Robotic Process Automation (RPA) technology has become increasingly popular in recent years, thanks to its ability to automate repetitive manual tasks across a range of business functions. RPA implementation, however, can be complex and requires a well-defined project management approach to ensure success. Software automation is the new normal in today’s business environment. It reduces time consumption and increases the productivity of enterprises. However, implementing an RPA in an already set organization or even a startup is not easy. Numerous stakeholders are involved, and many aspects need to be considered before diving into it.

A successful RPA implementation undoubtedly depends on the solution. However, it also depends on how the organization manages the changes that accompany the implementation and post-implementation phases. RPA change management involves managing the future of the company with respect to these three elements: process, organization, and people. We must fundamentally be prepared to redefine things in order to ensure synergy between these three. There are strategies and approaches that can be helpful in addressing the problems that arise when managing organizational transformation with RPA. Here are some best practices for managing an RPA project to achieve optimal results:

Have a clear plan

The first step is to define the goals and objectives of the RPA project clearly. These objectives must relate to specific business goals and metrics, such as cost reduction, process efficiency, and increased productivity. The project team should also consider how RPA will impact existing processes, employees, and roles. Defining the RPA roadmap for the organization is one of the most crucial steps. The plan should set short-term and long-term goals.

Create Solution Design Document

In RPA projects, the Solution Design Document (SDD) plays a crucial role by providing a comprehensive blueprint of the automation solution’s architecture and design. This document serves as a communication bridge among team members, ensuring everyone clearly understands the project’s structure and functionality.

The SDD becomes a valuable resource during troubleshooting, helping to pinpoint issues and identify areas for enhancement. As the project evolves, the SDD simplifies maintenance tasks, guiding developers in making updates or modifications to the RPA solution efficiently. In essence, the SDD is a cornerstone for effective collaboration, problem-solving, and project continuity in RPA development.

Assembling the right team

RPA implementation requires a team with cross-functional expertise. Team members must have a deep understanding of automation technology, and business processes, and the ability to collaborate effectively. Include process owners, IT staff, and business users with in-depth knowledge of the processes that will be automated. In order to ensure that it is perceived as a beneficial implementation, it is important to offer structured guidance to everyone who is associated with the project.
Providing appropriate training to all the associated employees helps create a bevy of digital workers in the organization. Ensure that you train everyone, right from the senior, managerial pool, right down to the junior employees. Effectively explain the need for RPA in the company, how and where it will make a difference to their responsibilities, and the potential benefits of implementing RPA.

Use reusable components

Reusable components are an important aspect of building efficient and effective RPA projects. Reusable components are pre-built software modules that can be easily integrated into an existing bot or process, reducing development time and improving overall performance.

Taking advantage of reusable components saves time, increases reusability and decreases maintenance costs. Well-built reusable components lead to increased collaboration, consistency, and standardization across RPA projects.
Examples of reusable components are the following: Log in and logout to the web portals, Data scraping, Sending and receiving emails, etc.

Regular monitoring and reporting

Regular monitoring and reporting of project progress is critical to ensure the project stays on track. To effectively monitor an RPA project, project managers should have a clear understanding of the project’s scope, timelines, and objectives. They should be aware of the specific workflows and processes being automated and the expected benefits of the project. Regular monitoring can involve tracking metrics such as cost savings, productivity gains, and error reduction. This may involve weekly or bi-weekly status updates, progress reports, or more frequent communications during critical project milestones. Reports should be concise, accurate, and easy to understand, providing relevant updates on the project’s progress and any issues or concerns that need attention.

Building an effective communication flow

Effective communication is crucial for the success of RPA projects. It helps everyone involved understand the project’s goals and requirements. Clear communication ensures that challenges are addressed promptly, keeping the project on track. It also allows for adjustments to changes in scope or timelines. Team collaboration is improved, making work more efficient and accountable. Transparent communication about potential risks helps in planning ahead to avoid issues. Reporting and documentation aid in accountability and knowledge transfer. Addressing end-user concerns ensures a smooth adoption of automated processes. Continuous feedback loops encourage ongoing improvements, making RPA initiatives more successful over time. In summary, a strong communication flow is essential for coordinating, managing risks, and improving RPA projects.

