Technology Archives – IT Creative Labs

Top 16 Applications of Artificial Intelligence in 2023

May 19, 2023 by ITCL

Artificial Intelligence (AI) is truly revolutionizing the way we live and work. From healthcare to finance, agriculture to banking sector, gaming to space exploration, AI is already making an impact in numerous industries.

In this blog post we will dive into the world of AI and explore the different ways in which this technology is being used today. Whether you’re a tech enthusiast or simply curious about how AI is changing our daily life, this post will provide you with valuable insights into the exciting possibilities of artificial intelligence applications. So, join us on this journey!

Contents

AI Applications in Healthcare
AI Applications in Finance
AI Applications in Agriculture
AI Applications in Education
AI Applications in Ecommerce
AI Applications in Manufacturing
AI Applications in Cybersecurity
AI Applications in Gaming
AI Applications in Marketing
AI Applications in Military
AI Applications in Transportation
AI Applications in Entertainment
AI Applications in Civil Engineering
AI Applications in Space
AI Applications in IoT
AI Applications in the Food Industry
Conclusion
Frequently Asked Questions

Artificial Intelligence Applications in Healthcare

Artificial intelligence is rapidly gaining popularity in the healthcare industry, and for good reason. The rise of AI in healthcare applications has led to numerous benefits, including improved efficiency, enhanced accuracy and better patient outcomes.

Medical imaging. With the help of AI algorithms, medical professionals can now accurately analyze and interpret medical images, including X-rays, MRIs and CT scans, in a fraction of the time it would take a human expert. This has led to quicker and more accurate diagnoses, as well as faster treatment plans.
Drug discovery. By utilizing machine learning algorithms, researchers can now analyze large datasets and identify potential drug targets, significantly reducing the time and resources required for drug development. This has resulted in the identification of new therapies for previously untreatable diseases, giving hope to patients and medical professionals alike.
Predictive analytics. AI can be used to analyze large amounts of patient data to identify at-risk patients and predict potential health risks. This can help healthcare providers proactively intervene before a medical emergency occurs. This is especially helpful in chronic disease management, where early detection and treatment can have a significant impact on patient outcomes.

In conclusion, the applications of AI in healthcare are vast and have the potential to revolutionize the medical industry. From improved accuracy in medical imaging to more efficient drug discovery, AI is transforming the way healthcare professionals diagnose, treat and manage diseases. So, if you’re in the healthcare industry, it’s important to stay up-to-date on the latest trends in artificial intelligence applications in healthcare and explore how they can be applied to your practice.

Artificial Intelligence Applications in Finance

Artificial Intelligence has transformed the finance industry in recent years, bringing unprecedented levels of automation, efficiency and accuracy to the sector. Here are some ways AI is transforming finance.

Fraud detection is an area where AI is being widely applied. By using machine learning algorithms, financial institutions have the capability to identify fraudulent activities in real-time and take appropriate measures to prevent financial losses. This has significantly reduced the instances of financial fraud, saving billions of dollars in losses.
Customer service. AI-powered chatbots and virtual assistants are now being used by financial institutions to provide personalized and efficient customer service, including answering queries and assisting customers with their needs.
Risk management, where machine learning algorithms are being used to analyze large datasets and identify potential risks. This has helped banks make better-informed decisions, resulting in better risk management and more accurate predictions.
Loan underwriting. AI can assess the creditworthiness of loan applicants by analyzing their financial data and credit history.
Financial planning. AI-powered tools can help individuals and businesses make smarter financial decisions by analyzing their income, expenses and investment portfolios.

All in all, the extensive range of artificial intelligence applications in financial services has the potential to revolutionize the finance sector, benefiting both companies and customers. From fraud detection to customer service and risk management, AI is transforming the way financial institutions operate.

Artificial Intelligence Applications in Agriculture

AI is helping improve agricultural practices in various ways:

Precision farming involves the use of AI-powered sensors to gather data about soil conditions, weather patterns and other environmental factors to help farmers make informed decisions about planting, irrigation and fertilization.
Crop monitoring involves the use of drones equipped with AI-powered cameras to monitor crop health and detect signs of disease or pests. This information is used to create a map of the field that helps farmers identify areas that require attention, saving time and resources.
Yield prediction, where machine learning algorithms analyze data on crop yields from previous years, along with environmental and weather data, to make predictions about future yields. This enables farmers to make well-informed choices regarding crop planning, storage and transportation, leading to increased productivity and efficiency in agriculture.

To sum up, AI is transforming the agricultural sector by automating many of the repetitive and time-consuming tasks associated with farming, including planting, crop monitoring and harvesting. The applications of AI in agriculture bring numerous benefits to farmers: increased productivity, reduced environmental impact and better business decision-making.

Application of Artificial Intelligence in Education

AI transforms different fields, and education is no exception. By analyzing massive amounts of data and detecting patterns, AI is revolutionizing the traditional teaching and learning methods adopted by educators.

AI has numerous applications in the education system, from automating administrative tasks to personalizing learning experiences for students. One example is the use of AI-powered chatbots, which can provide instant support to students and reduce the workload of teachers. AI can also analyze data from student assessments to identify knowledge gaps and provide targeted interventions.

Moreover, AI can enable personalized learning experiences that cater to individual student needs and abilities. By analyzing data on each student’s learning style, AI can generate customized lesson plans and educational content. This not only improves learning outcomes but also increases student engagement and motivation.

