The process of web designing encompasses several disciplines and may pose challenges to those who pursue it. Let’s delve into some of those issues. 

Key Web Design Challenges 

• Integration – Seamless synchronization with third-party applications is difficult to achieve.
• Responsiveness – Web designers find it challenging to ensure content can be accessed across multiple screens and platforms.
• User Experience – Maintaining consistently good user experience throughout the website is challenging.
• Accessibility – Access to the website may not be convenient for all levels of society, including the elderly or less privileged. It is tough to ensure compliance.
• Security – Protecting websites from viruses, malware or hacker attacks is tough.
• Retention – Keeping visitors engaged and returning to the website is difficult.

Overcoming these challenges requires using the latest technology. Incorporating technology helps in providing unique web designs, and web design software can make the process more straightforward and appealing for web designers and developers. 

There are many website designing tools available in the market. For small businesses with low budgets, free and open source website design software is the most suitable option. Let’s take a look at some of the most popular of these tools. 

4 Open Source Web Design Tools That Are Free to Use 

#1:  Brackets

Brackets is an open source tool intended for web designers and front-end developers who can work with it right inside the browser. Despite being lightweight, this open source design tool is quite compelling.

Brackets has an intuitive interface and allows web designers and developers to create new pages or sites effortlessly. With live preview functionality, users can connect to the web browser in real-time, making it an excellent tool for fast coding.

Key Features

• Inline Editors
• Live Preview
• Preprocessor Support
• Front-end editor
• Simple user interface
• Effortless switching between tabs or pages
• Multiple Cursors
• Open Source Text Editor

#2:  Adobe Dreamweaver (Free Trial)

Adobe Dreamweaver is a fast and responsive open source software that enables developers to create websites and web applications for any device.

This fully-featured web designing software tool is suitable for everything from single page design to dynamic page development. The software can be used to build beautiful sites that render perfectly across browsers and platforms. Setting up a website with Dreamweaver is fast and easy. 

Key Features

• Multi-device products
• Dynamic Code editor
• Regular Updates
• Quick Publishing
• Code Themes and Introspection
• Real-Time Browser Preview
• Syntax Checking
• Fast and flexible coding
• Creative cloud
• Seamless live view editing
• Multi-monitor support

#3: Mobirise 

Mobirise is free software that enables web designers and developers to easily create mobile-optimized websites, landing pages, online resumes, and portfolios, even without a web development background.

This user-friendly, drag-and-drop website builder offers the latest website blocks and techniques, along with customizable templates and extensions. Mobirise is downloadable and compatible with Win, Mac and Android.

Key Features

• Mobile-friendliness
• Latest website blocks and techniques
• Drag-and-drop website builder
• Themes and Templates
• Extensions
• Bootstrap and AMP (accelerated mobile page) pages

#4: Webydo (Free 30-Day Trial)

Webydo is a cloud-based software that is used to build attractive, responsive, interactive, and SEO-friendly websites without handling any code, making it a perfect option for businesses and design agencies. In addition, Webydo comes equipped with powerful content management features. 

Key Features

• SEO-friendly websites
• Content Management
• Client Billing
• White label branding
• Customizable Templates
• Hosting
• Web Analytics
• Social Integration
• Mobile styling
• eCommerce
• Drag and drop elements
• Responsive websites
• Parallax Scrolling Animator
• Professional design tool

Summary 

The world of web design is rife with challenges, from ensuring seamless integration and responsiveness to providing excellent user experience and robust security measures. 

To overcome these hurdles, modern technology comes to the rescue, offering a variety of tools to aid web designers. We’ve explored several such tools, including the lightweight Brackets, the powerful Adobe Dreamweaver, the mobile-friendly Mobirise, and the cloud-based Webydo.

Each of these tools offers unique features and functionalities that cater to different web design needs, making the design process more efficient and exciting. Whether you’re a novice or a seasoned designer, these tools can significantly enhance your productivity.