Build proper error handling

An efficient flow always contains error handling that makes the process execution trouble-free and easy-going at production and saves the execution from unknown error stoppages. The first step in building error handling in RPA project management is to identify potential errors. Once potential errors have been identified, prioritize them based on their severity and impact on the business process. This will help in determining the level of attention required for each error. Any new errors identified should be incorporated into the error management plan for continuous improvement. All errors and corresponding solutions should be documented for future reference and to avoid similar errors in the future. The RPA project management team can build a robust error-handling mechanism that minimizes the impact of errors on the RPA process and delivers optimal results for the business process.

Testing

Testing is a crucial aspect of RPA project management. The team should conduct extensive testing to ensure the software performs correctly and meets all the project requirements. Testing of RPA projects should include several levels and types of testing, such as unit testing, integration testing, system testing, and acceptance testing. That way we can achieve several benefits like cost effectiveness, improved quality, reduced risk, and faster deployment.

Successful RPA implementation

Implementing an RPA solution is a complicated and costly business. It is necessary to ensure that your business processes are as less affected during the implementation as possible. Proper project management is the key to successful RPA implementation. With the right planning, team, software, and testing, you can ensure that your RPA project delivers the intended benefits.

The Tech world is undergoing constant changes, and at the heart of these changes lie the remarkable works of Artificial Intelligence (AI) and Machine Learning (ML). Their impact on the rapidly evolving tech landscape is undeniable and unforgettable, as it drives innovations and advancements that were once unimaginable. These powerful technologies are shaping a future where possibilities are endless, making the fast-changing tech world a fascinating realm to explore.

AI and ML in Action: Real-World Applications

• Improving Customer Experience: AI and ML are being used to improve customer experience by analyzing customer behavior and preferences. For example, by using AI-powered chatbots, businesses can provide personalized support to customers 24/7. With ML algorithms, businesses can also analyze customer data to understand their behavior and preferences, and use that information to provide a more customized experience.

• Automation: AI and ML are being used to automate tasks and processes, freeing up time for workers to focus on higher-level tasks. For example, AI-powered robots can be used in manufacturing plants to automate repetitive tasks, while ML algorithms can be used to automate decision-making processes in finance and healthcare.

• Predictive Analytics: AI and ML are also being used to improve predictive analytics. With ML algorithms, businesses can analyze large datasets to identify patterns and relationships, and use that information to make predictions about future trends. This can be used in a variety of industries, including finance, healthcare, and retail.

• Personalization: AI and ML are being used to provide personalized experiences in a variety of areas, including e-commerce, advertising, and entertainment. By analyzing customer data, AI algorithms can recommend products and services based on their preferences, and ML algorithms can provide personalized recommendations for movies, music, books and many more.

• Improving Safety: AI and ML are being used to improve safety across industries such as transportation, manufacturing, healthcare etc. For example, AI-powered cameras and sensors can be used to detect potential safety hazards in the workplace, while ML algorithms can be used to predict and prevent medical conditions before they escalate.

AI and ML are revolutionizing the tech landscape. They enhance customer experience, automate tasks, predict trends, personalize content, and boost safety. Their impact is evident across industries, reshaping how we live, work, and engage with technology. As they advance, the future of tech holds even greater possibilities.

Another transformative aspect is task automation. Mundane and repetitive tasks that once consumed valuable time and resources can now be efficiently handled by AI and ML algorithms. This liberation from routine enables human resources to engage in more creative, strategic, and complex endeavors that drive innovation.

Moreover, AI and ML contribute to heightened safety and efficiency standards across industries. In sectors like manufacturing and transportation, AI-driven predictive maintenance ensures that equipment malfunctions are preemptively identified, minimizing downtime and enhancing workplace safety. This amalgamation of AI and ML reshapes not only the operational landscape but also the very fabric of our daily lives.

AI and ML: Shaping Industries, Redefining Future

• Automation and Efficiency: AI and ML are propelling rapid technological changes by driving automation and efficiency. Machine Learning algorithms, empowered by extensive data and master patterns are automating intricate tasks that were once time-consuming and prone to errors. Notably, AI-powered robotics in manufacturing execute precision tasks, accelerating production cycles and enhancing product quality.

• Personalized Experiences: AI and ML foster personalized experiences by analyzing user behavior for tailored content and recommendations. Entertainment platforms recommendations are based on viewing history, and e-commerce suggests products aligned with preferences, enhancing engagement and loyalty.