In addition, AI can help educators predict which students are at risk of dropping out or falling behind, allowing for early intervention. This can help prevent students from slipping through the cracks and ensure that all students receive the support they need to succeed.

In short, applications of artificial intelligence in education have the potential to transform the way we teach and learn. From automating administrative tasks to providing personalized learning experiences, AI is offering educators with the tools they need to improve learning outcomes and support student success. As the technology continues to evolve, we can expect to see even more exciting developments in the future of AI in the education sector.

Application of Artificial Intelligence in Ecommerce

Artificial intelligence has the potential to transform the way ecommerce operates. By analyzing massive amounts of data, AI can provide valuable insights that businesses can use to enhance the customer experience and optimize operations. Here are some of the ways AI is transforming ecommerce:

Personalization. Artificial intelligence can analyze customer data to create personalized experiences, such as personalized product recommendations and targeted marketing campaigns.
Inventory management. AI can optimize inventory management by predicting demand patterns and automating restocking processes.
Fraud detection. AI can help prevent fraud by analyzing customer behavior and identifying unusual patterns.
Chatbots. AI-powered chatbots can provide customers with instant support and assistance, improving customer satisfaction and reducing the workload of customer service representatives.
Pricing optimization. Artificial intelligence helps analyze pricing data and adjust prices in real-time based on market demand, competition and other factors.

Overall, the application of AI in ecommerce can lead to increased efficiency, cost savings and improved customer satisfaction.

Application of Artificial Intelligence in Manufacturing

AI has numerous applications in the manufacturing industry. Here are a few examples of them:

Predictive maintenance. AI-powered systems can analyze data from sensors and other sources to predict when equipment is likely to fail, allowing manufacturers to perform maintenance and repairs before breakdowns occur.
Quality control. AI can help improve product quality by identifying defects and anomalies during the manufacturing process, allowing manufacturers to make real-time adjustments.
Supply chain optimization. Artificial Intelligence can be used to optimize the supply chain, by predicting demand and adjusting production accordingly. This can reduce waste, improve efficiency and ultimately lower costs.
Robotics. AI-powered robots can perform repetitive tasks with precision and speed, freeing up human workers to focus on more complex tasks.

AI has the potential to revolutionize the manufacturing industry by making it more efficient, cost-effective and innovative. As AI technology continues to develop, we can expect to see even more progress in the manufacturing sector.

Application of Artificial Intelligence in Cybersecurity

AI enables organizations to proactively identify and respond to cyber threats. One of the key applications of AI in cybersecurity is in the area of threat detection. AI algorithms are able to analyze vast amounts of data and identify patterns that indicate the presence of potential cyber threats, such as malware or hacking attempts.

Another important application of AI in cybersecurity is in the area of identity and access management. AI can be used to analyze user behavior and detect anomalies that may indicate unauthorized access to sensitive data or systems. This can help organizations to prevent data breaches and other security incidents before they occur.

Artificial intelligence can also be used to automate certain cybersecurity tasks, such as vulnerability scanning and patch management. By automating these tasks, organizations can ensure that their systems are up to date and protected against the latest threats.

In a word, AI provides significant benefits to the field of cybersecurity, enabling organizations to better protect their networks, systems and data from a wide range of threats.

Application of Artificial Intelligence in Gaming

AI applications in gaming have become increasingly prevalent in recent years. Game developers use AI to create more realistic game characters, improve game physics and mechanics and enhance game environments.

Artificial intelligence is also used in game testing, where it can simulate the behavior of millions of players to identify potential bugs or performance issues.

One example of AI application in gaming is the use of machine learning algorithms to create more intelligent non-player characters (NPCs). These NPCs can learn from their interactions with players and adjust their behavior accordingly, providing a more immersive gaming experience.

Another application is the use of AI to generate game content, such as levels, puzzles or quests, which can reduce development time and improve game replayability.

Application of Artificial Intelligence in Marketing

Artificial intelligence helps enhance customer experience, optimize advertising campaigns and improve business outcomes. One of the most significant applications of AI in marketing is predictive analytics, which helps businesses forecast customer behavior and identify potential opportunities.

Another application in marketing is natural language processing (NLP), which allows businesses to analyze customer sentiment and generate personalized content. AI-powered chatbots and virtual assistants are also being used to provide customer support and improve the overall user experience. In addition, machine learning algorithms are helping businesses analyze and interpret large amounts of data, providing insights that can be used to optimize marketing strategies and improve ROI.

In conclusion, the use of AI in marketing is expected to continue to grow, providing businesses with new and innovative ways to engage with their customers and drive growth.

Application of Artificial Intelligence in Military

Artificial intelligence has various applications in the military sector. One such application is the use of unmanned aerial vehicles (UAVs) or drones for surveillance and reconnaissance missions. Drones equipped with AI can detect and identify targets, monitor suspicious activities and movements and provide real-time data to military commanders, which can be used to make informed decisions.

Another application of AI in the military is the development of autonomous weapons, such as drones or robots, which can operate without human intervention. These weapons can analyze data, make decisions and act accordingly, which could potentially reduce the risk of human casualties.

Additionally, artificial intelligence can also be used to predict and prevent equipment failures by analyzing data from sensors and predicting when maintenance is required. This can help reduce downtime and increase the readiness of military equipment.

However, there are also ethical concerns regarding the use of AI in the military, particularly with the development of autonomous weapons. There are concerns that these weapons may malfunction or make incorrect decisions, leading to unintended consequences and potential harm to civilians.