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Open-source libraries refer to software modules that are freely available to developers. They are created by developers for developers and can be employed in different programming languages and software projects. These libraries are generally hosted on public repositories such as GitHub or Bitbucket, where developers can download the latest versions or contribute to their development.

One of the most significant advantages of utilizing open-source libraries is the speed of development. By leveraging the work of others, developers can save time and effort, as opposed to building functionalities and modules from scratch. This allows them to focus on other crucial aspects of the project, such as user experience and interface design.

The libraries offer developers access to a wealth of knowledge and resources. Through studying the code written by other developers, they can enhance their skills and understanding of various programming languages and frameworks. Moreover, developers can actively participate in the development of open-source libraries, contributing to their functionality and making them more beneficial to the broader community.

The Role of Open Source Libraries in Modern Software Development

Open source libraries play a crucial role in modern software development. They provide developers with access to pre-built modules that can help them save time and resources, while still ensuring high-quality code. Here are some key ways in which open source libraries impact modern software development:

Accelerate Development Process

One of the primary advantages of using open-source libraries is their ability to accelerate the development process. With pre-built modules readily available, developers can avoid starting from scratch every time they need to add new functionality to their projects. This can save a significant amount of time and effort, enabling developers to focus on other crucial tasks.

Enhance Code Quality

Open-source libraries are created and maintained by experienced developers who have already invested a considerable amount of time testing and debugging their code. By leveraging open-source libraries, developers can benefit from the expertise of others and enhance the quality of their code.

Foster Collaboration

Open-source libraries are typically hosted on public repositories, making them easily accessible for anyone to download and contribute to. This fosters collaboration among developers, who can work together to improve the functionality and performance of the libraries. It also provides an opportunity for developers to learn from one another, enhancing their own skills and knowledge.

Reduce Costs

Open source libraries can significantly reduce the costs associated with software development, as they are available for free. Instead of creating modules from scratch, developers can utilize pre-built modules from open source libraries, which saves both time and resources. This not only reduces costs but also improves the speed of development, making it more efficient.

Provide Flexibility

Additionally, open source libraries provide flexibility to developers. They are available for a wide range of programming languages and software projects, allowing developers to choose the best libraries for their specific projects. This flexibility also means that developers can easily switch between different libraries if necessary.

To sum up, open source libraries are pivotal in modern software development, providing developers with pre-built modules that aid in saving time, reducing costs, and enhancing code quality. Leveraging open source libraries, developers can improve their collaboration and flexibility, leading to superior quality software that fulfills specific business requirements. Orangesoft offers remote developers who possess expertise in working with open source libraries to develop top-notch software that caters to your specific needs. Get in touch with us today to know more about our software development services.

The Pros and Cons of Outsourcing Software Development

In recent years, outsourcing software development has become a popular choice among businesses. It offers several advantages, such as access to a wider talent pool, cost savings, and the ability to focus on core business activities. However, outsourcing also comes with its own set of risks and challenges. In this article, we will explore both the benefits and drawbacks of outsourcing software development, providing you with the necessary information to make an informed decision.

Pros of Outsourcing Software Development

Access to a wider talent pool: Outsourcing software development allows businesses to access a wider talent pool that may not be available locally. This means that companies can hire developers with the specific skill sets they need to complete their projects.

Cost savings: Outsourcing software development can be cost-effective, especially for small businesses and startups that may not have the resources to hire full-time developers. Outsourcing allows companies to pay for only the services they need, without having to invest in infrastructure, equipment, and employee benefits.

Focus on core activities: Outsourcing software development allows companies to focus on their core business activities, such as marketing, sales, and customer support. This can help businesses become more efficient and competitive in their respective markets.

Cons of Outsourcing Software Development

Communication challenges: One of the biggest challenges of outsourcing software development is communication. This is especially true for businesses that outsource to offshore locations where there may be language barriers and time zone differences. Poor communication can lead to misunderstandings, delays, and errors in the development process.