• Healthcare Revolution: The significance of AI and ML in healthcare is immense. These technologies are reshaping diagnostics and treatment, with ML models accurately detecting diseases from medical images. AI-driven chatbots provide initial medical guidance, while AI simulations aid in drug discovery, potentially revolutionizing treatment development.

• Financial Advancements: In finance, AI and ML fuel progress in fraud detection, algorithmic trading, and risk assessment. ML identifies anomalies in transactions for real-time fraud prevention, while AI-driven algorithmic trading makes rapid investment choices, reshaping finance through efficiency and risk reduction.

AI and ML’s synergy sparks transformative innovation across industries, their evolving impact set to intensify. From automation to healthcare revolution, these technologies drive rapid change. Embracing and staying updated with AI and ML is essential for thriving in this dynamic tech landscape.

Densen Puthussery, the author of this article, has been serving as a Senior Machine Learning engineer at Founding Minds, focusing on developing AI and ML platforms for the past few years.

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is a technology that automates repetitive, manual tasks using software robots, freeing up human employees to focus on higher value tasks.

RPA Implementation

Robotic process automation implementation allows businesses and organizations to automate different, often menial, mundane, and rule-based tasks across applications and systems that would otherwise fall under the scope of human responsibility. In short, RPA implementations aim to transfer process execution from humans to software bots. RPA implementation technology is a game-changer for businesses looking to improve efficiency and productivity while cutting costs.

By implementing RPA in an organization, you can streamline operations and free up your employees to focus on more important tasks.

Here is a guide to help you implement RPA in an organization:

• Identify the tasks that can be automated: Start by identifying the tasks that are repetitive, rule-based, and time-consuming. These are the tasks that can be automated with RPA. Create a list of these tasks and prioritize them based on their impact on an organization.

• Evaluate the potential benefits of RPA: Analyse the benefits that implementing RPA can bring to your organization, such as reducing processing time, improving accuracy, and freeing up human employees for higher-value tasks. You can also consider the ROI of implementing RPA.

• Choose the right RPA tool: Research and choose an RPA tool that suits your organization’s needs. Look for a tool that is easy to use, scalable, and can integrate with your existing systems.

• Develop a proof of concept: Before implementing RPA on a large scale, it’s best to start with a small project to test the waters. Develop a proof of concept by automating a single task and evaluate the results. This will help you understand the benefits and limitations of RPA and make any necessary adjustments.

• Choose the right team: Implementing RPA requires a team of experts who can design, develop, and deploy the automation. Choose a team with experience in RPA and the skills required to execute the project successfully.

• Train your employees: Once the RPA solution is deployed, it’s important to train your employees on how to use it. This will help them understand the benefits of automation and how it can improve their productivity.

• Develop an RPA strategy: Develop an RPA strategy that outlines the processes to be automated, the timelines for implementation, and the roles and responsibilities of the team members.

• Test and refine the automation: Once the RPA solution is developed, test it thoroughly to ensure that it meets the requirements and is error-free. Refine the solution as needed.

• Roll out the RPA solution: Roll out the RPA solution in a phased manner, starting with a pilot program before scaling up to the entire organization. Provide training to the employees who will be using the RPA solution.

• Monitor and evaluate: Once the RPA solution is in place, it’s important to monitor its performance and evaluate its impact on your organization. This will help you identify areas for improvement and make any necessary adjustments.

• Continuously improve the RPA solution: Keep refining and improving the RPA solution to maximize its benefits and ensure that it continues to meet the organization’s needs.

By following these steps, you can successfully implement RPA in your organization and enjoy its benefits.

• Boost Productivity
• Improve Efficiency to Generate Savings 
• Hit Accuracy Goals 
• Reliable Consistency
• Better Business Data Security 
• Produce Data for Important Analytics 
• Better Customer Service Experience
• Great Scalability

Return on Investment (ROI) is a project’s expected return in percentage terms. ROI is a critical tool for measuring the impact of your RPA investment with real data. With an understanding of your RPA initiative’s ROI, you can: Justify the initial investment to business leaders with data-driven evidence.

An investment in RPA can help companies optimize their processes, improve delivery of information, capture errors, alert users, and improve solutions that drive customer satisfaction.

Return on investment (ROI) in the form of both cost and time savings is expected to boost the RPA .