Application of Artificial Intelligence in Transportation

Artificial intelligence has various applications in the transportation industry. One of the most significant is the development of autonomous vehicles, which use AI to operate without human intervention. This technology has the potential to revolutionize transportation by reducing the number of accidents caused by human error and making transportation more efficient.

AI is also used for traffic management systems, which can predict and manage traffic flow more accurately than traditional systems. This helps reduce congestion and improve safety on roads. AI-powered predictive maintenance can also improve the performance and lifespan of vehicles and transportation infrastructure by predicting potential issues before they cause significant problems.

Moreover, AI can be used for optimizing logistics and supply chain management. It can help transportation companies track and manage their inventory and fleets more efficiently, reducing costs and improving delivery times.

Application of Artificial Intelligence in Entertainment

The entertainment industry is also leveraging AI to enhance the way people consume and enjoy media.

In music, AI is being used to create personalized playlists, suggest new songs and artists and even generate music compositions.
In film and television, artificial intelligence is being used to streamline the production process by automating tasks such as script analysis, scheduling and budgeting.
AI is also being used to enhance visual effects and animation, creating more realistic and immersive experiences for viewers.
In the gaming industry, as we discussed earlier, AI is being used to create more intelligent and challenging opponents for players, as well as to improve graphics and sound. AI algorithms can also be used to personalize the gaming experience for individual players by analyzing their behavior and preferences.

Overall, the application of AI in the entertainment industry is still in its early stages, but it has the potential to significantly transform the way content is produced and consumed. With continued development and innovation, AI could play an increasingly important role in shaping the future of entertainment.

Application of Artificial Intelligence in Civil Engineering

From designing and planning to construction and maintenance, AI is revolutionizing the way civil engineers approach their work.

One key application of AI in civil engineering is in the design phase. AI algorithms can analyze large amounts of data and generate optimized designs for various civil engineering projects, such as buildings, bridges, roads. This can help engineers create more efficient and sustainable structures that are safe and cost-effective.

AI can also assist in the construction phase by providing real-time monitoring and quality control. AI-powered sensors and drones can monitor construction sites, track progress, and detect any potential issues or hazards. This can help ensure that projects are completed on time and within budget, while also improving safety and reducing the risk of accidents.

Another important application of artificial intelligence in civil engineering is in the field of maintenance and repair. AI algorithms can analyze data from sensors and other sources to identify potential issues and predict when maintenance or repairs may be necessary. This can help reduce downtime, improve safety and extend the lifespan of structures and infrastructure.

The use of artificial intelligence in civil engineering has the potential to increase efficiency, improve safety and reduce costs. As AI technology continues to evolve, it is likely that its impact on civil engineering will only continue to grow, leading to more advanced and sustainable infrastructure and construction projects.

Application of Artificial Intelligence in Space

With the vast amount of data generated by space missions, AI algorithms have become essential in analyzing and interpreting this data. Below are some of the applications of AI in space:

Autonomous spacecraft. AI is used to control spacecraft and make them autonomous. The spacecraft can learn from their surroundings and make decisions on their own without human intervention.
Image processing. AI algorithms are used to process and analyze the images captured by spacecraft cameras. This enables scientists to identify objects, terrain features, and anomalies that would have been difficult to detect using manual methods.
Predictive maintenance. AI is used to predict the failure of spacecraft components, allowing for maintenance and repairs before a critical failure occurs.
Mission planning. AI is used to optimize mission planning, ensuring that spacecraft are sent on the most efficient and cost-effective trajectories.
Mars exploration. AI is being used to help plan and execute missions to Mars. The Mars Exploration Rover mission is an example of a mission that has benefited from AI.

Lastly, the application of AI in space has improved the accuracy, efficiency and effectiveness of space exploration. As the field continues to evolve, it is likely that AI will become an even more critical component of space exploration and discovery.

Application of Artificial Intelligence in IoT

The world of the Internet of Things (IoT) has been greatly impacted by the application of artificial intelligence. AI enables smart devices and systems to learn from the data they collect, making them more efficient and effective in performing their tasks. AI can be used in IoT applications to process large amounts of data generated by various devices and sensors, identify patterns and make predictions.

One of the primary applications of AI in IoT is predictive maintenance. By analyzing data from various sensors and devices, AI can predict when a machine is likely to fail and schedule maintenance accordingly. This approach reduces downtime, saves costs and increases the lifespan of the machine.

Artificial intelligence can also be used in IoT to optimize energy consumption. Based on data and analysis from various sensors and devices, AI can identify patterns of energy usage and provide insights into how to reduce energy consumption.

Another application is in the field of smart homes. With the help of AI, smart homes can learn the behavior of the occupants and adjust settings accordingly. For example, smart thermostats can learn when the occupants are likely to be at home and adjust the temperature in time.

In summary, the application of AI in IoT has numerous benefits, including predictive maintenance, energy optimization and home automation. AI enables IoT systems to process large amounts of data, identify patterns and make predictions that can lead to increased efficiency, reduced costs and improved user experience.

