping area (meters); a method for updating with the current scan; a method that returns the current robot position; and a method for retrieving the current map as a byte array.


7 hours

$1,990

Discover Robintrack

About

Robintrack

Robintrack is a tool for viewing data about the popularity of various stocks on the Robinhood brokerage. It allows users to view information about which are the most (and least) popular stocks held by its users and view trends over time. When combined with price history and other forms of data, this can be a useful tool for analyzing market sentiment.

Components

Scraper

The scraper periodically scrapes the Robinhood API to pull popularity and price data for all of the stocks that it offers. A server/worker model is used, with RabbitMQ being used a work queue. Once scraped, the data is stored in MongoDB.

Backend

The backend is a Rails application that mainly serves as a shim to the MongoDB database. It serves the API used by the frontend.

Frontend

The frontend is a single-page React application that provides users with access to the site's data via data tables and visualizations.

Installation

You'll need a MongoDB instance running and a RabbitMQ instance for the scraper. Configuration for the backend is handled via environment variables; the MONGO_HOST and MONGO_PORT variables are read at runtime. The scraper takes the same environment variables for MongoDB and everything else via command line arguments. Use the --help flag for both the master and worker scripts to view available config options.

There also must be a Redis instance accessible to the instance with an address defined in the REDIS_HOST environment variable. This is used for caching API requests that do not change between runes of the scraper.


7 hours

$1,990

Know Smart App Rate

About

Smart App Rate

Smart app rate dialog for Android which takes user rating into consideration. If the user rates the app below the defined threshold rating, the dialog will change into a feedback form. Otherwise, It will take the user to the Google PlayStore.

Features

If you want the dialog to appear on the Nth session of the app, just add the session(N) to the dialog builder method and move the code to the onCreate() method of your Activity class. The dialog will appear when the app is opened for the Nth time.


7 hours

$1,990

Learn Dentaku

About

Dentaku

DESCRIPTION Dentaku is a parser and evaluator for a mathematical and logical formula language that allows run-time binding of values to variables referenced in the formulas. It is intended to safely evaluate untrusted expressions without opening security holes. EXAMPLE This is probably simplest to illustrate in code: calculator = Dentaku::Calculator.new calculator.evaluate('10 * 2')

=> 20

Okay, not terribly exciting. But what if you want to have a reference to a variable, and evaluate it at run-time? Here's how that would look: calculator.evaluate('kiwi + 5', kiwi: 2)

=> 7

To enter a case sensitive mode, just pass an option to the calculator instance: calculator.evaluate('Kiwi + 5', Kiwi: -2, kiwi: 2)

=> 7

calculator = Dentaku::Calculator.new(case_sensitive: true) calculator.evaluate('Kiwi + 5', Kiwi: -2, kiwi: 2)

=> 3

You can also store the variable values in the calculator's memory and then evaluate expressions against those stored values: calculator.store(peaches: 15) calculator.evaluate('peaches - 5')

=> 10

calculator.evaluate('peaches >= 15')

=> true

For maximum CS geekery, bind is an alias of store. Dentaku understands precedence order and using parentheses to group expressions to ensure proper evaluation: calculator.evaluate('5 + 3 * 2')

=> 11

calculator.evaluate('(5 + 3) * 2')

=> 16

The evaluate method will return nil if there is an error in the formula. If this is not the desired behavior, use evaluate!, which will raise an exception. calculator.evaluate('10 * x')

=> nil

calculator.evaluate!('10 * x') Dentaku::UnboundVariableError: Dentaku::UnboundVariableError Dentaku has built-in functions (including if, not, min, max, sum, and round) and the ability to define custom functions (see below). Functions generally work like their counterparts in Excel: calculator.evaluate('SUM(1, 1, 2, 3, 5, 8)')

=> 20

calculator.evaluate('if (pears < 10, 10, 20)', pears: 5)

=> 10

calculator.evaluate('if (pears < 10, 10, 20)', pears: 15)

=> 20

round can be called with or without the number of decimal places: calculator.evaluate('round(8.2)')

=> 8

calculator.evaluate('round(8.2759, 2)')

=> 8.28

round follows rounding rules, while roundup and rounddown are ceil and floor, respectively. If you're too lazy to be building calculator objects, there's a shortcut just for you: Dentaku('plums * 1.5', plums: 2)