To calculate ROI: Subtracting the initial value of your investment from the final value of the investment. This is called the net return. You then divide the net return by the cost of the investment and multiply by 100.

Common Challenges in the RPA Implementation Process

Despite the many benefits of RPA, the implementation process can be challenging. It’s important to understand the challenges and prepare adequate strategies for dealing with each one.

Here are some of the key challenges businesses and organizations face during the RPA implementation process:

• Reluctance — Reluctance, or resistance to change, is one of the most common challenges associated with RPA implementation. Your employees might be hesitant to embrace new technologies, fearing that their jobs might be at risk. Therefore, it’s important to communicate the various benefits of RPA implementations to your employees and involve them in the implementation process to help them overcome their reluctance and resistance to change.

• Process Standardization — RPA implementations require the standardization of processes taking place within your organization’s operations to automate effectively. Without standardization, the RPA implementation can be both challenging and time-consuming. So, it’s a good idea to standardize your organization’s operational processes before implementing RPA technology.

• Technical Complexity — Depending on the architecture, the RPA implementation can be technically complex, requiring expertise in areas such as programming, data analysis, and system integration. This can prove challenging for organizations that lack technically proficient staff with the necessary technical skills. If you are worried about the technical complexity of implementation at your organization, it’s best to work with a reputable vendor and establish a Center of Excellence (COE) that can provide technical guidance.

• Change Management — RPA implementations often require and result in significant changes in the organization’s workflows, processes, and systems. This can be challenging for organizations that lack a structured approach to change management. It’s important to develop a change management plan that includes communication, training, and stakeholder engagement.

• Security and Compliance — RPA software bots often require access to sensitive data, which raises security and compliance concerns. To avoid potential legal issues, it’s crucial to establish adequate security protocols to protect the data and ensure compliance with industry standards.

• Scalability — Scalability of RPA implementations can be challenging, especially if your organization has many processes that need to be automated. Choosing an RPA solution that can be easily scaled is crucial, and establishing a process that would prioritize other automated processes is vital.

• Maintenance — RPA requires ongoing maintenance to ensure the technology continues operating efficiently. However, this can be challenging for organizations lacking the necessary resources. That’s why it’s important to establish a maintenance plan and work with a vendor that offers comprehensive maintenance and support.

Addressing these common challenges allows organizations to better prepare for RPA implementation and increase the likelihood of success.

RPA implementation can be challenging and complicated if it is done without proper planning and allocation of tools and resources. In the absence of these, the implementation of RPA can seem daunting and intricate. Therefore, having an RPA automation expert at your disposal can take off the burden from the organization. This also prevents failures and problematic anomalies. Moreover, you can study the RPA implementation examples to derive insight before implementing them.

Posted by Nikhil Jayan and Kathika.S.M

Building modern web applications can be a tough job! It’s important to use a mix of different tools and frameworks to create something strong and scalable. Angular and ASP.NET Core are two powerful tools that lots of developers use to create cool web apps. At Founding Minds, we’ve used both of these tools in different projects and seen some amazing results! In this article, we’ll talk about the benefits of using Angular with ASP.NET Core and how this awesome combo can help developers create top-notch web applications.

Angular for Frontend

Angular is an awesome open-source JavaScript framework that is ideal for building powerful web applications with excellent performance. With Angular, developers have everything they need to build mobile and desktop applications quickly and efficiently. Angular has many great features, like two-way data binding, dependency injection, and modular architecture, making it easy to create scalable and maintainable code.

One of the coolest things about Angular is that it’s easy to learn, so developers can start building applications fast. Plus, Angular offers extensive documentation and tutorials to help developers get started quickly.

ASP.NET Core for Backend

ASP.NET Core is a web framework developed by Microsoft that lets developers create modern and cloud-based applications. This framework is perfect for building scalable and robust web applications that can handle a ton of requests. ASP.NET Core has a modular design, which means you can easily customize and extend it based on your project’s requirements.

One of the best things about using ASP.NET Core is that it’s open-source, which means it has a huge community of developers who contribute to its development. This makes it super easy to get help and support from others when you need it. Plus, ASP.NET Core offers simple upgrades and quick development release cycles, so you can stay up-to-date with the latest technologies without breaking a sweat.