Quality control issues: Outsourcing software development can also lead to quality control issues. This is because companies may not have direct control over the development process and may not be able to monitor the quality of work being done by the outsourced team. This can lead to delays, errors, and other issues that can affect the overall quality of the final product.

Security risks: Outsourcing software development can also pose security risks, especially if sensitive business information is shared with the outsourced team. Companies need to ensure that they have proper security measures in place to protect their intellectual property and confidential information.
How to Mitigate the Risks of Outsourcing Software Development
Choose the right outsourcing partner: Choosing the right outsourcing partner is crucial to the success of your project. Look for a partner that has experience in your industry and has a proven track record of delivering quality work.

Establish clear communication channels: Establishing clear communication channels with your outsourcing partner is essential to avoid misunderstandings and delays. Use video conferencing, instant messaging, and other tools to stay in touch with your outsourced team.

Set clear expectations: Setting clear expectations from the outset can help avoid quality control issues. Make sure that your outsourcing partner understands your project requirements and has the necessary skills to deliver quality work.
Protect your intellectual property: Protecting your intellectual property is crucial when outsourcing software development. Make sure that you have proper security measures in place, such as non-disclosure agreements, to protect your confidential information.

In conclusion, outsourcing software development can offer businesses a cost-effective and efficient solution to access a wider talent pool and focus on their core business activities. However, it’s important to note that outsourcing also presents its own set of challenges and risks. By understanding the pros and cons of outsourcing software development and implementing appropriate mitigation strategies, businesses can effectively leverage outsourcing to achieve their development goals.

If you are looking to hire remote developers, our team at Orangesoft offers high-quality remote development services. Our developers have experience in a wide range of programming languages, frameworks, and tools and can help you complete your software development projects on time and within budget. Contact us today to learn more about our remote development services and how we can help you.

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We need Java libraries to speed up the development process, to make writing this code more concise and understandable. Java libraries do not affect and do not impose any restrictions on the architecture of the software product. Moreover, they can be used as a set of subroutines close to their functionality. There are quite a few libraries available today. In this article we will consider which of them will be the most popular in 2023.

Selenium

One of the most popular libraries, Selenium, has been in the lead for years. The amount of web content is constantly growing, and the easiest way to automate testing is to start with a time-tested library. That’s why Selenium continues to be the very library that newcomers to automated testing can start with. What’s more, many companies use Selenium extensively to build their test automation frameworks and products.

Today’s Selenium ecosystem consists of three main parts:

• The first and most important is Selenium WebDriver, which helps create automated test suites and test suites to scale across different testing environments.
• The second part is the Selenium IDE, which allows uncomplicated automated scripts to go through various scenarios in the initial stages.
• The last is Selenium Grid, a server that allows you to manage tests in different environments from one central repository, simplifying startup and maintenance.

Selenide

Selenide is an intelligent add-on to Selenium WebDriver that takes full advantage of it and allows you to minimize the amount of code by reusing repetitive code in individual classes. Additional advantages include Ajax support for stabilizing autotests and powerful selectors which allow you to accurately find the right element even in a very complex project with a branched and dynamically changing DOM (Document Object Model) architecture. In addition, Selenide has a relatively simple configuration, making it easy and fast for beginners and professionals to write automated tests.

Selenoid

Selenoid is a server that allows you to run dozens of tests simultaneously in docker-container browsers. Selenoid has very high performance that outperforms Selenium Grid. Ease of installation and deployment is another advantage of Selenoid. It also scales and updates quickly. By running in docker containers, each browser runs clean and isolated from the main system and consumes no more than 20 megabytes of RAM (RAM) when idle.

TestNG

TestNG is a testing environment based on JUnit (Java) and NUnit (C#); it has long been popular with Java testers. It will also be in wide demand in 2023 because of its use for unit, functional, integration, and end-to-end testing. One of the main drivers for the continued use of this framework is that many projects have already been written using it. Multiple annotations and the ability to use autotest parameterization make it more flexible and easily adaptable to any tester’s needs.