=> 3.0

PERFORMANCE The flexibility and safety of Dentaku don't come without a price. Tokenizing a string, parsing to an AST, and then evaluating that AST are about 2 orders of magnitude slower than doing the same math in pure Ruby! The good news is that most of the time is spent in the tokenization and parsing phases, so if performance is a concern, you can enable AST caching: Dentaku.enable_ast_cache! After this, Dentaku will cache the AST of each formula that it evaluates, so subsequent evaluations (even with different values for variables) will be much faster -- closer to 4x native Ruby speed. As usual, these benchmarks should be considered rough estimates, and you should measure with representative formulas from your application. Also, if new formulas are constantly introduced to your application, AST caching will consume more memory with each new formula. BUILT-IN OPERATORS AND FUNCTIONS Math: +, -, *, /, %, ^, |, & Also, all functions from Ruby's Math module, including SIN, COS, TAN, etc. Comparison: , `=`,, !=, =, Logic: IF, AND, OR, NOT, SWITCH Numeric: MIN, MAX, SUM, AVG, COUNT, ROUND, ROUNDDOWN, ROUNDUP Selections: CASE (syntax see spec) String: LEFT, RIGHT, MID, LEN, FIND, SUBSTITUTE, CONCAT, CONTAINS RESOLVING DEPENDENCIES If your formulas rely on one another, they may need to be resolved in a particular order. For example: calc = Dentaku::Calculator.new calc.store(monthly_income: 50) need_to_compute = { income_taxes: "annual_income / 5", annual_income: "monthly_income * 12" } In the example, annual_income needs to be computed (and stored) before income_taxes. Dentaku provides two methods to help resolve formulas in order:

Calculator.dependencies

Pass a (string) expression to Dependencies and get back a list of variables (as :symbols) that are required for the expression. Dependencies also takes into account variables already (explicitly) stored into the calculator. calc.dependencies("monthly_income * 12")

=> []

(since monthly_income is in memory)

calc.dependencies("annual_income / 5")

=> [:annual_income]

Calculator.solve! / Calculator.solve

Have Dentaku figure out the order in which your formulas need to be evaluated. Pass in a hash of {eventual_variable_name: "expression"} to solve! and have Dentaku resolve dependencies (using TSort) for you. Raises TSort::Cyclic when a valid expression order cannot be found. calc = Dentaku::Calculator.new calc.store(monthly_income: 50) need_to_compute = { income_taxes: "annual_income / 5", annual_income: "monthly_income * 12" } calc.solve!(need_to_compute)

=> {annual_income: 600, income_taxes: 120}

calc.solve!( make_money: "have_money", have_money: "make_money" }

=> raises TSort::Cyclic

solve! will also raise an exception if any of the formulas in the set cannot be evaluated (e.g. raise ZeroDivisionError). The non-bang solve method will find as many solutions as possible and return the symbol :undefined for the problem formulas. INLINE COMMENTS If your expressions grow long or complex, you may add inline comments for future reference. This is particularly useful if you save your expressions in a model. calculator.evaluate('kiwi + 5 / This is a comment /', kiwi: 2)

=> 7

Comments can be single or multi-line. The following are also valid. /*

  • This is a multi-line comment / / This is another type of multi-line comment */ EXTERNAL FUNCTIONS I don't know everything, so I might not have implemented all the functions you need. Please implement your favorites and send a pull request! Okay, so maybe that's not feasible because:
    1. You can't be bothered to share
    2. You can't wait for me to respond to a pull request, you need it NOW()
    3. The formula is the secret sauce for your startup Whatever your reasons, Dentaku supports adding functions at runtime. To add a function, you'll need to specify a name, a return type, and a lambda that accepts all function arguments and returns the result value. Here's an example of adding a function named POW that implements exponentiation.

      c = Dentaku::Calculator.new c.add_function(:pow, :numeric, ->(mantissa, exponent) { mantissa ** exponent }) c.evaluate('POW(3,2)')

      => 9

      c.evaluate('POW(2,3)')

      => 8

      Here's an example of adding a variadic function: c = Dentaku::Calculator.new c.add_function(:max, :numeric, ->(*args) { args.max }) c.evaluate 'MAX(8,6,7,5,3,0,9)'

      => 9

      (However both of these are already built-in -- the ^ operator and the MAX function) Functions can be added individually using Calculator#add_function, or en masse


7 hours

$1,990

Discover Jest Fetch Mock

About

Jest Fetch Mock

Fetch is the canonical way to do HTTP requests in the browser, and it can be used in other environments such as React Native. Jest Fetch Mock allows you to easily mock your fetch calls and return the response you need to fake the HTTP requests. It's easy to setup and you don't need a library like nock to get going and it uses Jest's built-in support for mocking under the surface. This means that any of the jest.fn() methods are also available. For more information on the jest mock API, check their docs here It currently supports the mocking with the cross-fetch polyfill, so it supports Node.js and any browser-like runtime.