Benefits of Combining Angular with ASP.NET Core

When it comes to building modern web applications, combining Angular with ASP.NET Core offers several benefits, including:

1. Faster Development: By combining these two powerful tools, developers can create applications quickly and easily. There are plenty of templates and open-source or paid libraries available for both frameworks that can help accelerate development.
2. Cost-effective Deployment: Deploying an application built using Angular and ASP.NET Core is cost-effective compared to other solutions. These tools offer easy deployment options that can help reduce costs associated with hosting and managing an application.
3. Easily Scalable: Both Angular and ASP.NET Core are designed to be scalable. This means that as your application grows, you can easily scale it to handle more requests without having to rewrite large portions of your code.

Example Use Cases

There are several example use cases for combining Angular with ASP.NET Core. Here are a few:

• E-commerce Websites: E-commerce websites require a lot of functionality, including user authentication, payment processing, and inventory management. By using Angular for the front-end and ASP.NET Core for the back-end, developers can create a powerful e-commerce website that can handle a large number of requests.

• Enterprise Applications: Enterprise applications require a lot of functionality and are often used by a large number of users. By combining Angular with ASP.NET Core, developers can create a scalable application that can handle a large number of requests and users.
• Social Networking Applications: Social networking applications require a lot of functionality, including user authentication, user-generated content, and real-time messaging. By using Angular for the front-end and ASP.NET Core for the back-end, developers can create a powerful social networking application that can handle a large number of requests.

Final Words

Combining Angular with ASP.NET Core offers a powerful solution for building modern web applications. With Angular’s rich set of features and ASP.NET Core’s scalable and modular architecture, developers can create robust and scalable web applications quickly and easily. If you’re looking to build a feature-rich web application, then consider Angular with ASP.NET Core.

“ASP Core and Angular have been invaluable tools for developing our financial applications. The seamless integration between the two technologies has allowed us to create a user-friendly and efficient system that meets the needs of our clients. With ASP Core’s robust back-end and Angular’s dynamic front-end, we have the power to deliver top-notch financial solutions to our customers. It’s truly exciting to see how these technologies are transforming the industry, and I feel privileged to be a part of it.”

– Jayan Mathew, Technical Lead, Founding Minds Software

At Founding Minds, we believe that the combination of Angular and ASP.NET core underscores the most effective way to design web applications. We are reliable and experienced product development partners for your cloud, mobile, web and enterprise applications. Our products cater to a variety of industries including financial, hospitality, mobile technology, financial intelligence, healthcare and so on.

Rounding – Simple, but when it comes to the financial domain, it is more complex than it sounds! Rounding can be especially problematic when multiple rounds of calculations are involved, such as in compound interest calculations or currency conversions. A small error in calculations can lead to significant discrepancies over time. 

We faced unique challenges in a billing and invoicing project to keep utmost precision and still present the user with significant digits. We decided to do all calculations with high precision and round only in the UI and invoices, which was an acceptable solution until we ran into more complex issues.

How do we distribute $100 equally to three persons without rounding issues?

If you want to equally distribute $100 to three different persons without rounding issues, you can use the following method:

• Divide the total amount by the number of people you want to distribute it to. In this case, $100 divided by 3 equals $33.33
• Multiply the result by the number of people to get the final amount each person should receive. In this case, $33.33 x 3 = $99.99
• Subtract $99.99 from the total amount and give it to the last person. In this case, $0.01

By following this method, you ensure that each person receives $33.33 (100/3) and that the total amount is distributed without any rounding issues. This is a commonly used method in the financial domain. It’s important to note that this method will only work if the total amount is divisible by the number of people without a remainder. If the total amount is not divisible, it may not be possible to distribute it without rounding errors.

How do we decide on the last person?

Round Robin? Alphabetical? Again, this is tricky if we need to regenerate or redo the calculation later. It is prevalent in billing and invoice to revise an invoice in case of an error, and we need to set some standards.

Common Rounding Issues

One common issue in rounding is known as “rounding error.” This occurs when a number is rounded to a certain number of decimal places, but the original number contains more decimal places than the rounded number. For example, if a number like 0.1 is rounded to the nearest whole number, the result would be 0. However, the actual value of 0.1 is slightly more than 0; over time, this slight difference can add up to a significant error.

Another area for improvement with rounding is that it can create discrepancies between different systems or platforms. For example, suppose a financial calculation is performed on a system that uses different rounding rules than another. In that case, the results may be different, even if the inputs are the same.