Cucumber

The next framework that will also be popular in 2023 is Cucumber. Its popularity is due to the fact that tests are written in a common language understood by autotestors and people far from development and testing, such as analysts and project managers. Cucumber uses a BDD (Behavior-Driven Development) approach; it allows third-party people to create their own scripts, which increases the quality and reach of autotests.

JUnit

JUnit is designed for unit testing and will be very popular in 2023 because of the growing number of projects on microservice architecture. The TDD (Test-Driven Development) technique allows it to take the lead by reducing the risk of errors at the earliest stage, when there is no code yet. With the release of the new version of JUnit 5, which introduces parameters and asserts, creating autotests takes a whole new level, allowing you to perform tests where it wasn’t possible before.

Lombok

The Lombok library reduces the amount of code you write, improving its readability. It’s easy to add it to your project and you only need to do it once. Lombok also generates code at compile time, making autotests faster and more stable.

Most of the libraries and frameworks listed in this article will help you stay at the forefront of automated testing and make your daily life easier. They will also define the point of near-term development in the coming year 2023.

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Data Science professionals know exactly what Python libraries to use in data science, but when asked to name them or specify their function in an interview, we often get caught up or perhaps don’t remember more than 5 libraries (this happened to me: / )

Today I prepared a list of 10 Python libraries that help in Data Science, when to use them, what are their features and benefits.

In this article I’ve summarized the 10 most useful Python libraries for data scientists and engineers, based on my recent experience and research.

Pandas

Pandas is an open-source Python package that provides high-performance, easy-to-use data structures and analysis tools for labeled data in the Python programming language. Pandas stands for Python Data Analysis Library. Did anyone know about it?

When to use it? Pandas is the perfect tool for data processing. It is designed for fast and easy data processing, reading, aggregation, and visualization.

Pandas takes data in a CSV or TSV file or SQL database and creates a Python object with rows and columns called a data frame. A data frame is very similar to a table in statistical software, say Excel or SPSS.

What can you do with Pandas?

1. index, manipulate, rename, sort, merge the data frame;
2. update, add, remove columns from the data frame;
3. Restore missing files, process missing data or NAN;
4. Construct a histogram or rectangular chart.

This makes Pandas a fundamental library in learning Python for Data Science.

NumPy

NumPy is one of the most fundamental packages in Python, a versatile array processing package. It provides high-performance multidimensional array objects and tools for working with arrays. NumPy is an efficient container of universal multidimensional data.

The basic NumPy object is a homogeneous multidimensional array. It is a table of elements or numbers of the same data type, indexed by a set of natural numbers. In NumPy, the dimensions are called axes, and the number of axes is called a rank. The NumPy array class is called ndarray, aka array.

When to use it? NumPy is used to handle arrays that hold values of the same data type. NumPy facilitates mathematical operations on arrays and their vectorization. It greatly improves performance and therefore speeds up execution time.

What can I do with NumPy?

1. basic array operations: add, multiply, slice, align, reshape, index arrays;
2. Advanced array operations: stack arrays, partitioning, broadcast arrays;
3. Working with DateTime or linear algebra;
4. Basic slicing and advanced indexing in NumPy Python.

SciPy

The SciPy library is one of the key packages that make up the SciPy stack. Now there is a difference between the SciPy Stack and the SciPy library. SciPy is based on the NumPy array object and is part of a stack that includes tools like Matplotlib, Pandas and SymPy with additional tools.

The SciPy library contains modules for efficient mathematical procedures such as linear algebra, interpolation, optimization, integration and statistics. The main functionality of the SciPy library is built on NumPy and its arrays.

When to use? SciPy uses arrays as a basic data structure. It has various modules for common scientific programming tasks such as linear algebra, integration, matanalysis, ordinary differential equations, and signal processing.