Contents


7 hours

$1,990

Know Insetter

About

Insetter

Insetter is a library to help apps handle The library contains implementations of many of the concepts described in our blog post. There are a number of libraries available:

insetter

The base library which is written in Java, and provides an easy-to-use instances: Insetter.builder() // This will apply the system window insets as padding to left, bottom and right of the view, // maintaining the original padding (from the layout XML, style, etc) .applySystemWindowInsetsToPadding(Side.LEFT | Side.BOTTOM | Side.RIGHT) // This is a shortcut for view.setOnApplyWindowInsetsListener(builder.build()) .applyToView(view);

insetter-ktx

A Kotlin extension library, providing Kotlin-specific functionality. This library contains extension functions allowing easy access to the helper functions from the base library: bottomNav.applySystemWindowInsetsToPadding(bottom = true) btnConfirm.applySystemWindowInsetsToMargin(bottom = true, right = true) A [Data Binding][databinding] extension library, providing [Data Binding][databinding] specific functionality. This primarily contains binding adapters, which allow access to the helper functions from your layouts:

You can read more information here. An extension library which provides versions of commonly used ViewGroups with enhanced inset handling. Currently this library is focusing on building upon A example of a widget is InsetterConstraintLayout, which enables new attributes to define inset behavior on child views. The behavior enabled through InsetterConstraintLayout is similar to that provided by the insetter-dbx library, but without the requirement of using data-binding.

You can read more information here.

Attention

The library is being written to production quality, but it is not adhering to semantic versioning, mean we may change the API if needed, though we'll try not to. We're using this course to allow quick and easy prototyping. 


7 hours

$1,990

Know Telemetry

About

Telemetry

Telemetry is a dynamic dispatching library for metrics and instrumentations. It is lightweight, small and can be used in any Erlang or Elixir project. In a nutshell, you register a custom module and function to be invoked for certain events, which are executed whenever there is such event. Event name is a list of atoms. Each event is composed of a numeric value and can have metadata attached to it. Let's see an example. Imagine that you have a web application and you'd like to log latency and response status for each incoming request. With Telemetry, you can build a module which does exactly that whenever a response is sent. The first step is to execute a measurement. In Elixir: :telemetry.execute( %{latency: latency}, %{request_path: path, status_code: status} ) In Erlang: telemetry:execute(

{latency => Latency},

{request_path => Path, status_code => Status}

) Then you can create a module to be invoked whenever the event happens. In Elixir: defmodule LogResponseHandler do require Logger def handle_event([:web, :request, :done], measurements, metadata, _config) do Logger.info("[#{metadata.request_path}] #{metadata.status_code} sent in #{measurements.latency}") end end In Erlang: handle_event([web, request, done], #{latency := Latency}, #{request_path := Path, status_code := Status}, _Config) -> ?LOG_INFO("[~s] ~p sent in ~p", [Path, Status, Latency]). Important note: The handle_event callback of each handler is invoked synchronously on each telemetry:execute call. Therefore, it is extremely important to avoid blocking operations. If you need to perform any action that it is not immediate, consider offloading the work to a separate process (or a pool of processes) by sending a message. Finally, all you need to do is to attach the module to the executed event. In Elixir: :ok = :telemetry.attach(