Application of Artificial Intelligence in the Food Industry

AI has brought significant transformations to the food industry, revolutionizing various aspects of food production, processing, and consumption. Here are 6 examples of how AI is making an impact:

Food safety and quality. Artificial intelligence can detect contaminants, such as bacteria and foreign objects, in food products. This helps in ensuring food safety and quality control throughout the supply chain.
Allergy awareness. AI algorithms accurately detect potential allergens and cross-contamination risks. They also aid in minimizing the risk of cross-contamination during food processing, ensuring accurate allergen labeling, providing personalized recommendations, and analyzing data to identify emerging allergenic risks.
Supply chain optimization. AI algorithms optimize inventory management, demand forecasting, and logistics. This reduces waste, minimizes costs, and ensures timely delivery of fresh products.
Food creation and recipe development. AI can generate new recipes by analyzing large databases of ingredients and flavor profiles. It can also create innovative food products by simulating taste and texture using machine learning techniques.
Menu optimization. Artificial intelligence can analyze customer feedback, social media trends, and sales data to identify popular dishes, ingredients, and flavor combinations. This helps restaurants create and refine menus that align with customer preferences and market trends, increasing customer satisfaction and profitability.
Personalized nutrition. AI-based platforms analyze individuals’ dietary preferences, health data, and genetic information to provide personalized nutrition recommendations. This helps individuals make informed choices based on their specific needs.

When it comes to food preferences, having a mobile app that can cater to individual needs and provide personalized recommendations is invaluable. That’s where UMA comes in. UMA (Universal Meal Assistant) is a digital menu app that enables users to track food intolerances and check the nutritional value of food items.

UMA understands that each person has unique dietary requirements, whether it’s due to allergies, intolerances, or specific nutritional goals. With UMA, users can effortlessly find allergens in dishes, track food intolerances, and check the nutritional value of their meals.

Gone are the days of struggling to decipher unfamiliar ingredients or worrying about accidentally consuming something that may trigger an allergic reaction. UMA built-in SCAN tool allows users to effortlessly scan and translate restaurant menus with just a one click.

Whether you have food allergies, intolerances, or simply want to make healthier choices, UMA app is the universal meal assistant for you.

Download UMA (App Store, Google Play) now and unlock a world of personalized dining, where your food preferences and needs are always prioritized.

Conclusion

Artificial Intelligence is transforming and revolutionizing our daily lives in many ways. It is being used across various industries, including healthcare, finance, education, manufacturing, transportation and more. AI can help us make more informed and smarter decisions, improve efficiency and enhance overall user experience.

While AI offers many benefits, it is important to recognize that it is not a replacement for human intelligence. AI systems still require human oversight and their outputs should be carefully analyzed and evaluated. Additionally, there are concerns about privacy, security and the potential for AI to exacerbate existing societal inequalities.

In general, AI has the potential to greatly improve our real life, but it is important to approach its implementation with caution and ensure that it is used ethically and responsibly.

Frequently Asked Questions

AR/VR Trends for Better Clothes Fitting in Fashion Industry and Beyond

Jan 31, 2023 by ITCL

The fashion industry is constantly evolving, and with the advent of AR/VR technology, the way we try on and purchase clothes is changing. Here are some of the current AR/VR trends in the fashion industry that we have worked on for our clients thus far:

Virtual Try-Ons: AR and VR technologies are being used to create virtual fitting rooms where customers can try on clothes without physically being in the store. Brands are using this technology to improve the customer experience and reduce returns.
Customized Fit: With AR/VR, customers can see how clothes fit on a virtual model that is customized to their body shape and size. This allows customers to get a better idea of how a garment will fit, reducing the likelihood of returns.
Interactive Showrooms: AR and VR are being used to create interactive showrooms where customers can see how clothes look in different environments and lighting conditions. This allows customers to make more informed purchasing decisions.
Personalized Style Recommendations: AR/VR technology is being used to recommend clothes based on customers’ personal style and preferences. This helps customers find clothes that fit their body and style, and increases customer satisfaction.
Sustainable Fashion: AR/VR is also being used to promote sustainable fashion. For example, virtual try-ons can be used to showcase the environmental impact of certain fabrics and manufacturing processes, helping customers make more sustainable choices.

In conclusion, AR/VR is changing the way we try on and purchase clothes, making the experience more personalized, sustainable, and enjoyable. These were just some of the use cases we have worked on, as you can imagine, there are many more actual use cases. As technology continues to evolve, we can expect to see even more innovative uses of AR/VR in the fashion industry.

Are you in the fashion or beauty industry and looking to stand out? Are you in a different industry but looking into AR/VR applications to solve your business problem? Reach out and let’s discuss what technology solution would be the best fit for you!

Why Flutter is a Good Choice for Your Next Mobile Application Development Project

Nov 11, 2022 by ITCL

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase.
There are a few differentiating advantages of Flutter app development. Let’s look at some of the major ones.

We have asked our flutter enthusiasts to provide their feedback on why they love flutter so much and why they’d recommend this framework above any other for your next software development project, especially when it comes to mobile application development. Here is what they said:

Cross-platform development

Flutter is cross-platform development, in which developers can build attractive apps in iOS and Android platforms contemporaneously.

As a result, Flutter programming is a great choice from cost-savings perspective, as you don’t have to hire two development teams to build a separate application for each of the mobile platforms.

Faster code writing

Flutter code compiles to ARM or Intel machine code as well as JavaScript, for fast performance on any device.

Hot reload

Typically, iOS and Android developers need to write code, then wait for it to compile, and be loaded on the device before seeing changes. But, with Flutter’s hot reload, they can check the effects immediately or without delay.

Reach users on every screen

Deploy to multiple devices from a single codebase: mobile, web, desktop, and embedded devices.

Native performance

Flutter compiles down to native machine code for fast apps and beautiful animations.

Strong Community

Flutter is supported and used by Google, trusted by well-known brands around the world, and maintained by a community of global developers.