What can you do with SciPy?

1. Mathematical, scientific, engineering calculations;
2. Numerical integration and optimization procedures;
3. Finding minima and maxima of functions;
4. Calculation of function integrals;
5. Support for special functions;
6. Working with genetic algorithms;
7. Solving ordinary differential equations. 4.

Matplotlib

This is by far my favorite and most basic Python library. You can create stories with data visualized with Matplotlib. Another library from the SciPy stack, Matplotlib, builds 2D shapes.

When to use. Matplotlib is a Python library that provides an API for embedding graphs into applications. It is very similar to MATLAB, which is built into the Python programming language.

What can I do with Matplotlib?

Histograms, bar charts, point charts, pie charts – Matplotlib can display a wide range of visualizations. With a little effort, with Matplotlib, you can create any visualization you want:

1. line diagrams;
2. point diagrams;
3. Diagrams with areas;
4. Bar charts and histograms;
5. Pie charts;
6. Stem-leaf charts;
7. Contour charts;
8. Vector fields;
9. Spectrograms.

Matplotlib also facilitates the use of labels, grids, legends and some other formatting objects. Basically, it’s about everything you can draw!

Seaborn

So, when you read the official documentation on Seaborn, it is defined as a data visualization library based on Matplotlib, providing a high-level interface for depicting interesting and informative statistical plots. Simply put, seaborn is an extension to Matplotlib with additional features.

So what is the difference between Matplotlib and seaborn? Matplotlib is used for basic construction of bar, pie, line, point charts, etc., while Seaborn provides many visualization templates with fewer syntax rules, and simpler ones at that.
What can I do with Seaborn?

1. define relationships between multiple variables (correlation);
2. Observe qualitative variables for aggregated statistics;
3. Analyze univariate or bivariate distributions and compare between different subsets of data;
4. Construct linear regression models for dependent variables;
5. Provide multi-level abstractions, multi-story grids.

Seaborn is a great option for R visualization libraries such as corrplot and ggplot.

Scikit Learn

Scikit Learn, introduced to the world as a Google Summer of Code project, is a robust machine learning library for Python. It includes ML algorithms such as SVM, random forests, k-means clustering, spectral clustering, mean shift, cross-validation, and many others. Even NumPy, SciPy and related scientific operations are supported by Scikit Learn, with Scikit Learn being part of the SciPy Stack.

When to use. Scikit-learn provides a number of supervised and unsupervised learning algorithms through a consistent interface in Python. Scikit learn will be your guide for making supervised learning models, such as Naive Bayes, group unlabeled data, such as KMeans.

What can you do with Scikit Learn?

1. Classification: spam detection, image recognition;
2. Clustering: drug exposure, stock price;
3. Regression: customer segmentation, clustering experiment results;
4. Dimensionality reduction: visualization, increased efficiency;
5. Model selection: improved accuracy due to parameter tuning;
6. Pre-processing: preparation of input data in the form of text for processing with machine learning algorithms.

Scikit Learn focuses on data modeling; not data manipulation. For generalization and manipulation we have NumPy and Pandas.

TensorFlow

TensorFlow is an AI library that helps developers create large-scale neural networks with many layers using data flow graphs. TensorFlow also makes it easy to build deep learning models, promotes modern ML / AI technology, and enables easy deployment of ML-based applications.

One of the most developed websites among all the libraries is TensorFlow. Giants like Google, Coca-Cola, Airbnb, Twitter, Intel, DeepMind all use TensorFlow!

When to use? TensorFlow is quite effective when it comes to classifying, perceiving, understanding, detecting, predicting and creating data.

What can you do with TensorFlow?

1. Voice/sound recognition – IoT, automotive, security, UX/UI, telecom;
2. sentiment analysis – mostly for CRM or CX;
3. Text Applications – Threat Detection, Google Translate, Gmail Smart Reply;
4. Face Recognition – Facebook’s Deep Face, Photo tagging, Smart Unlock;
5. Timeline – Amazon, Google and Netflix recommendations;
6. Video detection – motion detection, real-time threat detection in games, security, airports.