unique handler id

"log-response-handler", &LogResponseHandler.handle_event/4, nil ) In Erlang: ok = telemetry:attach( %% unique handler id , fun log_response_handler:handle_event/4, ) You might think that it isn't very useful, because you could just as well write a log statement instead of Telemetry.execute/3 call - and you would be right! But now imagine that each Elixir library would publish its own set of events with information useful for introspection. Currently each library rolls their own instrumentation layer - Telemetry aims to provide a single interface for these use cases across whole ecosystem. In order to provide uniform events that capture the start and end of discrete events, it is recommended that you use the telemetry:span/3 call. This function will generate a start event and a stop or exception event depending on whether the provided function successfully executed or raised and error. Under the hood, the telemetry:span/3 function leverages the telemetry:execute/3 function, so all the same usage patterns apply. If an exception does occur, an EventPrefix ++ [exception] event will be emitted and the caught error will be re-raised. The measurements for the EventPrefix ++ [start] event will contain a key called system_time which is derived by calling erlang:system_time(). For EventPrefix ++ [stop] and EventPrefix ++ [exception] events, the measurements will contain a key called duration, whose value is derived by calling erlang:monotonic_time() - StartMonotonicTime. Both system_time and duration represent time as native units. To create span events, you would do something like so: In Elixir: def process_message(message) do start_metadata = %{message: message} result = :telemetry.span( start_metadata, fn -> result = # Process the message {result, %{metadata: "Information related to the processing of the message"}} end ) end In Erlang: process_message(Message) -> StartMetadata = #{message => Message}, Result = telemetry:span( StartMetadata, fun() -> Result = % Process the message {Result, #{metadata => "Information related to the processing of the message"}} end ). To then attach to the events that telemetry:span/3 emits you would do the following: In Elixir: :ok = :telemetry.attach_many( "log-response-handler", ], &LogResponseHandler.handle_event/4, nil ) In Erlang: ok = telemetry:attach_many( , ], fun log_response_handler:handle_event/4, ) With the following event handler module defined: In Elixir: defmodule LogResponseHandler do require Logger def handle_event(event, measurements, metadata, _config) do Logger.info("Event: #{inspect(event)}") Logger.info("Measurements: #{inspect(measurements)}") Logger.info("Metadata: #{inspect(metadata)}") end end In Erlang: handle_event(Event, Measurements, Metadata, _Config) -> ?LOG_INFO("Event: ~p", [Event]), ?LOG_INFO("Measurements: ~p", [Measurements]), ?LOG_INFO("Metadata: ~p", [Metadata]). See the documentation for more details.


7 hours

$1,990

Explore DECAF

About

DECAF (Dynamic Executable Code Analysis Framework) is a binary analysis platform based on QEMU.

DECAF++

DECAF++, the new version of DECAF, taint analysis is around 2X faster making it the fastest, to the best of our knowledge, whole-system dynamic taint analysis framework. This results in a much better usability imposing only 4% overhead (SPEC CPU2006) when no suspicious (tainted) input exists. Even under heavy taint analysis workloads, DECAF++ has a much better performance, around 25% faster on nbench, because of its elasticity. DECAF++ elasticity makes it a very suitable case for security analysis tasks that would selectively analyze the input e.g. Intrusion Detection Systems (IDS) that can filter out benign traffic. 


7 hours

$1,990

Learn Subdomain3

About

Subdomain3 is a new generation of tool , It helps penetration testers to discover more information in a shorter time than other tools.The information includes subdomains, IP, CDN, and so on. Please enjoy it.

Features

  • More quick Three patterns for speed. User can modify the configuration(lib/config.py) file to speed-up.
  • CDN support Determines whether the subdomain uses CDN storage automatically,even though the dict of CDN severs not contain the cname suffix.
  • RFC CIDR Sorting ip and report CIDR(example 1.1.1.1/24) that it not use CDN storage;
  • Multi-level subdomain support Discover more subdomains,example:admin.test.xx.com
  • Big dict support Million of subs support
  • Less resource consumption 1 CPU/1GB Memory/1Mbps bandwidth
  • More intelligent Discover the fastest nameserver;The strategy of dynamically adjusting of dict by importing subdomains from other sources;Prevent dns cache pollution;

7 hours

$1,990

Learn Mappedbus

About

Mappedbus is a Java based high throughput, low latency message bus, using a memory mapped file or shared memory as transport.

Mappedbus was inspired by Java Chronicle with the main difference that it's designed to efficiently support multiple writers enabling use cases where the order of messages produced by multiple processes are important. The throughput (on a laptop, i7-4558U @ 2.8 GHz) between a single producer writing at full speed and a single consumer is around 14 million messages per second (a small message consisting of three integer fields), and the average read/write latency is around 70 ns per message. Mappedbus does not create any objects after startup and therefore has no GC impact.