Some of them: Abbey Road, Alibaba, BMW, Byte Dance, Capital One, Dream11, Ebay, Emaar, Grab, Google, Groupon, Toyota, MGM, Nubank, NY Times, Philips Hue, Sonos, Square, Tencent.

Flutter continues to grow, both in usage and in the size of the ecosystem. Every day, more than 1,000 new mobile apps using Flutter are published to the Apple and Google Play stores, along with more usage on web and desktop. And the Flutter package ecosystem now comprises over 25,000 packages, providing further evidence of maturity and breadth.

Always open source

Trusted by a global community of contributors and supported by Google, Flutter is open, transparent, and reliable.

Interested to work with Flutter or apply it to your business idea?

We’ve got you covered! Tell us about the project you have in mind and we will help you turn your idea into reality!

What is MVVM?

Sep 19, 2022 by ITCL

Model–view–viewmodel (MVVM) is a software architectural pattern that facilitates the separation of the development of the graphical user interface (the view) – be it via a markup language or GUI code – from the development of the business logic or back-end logic (the model) so that the view is not dependent on any specific model platform.

The viewmodel of MVVM is a value converter, meaning the viewmodel is responsible for exposing (converting) the data objects from the model in such a way that objects are easily managed and presented. In this respect, the viewmodel is more model than view, and handles most if not all of the view’s display logic. The viewmodel may implement a mediator pattern, organizing access to the back-end logic around the set of use cases supported by the view.

MVVM was invented by Microsoft architects Ken Cooper and Ted Peters specifically to simplify event-driven programming of user interfaces. The pattern was incorporated into Windows Presentation Foundation (WPF) (Microsoft’s .NET graphics system) and Silverlight (WPF’s Internet application derivative).

Like many other design patterns, MVVM helps organize code and break programs into modules to make development, updating and reuse of code simpler and faster. The pattern is often used in Windows and web graphics presentation software.

The separation of the code in MVVM is divided into View, ViewModel and Model:

View is the collection of visible elements, which also receives user input. This includes user interfaces (UI), animations and text. The content of View is not interacted with directly to change what is presented.
ViewModel is located between the View and Model layers. This is where the controls for interacting with View are housed, while binding is used to connect the UI elements in View to the controls in ViewModel.
Model houses the logic for the program, which is retrieved by the ViewModel upon its own receipt of input from the user through View.

MVVM is a powerful architectural pattern that has gained immense popularity in recent years due to its numerous advantages. However, as with any design pattern, it also has its drawbacks. To make an informed decision on whether to use MVVM for your application, it’s important to understand its key features, as well as its advantages and disadvantages. So, let’s explore them in more detail.

Features

MVVM separates the different concerns of an application, making it easier to maintain and scale. Let’s take a closer look at the key features of MVVM and how they can be improved:

Life Cycle State

One of the key benefits of MVVM is that it helps maintain the life cycle state of an application. The ViewModel can store and manage the application state, allowing the application to resume where the user left off. To improve this feature, we can use the Android Architecture Components like ViewModel and LiveData to persist data and manage the application state effectively.
UI and Business Logic Separation

MVVM keeps UI components away from the business logic, making the code more modular and maintainable. To further improve this feature, we can use Data Binding to simplify the code and reduce boilerplate. By using Data Binding, we can bind UI components directly to ViewModel properties, reducing the amount of code required to update the UI.
Business Logic and Database Operations

MVVM keeps the business logic separate from the database operations. This separation of concerns makes the code more testable and maintainable. To improve this feature, we can use the Repository pattern to further decouple the ViewModel from the database. The Repository acts as a mediator between the ViewModel and the database, providing a simple and consistent interface to perform database operations.
Easy to Understand and Read

MVVM is designed to be easy to understand and read. The ViewModel acts as a mediator between the View and the Model, making it easier to reason about the code. To further improve this feature, we can use the SOLID principles to keep the code clean and maintainable. By following SOLID principles like Single Responsibility and Dependency Inversion, we can create code that is easy to understand and maintain.

Now that we have explored the key features of MVVM and how they can be improved, let’s take a closer look at the advantages and disadvantages of this architectural pattern.

Advantages

Maintainability

The Model-View-ViewModel (MVVM) architecture pattern has become a popular choice for building software applications, and for good reasons. One of the key advantages of MVVM is its maintainability, which allows developers to remain agile and continuously release successive versions quickly. This is due to the clear separation of concerns within the architecture, making it easier to modify and update the codebase without affecting other parts of the application.
Extensibility

Another benefit of MVVM is its extensibility. The architecture enables developers to add new pieces of code or replace existing ones without requiring significant modifications to the overall system. This makes it easier to scale and evolve the application over time, adapting to new requirements and changes in the market.
Testability

Moreover, MVVM promotes testability by separating the business logic from the view layer, making it easier to write unit tests against the core logic. This not only improves the overall quality of the codebase but also reduces the likelihood of introducing new bugs during the development process.
Transparent Communication

Finally, the transparent communication between the layers of an application is another advantage of MVVM. The view model provides a clear and concise interface to the view controller, which populates the view layer and interacts with the model layer. This results in a transparent and seamless communication between the different layers of the application, making it easier to understand and maintain the codebase.

In conclusion, the advantages of MVVM make it a great choice for developers who want to build scalable, maintainable and extensible software applications. Its clear separation of concerns, testability and transparent communication between layers, make it a powerful tool for building high-quality software applications that can adapt to changes in the market and evolving business requirements.