Keras

Keras is TensorFlow’s high-level API for creating and training deep neural network code. It is an open-source neural network library in Python. With Keras, statistical modeling, working with images and text is much easier with simplified coding for deep learning.

What is the difference between Keras and TensorFlow?

Keras is a neural network library written in Python, while TensorFlow is an open source library for various machine learning tasks. TensorFlow provides both high-level and low-level APIs, while Keras provides only high-level APIs. Keras is designed for Python and makes it more user-friendly, modular, and componentizable than TensorFlow.

What can you do with Keras?

1. define percentage accuracy;
2. Function to calculate losses;
3. Create custom function layers;
4. Built-in data and image processing functions;
5. Functions with repeating blocks of code: 20, 50, 100 layers deep.

Statsmodels

Statsmodels is a versatile Python package that provides simple calculations for descriptive statistics and estimation and statistical model building.

What can I do with Statsmodels?

1. linear regression;
2. Correlation;
3. the method of least squares (OLS);
4. survival analysis;
5. Generalized linear models and Bayesian model;
6. Single-factor and two-factor analysis, hypothesis testing (basically, what R can do!).

Plotly

Plotly is a typical graphics library for Python. Users can import, copy, paste, or transfer data to be analyzed and visualized. Plotly offers an isolated version of Python (where you can run Python limited in its capabilities). Now it remains to be seen what the limited version means, but I know for a fact that Plotly makes it easy!

When to use it? You can use Plotly if you want to create and display shapes, update shapes, hover over text for details. Plotly also has the additional feature of sending data to cloud servers. That’s interesting!

What can I do with Plotly?

The Plotly chart library has a wide range of charts that you can build:

1. basic charts: line, pie, point, bubble, Gantt, Sunburst, tree, sankey, area charts;
2. Statistical and Seaborn styles: errors, histograms, Facet and Trellis charts, tree charts, scree charts, trend lines;
3. scientific maps: contour, ternary plot, logarithmic plot, vector fields, Carpet plot, radar chart, Wind Rose and Polar plot heat maps;
4. Financial charts;
5. Maps;
6. Subplots;
7. Transformations;
8. Jupyter Widgets interaction.

Plotly is a typical graph library. Think about visualization and Plotly does it!

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TensorFlow

TensorFlow is a library developed by the Google Brain team with the main goal of deep learning and neural networks. It makes it easy to distribute work across multiple CPU cores or GPU cores and can even distribute work across multiple GPUs. TensorFlow uses tensors for this purpose. Tensors can be defined as a container that can store N-dimensional data along with its linear operations. Although it is production ready and supports reinforcement learning along with neural networks, it is not commercially supported, which means that any error or defect can only be fixed with community help.

Numpy

Based on the older Numeric library, Numpy is used to process multivariate data and complex mathematical functions. Numpy is a fast computational library that can handle problems and functions ranging from basic algebra to even Fourier transforms, random modeling, and shape manipulation. This library is written in C, which gives it an advantage over the standard built-in Python sequencer. Numpy arrays are better than the pandas series in terms of indexing, and Numpy works better when the number of entries is less than 50 KB. NumPy arrays are loaded into a single CPU, which can cause slower processing compared to newer alternatives such as Tensorflow, Dask or JAX, but nevertheless, learning Numpy is very easy and it is one of the most popular libraries to enter the Machine Learning World.

Natural language toolkit (NLTK)

NLTK is a widely used library for text classification and natural language processing. It performs word construction, lemmatization, tokenization and keyword searching in documents. The library can additionally be used for sentiment analysis, understanding movie reviews, food reviews, text classifier, checking and censoring vulgarized words from comments, text mining, and many other human language operations. Its broader uses include artificial intelligence-based chatbots, which require text processing to train their identification models, as well as creating sentences important to machine-human interaction in the near future.