Features:

  • IPC between multiple processes by message passing
  • Support for a memory mapped file or shared memory as transport
  • Support for object or byte array (raw data) based messages

7 hours

$1,990

Discover NFHTTP

About

A cross platform C++ HTTP framework.

Raison D'être :thought_balloon:

At Spotify we have performed studies that show the efficacy of using native backed solutions for interfacing to backends, especially when it came to the battery life of certain devices. In order to carry this forward in the cross-platform C++ world, we created this library that provides a common interface to many of the system level HTTP interfaces, and predictable caching and request hooking. We found that many of the current solutions that claimed to do this lacked key supports for many kinds of platforms, and ended up being libraries that heavily favoured 1 platform and gave the other platforms a generic implementation. We also wanted to provide a caching layer that was consistent across all platforms in our layered architecture.

Architecture :triangular_ruler:

NFHTTP is designed as a common C++ interface to communicate with different systems over HTTP! The API allows you to create objects to make Requests and read Responses. To initiate, send and receive messages you create and use a Client object. This is a layered architecture where requests and responses can pass through multiple places in the stack and get decorated or have actions taken upon them. The layer design is as follows: Our support table looks like so: In addition to this, it is also possible to use curl on any of the above platforms or boost ASIO (provided by CPP REST SDK).

Dependencies :globe_with_meridians:

  • C++ REST SDK
  • curl
  • JSON for Modern C++
  • OpenSSL
  • SQLite
  • boost

7 hours

$1,990

Discover Nohm

About

Nohm

Description

Nohm is an object relational mapper (ORM) written for node.js and redis written in Typescript.

Features

Allows using the same code for client validations that is used for backend. Includes filtering which validations are shared. With this you can do things like socket connections to get live updates from stored models.
Since it uses redis PUBSUB you can scale your node app and clients can connect to separate node app instances but will still get the same live updates. nohm is written in Typescript and thus provides first-class typings for most things, including the option to type your model properties. This means if you use Typescript you don't have to remember every single property name of each model anymore, your IDE can tell you. This is a double-edged sword. Usually ORMs describe relations statically and you have to do database changes before you can add new relations.
In nohm all relations are defined and used at run-time, since there are no schemas stored in the database.


7 hours

$1,990

Learn Elix

About

Elix is a community-driven collection of high-quality web components for common user interface patterns. Most applications make use of common, general-purpose user interface patterns such lists, menus, dialogs, carousels, and so on. Such patterns can be efficiently implemented and packaged as web components. Their modular nature lets you easily incorporate web components into your web application, and their standard definition ensures good results across browsers. This arrangement permits a beneficial economy of scale, as common patterns only have to be implemented once. But that is not to say that its easy to develop general-purpose user interface patterns as solid components. To the contrary, implementing even simple patterns with a very high degree of quality can entail substantial complexity. For that reason, the Elix project believes that implementing high-quality, general-purpose components is best done as a community effort. This spreads the cost of creating the components across organizations, and ensures that the resulting components satisfy a broad range of concerns and can be used in many contexts. For full details and demos, see the Elix documentation.

Quick start

Plain HTML and JavaScript

Add the Elix package to your package.json: { "dependencies": { "elix": "" } } Then run npm install. We generally recommend locking your package.json to a fixed Elix version number (1.0.0 rather than ^1.0.0 or 1.x, for example). See more at Versioning. In markup, you can then reference the components you need:

Alternatively, in JavaScript, you can directly import components: import Carousel from "elix/define/Carousel.js"; const carousel = new Carousel(); // Add images, etc., to the carousel. const image1 = new Image(); image1.src = "image1.jpg"; carousel.append(image1); // Add the carousel to the page. document.body.append(carousel); The Elix project itself requires no build step. You are free to use your preferred tools to bundle the Elix modules for better network performance. You can try out the plain HTML and JavaScript project on your own machine.

React

See the sample React project showing how to use Elix components in a React application.

TypeScript

See the sample TypeScript project showing how to use Elix components in a TypeScript application. Elix includes TypeScript declaration files so that you can confirm that interactions with Elix components are type safe.

Core principles

Versioning

Elix is an ambitious attempt to deconstruct complex user interface elements into constituent parts at various levels of abstraction. We regularly feel like the project is breaking new ground at the frontier of front-end design and implementation. That can be exciting, but also means that promising implementation strategies will sometimes turn out to be dead ends. Moving forward will often entail breaking changes.