Disadvantages

Like any software architecture pattern, MVVM also has some disadvantages that developers should consider before adopting it. Here are a few of them:

Learning curve: MVVM can have a steep learning curve for developers who are new to the pattern, which can lead to longer development times and potential mistakes during implementation.
Increased complexity: While MVVM promotes separation of concerns, it can also increase the complexity of the application due to the added layers of abstraction. This can make it harder to debug and maintain the codebase.
Overkill for simple UIs: For simple UIs, MVVM can be considered overkill, and using a simpler pattern or approach may be more appropriate.
Designing the ViewModel: In larger applications, designing the ViewModel layer can be challenging, as it needs to handle multiple use cases and be flexible enough to accommodate changes in the future.
Debugging complex data bindings: MVVM relies heavily on data binding, which can make debugging more difficult, especially when dealing with complex data bindings.

Despite these disadvantages, MVVM remains a popular and powerful architecture pattern for building software applications. Developers should weigh the pros and cons carefully and choose the architecture pattern that best fits their specific use case and project requirements.

What is CICD and why is it so Popular?

Aug 26, 2022 by ITCL

What is CI/CD?

CI/CD is a set of practices that automate the building, testing, and deployment stages of software development. Automation reduces delivery timelines and increases reliability across the development life cycle.

Most modern applications require developing code using a variety of platforms and tools, so teams need a consistent mechanism to integrate and validate changes. Continuous integration establishes an automated way to build, package, and test their applications. Having a consistent integration process encourages developers to commit code changes more frequently, focus on meeting business requirements, code quality, and security, which leads to better collaboration and code quality.

Continuous integration and continuous delivery are two distinct processes in CI/CD and have different purposes:

CI runs automated build-and-test steps to ensure that code changes reliably merge into the central repository.
CD provides a quick and seamless method of delivering the code to end-users.

So the main goal of CI/CD is to help developers ship software with speed and efficiency. The team continuously delivers code into production, running an ongoing flow of new features and bug fixes.

The most popular CI/CD tools

A CI/CD tool helps DevOps teams create a pipeline and automate integration, deployment, and testing stages. Some tools specifically handle the integration (CI) side, some manage development and deployment (CD), while others specialize in continuous testing or related functions.

Here is a list of the most popular CI/CD tools you can choose from:

Jenkins: An automation server that can handle anything from simple CI to a complex CI/CD pipeline.
TeamCity: A CI server that helps build and deploy projects with reusable settings and configurations.
Spinnaker: An open-source CD platform ideal for multi-cloud environments.
GoCD: A CI/CD server that emphasizes modeling and visualization.
CircleCI: A flexible, cloud-based CI/CD tool perfect for smaller projects.
Travis CI: A Ruby-based tool with a robust build matrix.
Bamboo: A CI server with support for several top stacks (Docker, AWS, Amazon S3, Git, CodeDeploy, Mercurial) and up to a hundred remote build agents.

CI/CD enables more frequent code deployment.

So, let’s sum up

CI packages, tests builds, and notifies developers if something goes wrong. The CD automatically deploys applications and performs additional tests.

CI/CD pipelines are designed for organizations that need to make frequent changes to applications with a reliable delivery process. In addition to build standardization, test development, and deployment automation, we get a holistic production process for deploying code changes. The introduction of CI/CD allows developers to focus on improving applications and not spend effort on deploying it.

CI/CD is one of the DevOps practices, as it aims to combat the tension between developers who want to make frequent changes and operations that require stability. With automation, developers can make changes more frequently, and operations teams, in turn, gain greater stability because environment configuration is standardized and continuous testing is carried out during delivery. Also, the setting of environment variables is separated from the application and there are automated rollback procedures.

However, CI/CD is just one of the processes that can contribute to improvements. There are other conditions for increasing the frequency of delivery.

To get started with CI/CD, the development and operations teams need to decide on technologies, practices, and priorities. Teams need to build consensus on the right approaches for their business and technology so that once CI/CD is implemented, the team consistently adheres to the chosen practices.

Everything You Need to Know About Docker & Docker Compose to Get Started

Aug 22, 2022 by ITCL

What is Docker Compose?

Docker is known for its use of OS-level virtualization and for the container system that employs to make creating, deploying and running applications much easier for developers.

While learning the basics of Docker, you may have come across the creation of simple applications that work autonomously, not depending, for example, on external data sources or on certain services. In practice, such applications are rare. Real projects usually involve a whole set of collaborative applications.

Docker Compose technology, if we describe it in a simplified way, allows, with the help of one command, to start many services.

So, Docker Compose is software tool used for defining and running multi-container Docker applications.

Difference Between Docker and Docker Compose

Docker is used to manage the individual containers (services) that make up an application.

Docker Compose is used to manage multiple containers that are part of an application at the same time. This tool offers the same features as Docker, but allows you to work with more complex applications.

Docker Compose Use Cases

Automated testing environments.

An important part of any Deployment or Integration process is the automated test suite.

Compose supports automated testing, which is an essential part of CI/CD and provides a convenient and easy way to create and destroy isolated testing environments for your testing. Developers can define and configure the environment needed for running automated end-to-end testing in just a few commands using the appropriate Docker Compose file.

Single host deployments.

In Docker Compose, containers are designed to run on a single host as they have traditionally been focused on development and testing workflows.

Development Environments.