Pandas

The library is written in the Python.web platform and is used to process numerical data and time series. It uses frames and data series to define three-dimensional and two-dimensional data, respectively. It also provides big-data indexing options for quickly searching through large data sets. It is well known for its ability to reshape data, rotate along a user-defined axis, handle missing data, merge and combine data sets, and filter data options. Pandas is very useful and very fast with large data sets. Its speed exceeds that of Numpy when the number of records exceeds 50k. It is the best library for data cleaning because it provides interactivity like Excel and speed like Numpy. It is also one of the few ML libraries that can handle DateTime without the help of any external libraries, and with minimal Python code scanning . As we all know, the most important part of data analysis and machine learning is data cleaning, processing and analysis, in which Pandas helps very effectively.

Scikit-Learn

Scikit-learn mainly focuses on various data modeling concepts such as regression, classification, clustering, model selection, etc. D. The library is written on top of Numpy, Scipy and matplotlib. It is a commercially available open source library that is also very easy to understand. It integrates easily with other machine learning libraries such as Numpy and Pandas for analysis, and Plotly for graphing data in graphical format for visualization purposes. This library helps both with and without a teacher.

Keras

Keras provides a Python interface to the Tensorflow library, specifically focused on AI neural networks. Earlier versions also included many other server-side parts, such as Theano, the Microsoft cognitive platform, and PlaidMl. Keras contains standard blocks of commonly used neural networks as well as tools to make image and text processing faster and smoother. In addition to the standard neural network blocks, it also provides recurrent neural networks.

PyTorch

Pytorch is an ML library developed by Facebook, based on the Torch Library (an open-source ML library written in the Lua programming language). The project is written in Python Web Development , C ++ and CUDA. In addition to Python, PyTorch has extensions to both C and C++ languages. It is a competitor to Tensorflow because both of these libraries use tensors, but it is easier to learn and integrates better with Python. Although it supports NLP, the focus of the library is on developing and teaching deep learning models only.

MlPack

MlPack is basically a C ++-based ML library that has bindings to other Python languages, including R, Julia, and GO programming. It is designed to support almost all well-known machine learning algorithms and models, such as GMM, K-means, least-corner regression, linear regression, etc. and is an easy-to-use library so that even a programmer who is not familiar with programming can understand and use it without any problems. It comes under a BSD license, making it available as both open source and proprietary software as needed.

OpenCV

OpenCV is an open-source platform for computer vision and image processing. This library contains over 2,500 algorithms dedicated to computer vision and machine learning. It can track human movements, detect moving objects, extract 3D models, merge images to create high-resolution images, and explore AR capabilities. It is used in various surveillance monitoring activities by many governments, especially in China and Israel. In addition, major camera manufacturers around the world are using OpenCv to make their technology convenient and user-friendly.

Matplotlib

Matplotlib is a library used in Python to graphically represent data before moving it to data processing and training for machine learning purposes. It uses Python GUI tools to create graphs and plots using object-oriented APIs. Matplotlib also provides a MATLAB-like interface so that the user can perform tasks similar to MATLAB. This library is free and open source and has many extension interfaces that extend the matplotlib API to various other libraries.

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I love developers who spend their free time creating amazing things. They share with others and don’t want anything in return. Thank you for your work. Since I’m a big fan of curating, I picked the best of my favorite open source iOS libraries. I made the list randomly, but they’re all great.

DZNEmptyDataSet

With this library, you just need to take advantage of multiple protocols, and iOS takes care of presenting collections correctly and beautifully to users.

PDTSimpleCalendar

Need a simple, beautiful and well working calendar for your app? Now you have PDTSimpleCalendar – it’s probably the best calendar for iOS. You can customize it to your liking so that it looks great and still works well.