7 hours

$1,990

Discover Helm Operator

About

Helm Operator

The Helm Operator is a Kubernetes operator, allowing one to declaratively manage Helm chart releases. Combined with releases in a GitOps manner, but the usage of Flux is not a strict requirement. The desired state of a Helm release is described through a Kubernetes Custom Resource named HelmRelease. Based on the creation, mutation or removal of a HelmRelease resource in the cluster, Helm actions are performed by the operator.

Helm Operator features

  • Declarative install, upgrade, and delete of Helm releases
  • Pulls chart from any chart source;
    • Public or private Helm repositories over HTTP/S
    • Public or private Git repositories over HTTPS or SSH
    • Any other public or private chart source using one of the available
  • Allows Helm values to be specified;
    • In-line in the HelmRelease resource
    • In (external) sources, e.g. ConfigMap and Secret resources, or a (local) URL
  • Automated purging on release install failures
  • Automated (optional) rollback on upgrade failures
  • Automated (optional) helm test gating of installs and upgrades.
  • Automated image upgrades using Flux
  • Automated (configurable) chart dependency updates for Helm charts from Git sources on install or upgrade
  • Detection and recovery from Helm storage mutations (e.g. a manual Helm release that was made but conflicts with the declared configuration for the release)
  • Parallel and scalable processing of different HelmRelease resources

7 hours

$1,990

Discover PruneCluster

About

PruneCluster is a fast and realtime marker clustering library. Example 1: 150 000 randomly moving markers. Example 2: Realtime clusters of tweets. It's working with Leaflet as an alternative to Leaflet.markercluster.

The library is designed for large datasets or live situations. The memory consumption is kept low and the library is fast on mobile devices, thanks to a new algorithm inspired by collision detection in physical engines.

Features

Realtime

The clusters can be updated in realtime. It's perfect for live datasets or datasets you want to filter at runtime.

Fast

Number of markers|First step|Update (low zoom level)|Update (high zoom level) These values are tested with random positions, on a recent laptop, using Chrome 38. One half of markers is moving randomly and the other half is static. It is also fast enough for mobile devices. If you prefer real world data, the 50k Leaflet.markercluster example is computed in 60ms (original).

Weight

You can specify the weight of each marker. For example, you may want to add more importance to a marker representing an incident, than a marker representing a tweet.

Categories

You can specify a category for the markers. Then a small object representing the number of markers for each category is attached to the clusters. This way, you can create cluster icons adapted to their content.

Dynamic cluster size

The size of a cluster can be adjusted on the fly (Example)

Filtering

The markers can be filtered easily with no performance cost.


7 hours

$1,990

Work around with Qt Modbus TcpIp

About

A class to manage Modbus frames and another to make a Modbus server. Both classes are fully portable. An example of a modbus server is given. The code is very simple and comprehensive. Qt includes some classes related to Modbus since Qt 5.6.


7 hours

$1,990

Basics of Coronis SDK

About

Set of module for data processing of wireless device

The goal is to create a leading “Set of software libraries for data processing of wireless device based on Coronis platform“. These libraries will run on all major platforms (embedded and desktop) and provide services for data processing through generic APIs. Keywords: Coronis, Wavenis, API, Plugin, Wireless, Waveport, Wavecard, Profile, Java, C, C++, C#


7 hours

$1,990

Fundamentals of OpenCV CUDA Binaries

About

OpenCV Pre-built CUDA binaries

opencvcuda-release opencvcuda-debug 3 Builds now available as nuget packages : - Package for the default Windows x64 build available on opencv.org - Package for Windows x64 Visual Studio 2015 for the contrib and vtk modules built with AVX, SSE & OpenGL support. - - Packages for Release and Debug configurations (due to file size limitations on nuget.org) built with CUDA Toolkit 7.0.0 targeting architectures 2.0, 2.1, 3.0, 3.5, 3.7, 5.0, 5.2 suitable for Visual Studio 2013.


7 hours

$1,990


Is learning Snippets and Libraries hard?


In the field of Snippets and Libraries 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 Snippets and Libraries a good field?


For now, there are tremendous work opportunities for various IT fields. Most of the courses in Snippets and Libraries 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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