Compose is a fast and simple way of starting projects as it can quickly spin up new isolated development environments. The software documents and configures all the application’s service dependencies (including databases, caches, web service APIs, etc.). It allows you to create and start one or multiple containers for each dependency using a single command.

Release notes.

You can see a detailed list of changes for past and current releases of Docker Compose, refer to the Changelog.

What features make Docker Compose so effective?

Multiple isolated environments on a single host
Preserve volume data when containers are created
Only recreate containers that have changed
Variables and moving a composition between environments

We have covered the basics of working with Docker Compose technology, the knowledge of which will allow you to use this technology and, if you desire, begin to study it in more depth.

Do you use Docker Compose in your projects?

Overwhelmed by this content? Reach out with your next big idea and we’ll take care of all the technical details so you can focus on the bigger picture.

34 Web3 Terms You Should Know

Jul 6, 2022 by ITCL

Level up your Web3 vocab with these keywords by IT creative Labs.

Our vocabulary is designed to help you navigate web3’s foundational concepts.

First of all,

Web 3.0 or Web3

The next generation of internet in which the web is a decentralized online ecosystem, built on the blockchain.

Airdrop

An airdrop is an unsolicited distribution of a cryptocurrency token or coin, usually for free, to numerous wallet addresses.

Altcoin

Altcoin simply means each and every cryptocurrency other than bitcoin. Altcoin comes from an “alternative coin”, referring to any new cryptocurrency with a relatively small market cap.

BTC (Bitcoin)

The very first decentralized digital currency that can be transferred on the peer-to-peer bitcoin network.

Block

A batch of transactions written to the blockchain. Every block contains information about the previous block, thus, chaining them together.

Blockchain

Blockchain is a publicly-accessible digital ledger used to store and transfer information without the need for a central authority. Blockchains are the core technology on which cryptocurrency protocols are built.

Bridge

A protocol allows separate blockchains to interact with one another, enabling the transfer of data, tokens, and other information between systems.

Cold Wallet

A physical device use to store cryptocurrencies. Cold wallets can be hardware devices or simply sheets of paper containing a user’s private keys. Because cold wallets are not connected to the internet, meaning they’re offline, they are generally a safer method of storing cryptocurrencies.

Consensus

The state of agreement amongst the nodes on a blockchain. Reaching consensus is necessary for new transactions to be verified and new blocks to be added to the blockchain.

Cryptocurrency

Cryptocurrency is the native asset of a Blockchain like Bitcoin or Ethereum. All coins are basically a token, also known as protocol tokens.

Dapp (Decentralized Application)

An application built on open-source code that lives on the blockchain. Dapps exist independent of centralized groups or figures and often incentivize users to maintain them through rewarded tokens

DeFi (Decentralized finance)

Decentralized finance (DeFi) is an emerging financial technology based on blockchain. The system removes the control banks and institutions have on financial services, assets and money.

DEX (Decentralized Exchange)

DEX is a peer-to-peer cryptocurrency exchange built on the blockchain. A DEX is run by its users and smart contracts instead of an intermediary figure or centralized institution.

ETH/Ether

ETH/Ether is the native cryptocurrency of the platform ethereum. Ethereum is a a decentralized ledger technology (Blockchain). Second to Bitcoin, Ether is the next most popular cryptocurrency.

Fiat

A currency established as legal tender, often backed and regulated by the government.

Floor

The current lowest price available to acquire an NFT in a collection.

Fork

A change to a blockchain protocol. When the changes are more fundamental, it may be result in a hard fork, leading to the formation of a separate chain with different rules. When the changes are minor, the results are in a soft fork.

Fractionalization

The process of locking an NFT into a smart contract, and then dividing it into smaller parts which are issued as fungible tokens. It lowers the price of ownership and allows artwork and other digital assets be owned by a community.

Gas

Gas refers to the fee, required to successfully conduct a transaction or execute a contract on the Ethereum blockchain.

Hashing

The process of taking data and creating a completely unique hash value. This hash value now acts as an identifier you can reference to retrieve the original data. This means that no matter how complex or large the data was, you can now easily identify this information by referencing its hash value.

Hot Wallet

A cryptocurrency wallet that is always connected to the internet and cryptocurrency network. Used to send and receive cryptocurrency, allow you to view how many tokens you have available to use.

Liquidity

A measure of how easily an asset can be bought, sold, or traded in a given market or on an exchange.

Mining

This is the process of verifying transactions, organizing them into blocks, and then adding blocks to the blockchain. It’s like bitcoin mining will add fresh new coins and give them to the node that was mining that block.

Minting

The process of adding a transaction or block to a blockchain. The term is commonly used to express someone putting up an NFT on an exchange.

NFT

NFT stands for non-fungible token. NFTs represent a digital asset on the blockchain which are unique and represents ownership by someone.

Non-fungible

Means that it is completely unique.

Peer to peer (P2P)

Something where two decentralized individuals interact directly with each other, without intermediation by a third party.

POAP

Stands for ‘proof of attendance protocol’. This is an NFT that is used to signify an event or certain moment in time.

PoS (Proof of Stake)

A consensus mechanism that requires nodes, called validators, to stake a set amount of cryptocurrency on the blockchain in order to verify transactions and mint blocks.

PoW (Proof of Work)

A consensus mechanism that requires miners to complete complex mathematical puzzles in order to verify transactions and mint blocks. When a miner correctly solves a puzzle, they gain access to mint the next block and receive the corresponding block reward and transaction fees.