MagicalRecord

Core Data. “It’s easy,” they said. “It’s easy,” they said. Seriously, Apple? A ton of boilerplate code added to every project isn’t that easy or simple. Not to mention adding, removing, and updating a lot of stuff, keeping context, creating Core Data. I really like Core Data, of course, but Apple really could have simplified and improved it.

MagicalRecord works as a wrapper for Core Data and hides all the extra stuff from the developer. If you’ve ever worked with active record patterns (like Ruby on Rails), this is all familiar to you. I highly recommend this library if you use Core Data in your application.

Chameleon

If you’re reading this, chances are you’re a programmer and not a designer. This is for you.

Chameleon is a color framework for iOS. It includes UIColor with beautiful, modern colors. It also gives you the ability to create color palettes from suggested colors. If you want to create a beautiful app, definitely add this library to your project.

Alamofire

Alamofire is a network of libraries written in Swift. Have you ever used AFNetworking? Alamofire is its little brother. Younger and more stylish, of course (AFNetworking is written in Objective-C).

You need to work with the network: download, upload, receive JSON? Then Alamofire is for you. The 8,000 people on GitHub can’t go wrong.

TextFieldEffects

Do you find the standard UITextField a bit boring? Neither do I. So give it up for TextFieldEffects!

You can even use IBDesignables in the storyboard! Unfortunately, this library doesn’t support CocoaPods, but it does support Carthage. You can also just download the project from GitHub and add it to your workspace.

GPUImage

Have you ever created a camera app? If not, you will definitely create one after getting to know this library.

GPUImage provides GPU camera effects (images and video) at incredible speed. There are hundreds of apps in the App Store that use this library – and one of mine does too: 8869 stars on GitHub, and that’s not the limit.

iRate

What’s the best way to get more reviews on the App Store? The easiest way to find out is to ask the user. It may be a little outdated, but if you don’t have time or don’t want to create everything from scratch, use iRate.

GameCenterManager

Game Center for iOS isn’t all that complicated, but with this library, it will be even easier and faster. The best is the enemy of the good.

I use this library in one of my games, and it’s a very good experience.

PKRevealController 2

This is a real gem, one of my favorite iOS controllers. PKRevealController is a side menu with a flipping effect (left, right or both sides) that you activate with your finger (or just by pressing a button).

I’ve tried other libraries that use the same control, but PKRevealController is the best. Very easy to install, easy to customize, and it recognizes gestures well. It could be included in the iOS SDK as a standard controller.

SlackTextViewController

Have you ever used Slack iOS apps? Probably yes, if you work for a large software company. For those people who haven’t, Slack will do just fine.

RETableViewManager

RETableViewManager will help you create and manage screen tables. There are standard items here, but you can create your own as well.

PermissionScope

A useful library for getting consent to collect user information by explaining why certain permissions are needed.

SVProgressHUD

This image loads quickly, you don’t have to wait long or refresh the page. This is what SVProgressHUD looks like in your application. If you need a custom wait indicator, this is the best you can find.

FontAwesomeKit

Font Awesome is a great library with which you can easily add fonts to your projects and use them in different ways.

SnapKit

Creating constraints in code isn’t easy, but luckily with SnapKit you can easily program your constraints.

MGSwipeTableCell

Another UI component that is often seen in many apps and that Apple should bring as a standard into the iOS SDK.

Quick

Module testing for Swift as well as Objective-C, integrated with Xcode. If you like Objective-C, I’d recommend Specta instead. But Swift Quick would probably be a better option.

IAPHelper

The in-app purchase function contains a lot of boilerplate code, which this library will help get rid of. And will create a simple wrapper for common tasks related to transferring money from an iOS user to a money account, yours or your company’s.

ReactiveCocoa

ReactiveCocoa is as good as the other projects on this list. ReactiveCocoa shows a completely different style of programming/architecture, based on signals and value streams. It’s unfathomable. To understand how it works, you’ll have to forget everything you’ve known up to this point. Not easy, but useful.

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