Smart contract

Self-executing code deployed on a blockchain that allows transactions be made without an intermediary figure and without the parties involved having to trust one another.

Stablecoin

Cryptocurrencies where the price is designed to be pegged to a cryptocurrency, fiat money, or exchange-traded commodities. It stays stable.

Token

Means that it can be transferred on a blockchain. Created by platforms and applications that are built on an existing blockchain.

Wallet Address

Similar to a bank account number. Your wallet address is a unique string of numbers and letters (also called a public key) that people can use to send you cryptocurrency. But only you can access your wallet’s contents by using the corresponding private key.

Web3 is growing rapidly. So key WEB3 terminology can give you the opportunity to understand better the conversations around the evolution of the Internet and stay on top of the game.

Connect to WooCommerce Rest API using Javascript

Jul 21, 2019 by ITCL

Do you know, how to get data from WooCommerce via Rest API from the client, using JavaScript? Surprisingly, tutorials on this delicate subject are seldom found on page one of search results. Moreover, as of the moment of this writing WooCommerce documentation is also missing this information.

Warning: It is never a good idea to expose API authentication credentials.

Nevertheless, in rare cases when you do end up using this frivolous setup in production, make sure that your API key is for read only.

Below are a couple working code snippets – one using jQuery and one using vanilla async/await. Both get the job done:

const wooClientKey = 'ck_00000000000000000000000000';
const wooClientSecret = 'cs_00000000000000000000000000';
const wooUrl = 'https://yoursite.com/wp-json/wc/v3/products';

function basicAuth(key, secret) {
    let hash = btoa(key + ':' + secret);
    return "Basic " + hash;
}

let auth = basicAuth(wooClientKey, wooClientSecret);

function getData(url) {
    jQuery.ajax({
        url: url,
        method: 'GET',
        beforeSend: function (req) {
            req.setRequestHeader('Authorization', auth);
        }
    })
        .done(function (data) {
            console.log(data);
            return data;
        });
}

getData(wooUrl);

While the code itself is fairly self explanatory, here’s a quick step by step breakdown:

Const

The “consts” are merely script settings, which one can generate from within their WooCommerce API section (WP Admin area).

basicAuth

This is a helper function, introduced for clarity.

The “Basic” HTTP authentication scheme transmits access credentials – username and password, encoded using base64 (defined in RFC 7617).

Thus, all basicAuth does is uses btoa() method to create a base64 encoded ASCII string (<key>: <secret>) and return the resulting hash along with the “Basic” identifier.

Vanilla Javascript

const wooClientKey = 'ck_00000000000000000000000000';
const wooClientSecret = 'cs_00000000000000000000000000';
const wooUrl = 'https://yoursite.com/wp-json/wc/v3/products';

function basicAuth(key, secret) {
    let hash = btoa(key + ':' + secret);
    return "Basic " + hash;
}

let auth = basicAuth(wooClientKey, wooClientSecret);

async function getProducts() {
    try {
        const response = await fetch(wooUrl + 'products', {
            headers: {"Authorization": basicAuth(wooClientKey, wooClientSecret)}
        });
        return await response.json();
    }
    catch (error) {
        // catches errors both in fetch and response.json
        console.log(error);
    }
}

Deploy Using Git Pull

Jan 28, 2019 by ITCL

Automatically pull files from bitbucket git repo upon commit/merge to master.

Whenever there’s a need to deploy by means of an automated git pull, without the whole container, docker registry, aws overhead, the code below works best.

exec(“git pull https://username:password@bitbucket.org/company/repo.git master”);

Here are the step-by step instructions

Create a user on bitbucket with access to the desired repository.
SSH to your remote server, where the code should be pulled to.
Clone the repository into the desired directory: git clone https://username:password@bitbucket.org/company/repo.git
Create a script (python, php or shell) – doesn’t matter. The “exec” command above is for php. Script should be accessible from the web.
Add a webhook to the repository on bitbucket, where the triggered url is the url to the php script.

All done! Now every time there’s a change to master, the code will be pulled by the remote server.

Why use username and password and not SSH keys?

SSH keys won’t work for a number of reasons, but anyone is free to give it a shot and let us know how it went. Note, the script is executed by the webserver, which runs under it’s own user that naturally has no access to the private key. Trying to make the keys available for that user is not worth the headache.

Bitbucket’s own documentation has an FTP example, why not use that?

The whole FTP business is not secure and not that widely used. If one can avoid spinning up an FTP server, one probably should.

Below are a few search queries suggested by google, left here to help those seeking the solution to their problem 🙂

How to sync with git repository

Bitbucket pull via ftp sftp

Deploy using ftp sftp

Automate git pull

Simplest continuous integration

muCommander for Mac OS X

Jun 24, 2016 by ITCL

Mac OS has long lacked a robust file manager and unfortunately continues to do so, sigh. Nonetheless there exists muCommander, which fills the void. A cross-platform file manager written in Java is a great tool. Free download of muCommander v. 0.9.1 (year 2016) is available from our site.

Note: You need to have java installed in order for muCommander to work. In case you don’t have it already, we recommend using homebrew or the official java installer for Mac OS. Tips and instructions are available via Google search or this link.

Unfortunately the official muCommander site hasn’t been updated for years and while anyone can download and compile the app from source files, available on github, it may not be as easy and straightforward even for tech savy users. Since our team uses muCommander for daily operations and a member of our team occasionally contributes to the code he decided to compile a stable and working solution for all of us to use. We decided to share it with everybody else. Enjoy!