To avoid these issues, it’s essential to use consistent rounding rules across all systems and platforms and to be aware of the potential for rounding errors in calculations. Additionally, it’s a good practice to use more decimal places in calculations to minimise the impact of rounding errors.

The rule of thumb when it comes to rounding is that if you are dealing with large sums of money and rounding to the nearest decimal does affect materiality, then do not round. The rounding is acceptable so long as the rounded amounts will not mislead the reported information.

Different Algorithms

There are numerous algorithms to round the numbers, each one with different trade-offs to get rid of the unwanted digits. Round-toward-nearest, Round-half-up, half-down, half-even, half-odd, ceiling, floor, towards-zero, away-from-zero, round-up, round-down and even truncate is used in rounding.

• Arithmetic Rounding: This is the most common rounding algorithm, and it is based on the “round to nearest” rule. It rounds a number to the nearest whole number, with numbers exactly halfway between two whole numbers rounded to the nearest even number.
• Bankers Rounding: This algorithm is similar to arithmetic rounding, but it is used mainly in financial systems. It rounds a number to the nearest whole number, with numbers exactly halfway between two whole numbers rounded to the nearest even number. It’s also known as round to nearest even.
• Ceiling Rounding: This algorithm always rounds a number up to the nearest whole number. This can be useful when it’s crucial to ensure that a value is never less than a certain number.
• Floor Rounding: This algorithm always rounds a number down to the nearest whole number. This can be useful in situations where it’s important to ensure that a value is never greater than a certain number.
• Truncation: This algorithm simply cuts off the decimal part of a number without rounding. This can be useful in situations where precision is not important, and the final result should always be less than or equal to the original number.

The choice of a rounding algorithm can significantly impact the final result, especially when working with large data sets or in financial calculations. DotNET uses banker’s rounding by default.

Standards in Rounding!

Does rounding numbers in the financial domain have a common standard? There exists some standard when it comes to currency conversion.

• International Financial Reporting Standards (IFRS): IFRS is a set of accounting standards developed by the International Accounting Standards Board (IASB). It includes guidelines on rounding financial numbers in financial statements, such as the requirement to round amounts to the nearest whole number or the nearest multiple of 10.
• Generally Accepted Accounting Principles (GAAP): GAAP is a set of accounting standards used in the United States. It includes similar guidelines on rounding financial numbers as IFRS and requires that any rounding errors should be immaterial and insignificant.
• International Organization for Standardization (ISO): ISO has a standard for Rounding off numerical values, which is ISO 80000-1:2009. It provides guidelines on rounding numerical values in general and not specific to the finance domain, but it’s widely used in financial systems.
• The Federal Reserve Board (FRB): The FRB, the central banking system of the United States, has guidelines on rounding financial numbers for bank reporting and financial statement preparation.
• The European Central Bank (ECB): The ECB, the central banking system of the European Union, has similar guidelines on rounding financial numbers as the FRB.

Data types to Store Rounded Digits!

The decimal data type is the best choice to store currency in the financial domain because it allows for high precision and can handle decimal numbers, which is important for monetary values. Never use binary floating-point types for storing currency. Because floating-point numbers have a limited number of digits, they cannot represent all real numbers accurately: when there are more digits than the format allows, the leftover ones are omitted – the number is rounded. If you really need your results to add up exactly, especially when you work with money: use a special decimal datatype. If you have no decimal data type available, an alternative is to work with integers.

It’s crucial to ensure that the decimal data type is implemented to handle the range of expected currency values, it’s compatible with the systems and platforms, and it can take the currency formatting and localisation. By using consistent rounding rules and being mindful of the potential for errors, businesses can minimise the impact of rounding on their financial calculations.

Author: Bharath Krishna

Bharath Krishna is a Technical lead at Founding Minds with 8+ years of experience in the finance domain. In his current role at FoundingMinds Software Private Limited, he is responsible for the technical direction and delivery of dotNET-based solutions. He is proficient in the latest .NET technologies such as DotNet Core, EF Core, Worker service etc. He is always looking for ways to improve the efficiency and effectiveness of the development process. He has experience with AWS, Azure & GCP. He is committed to staying current with the latest trends and technologies and is always open to exploring new approaches to system design.

Reference:  https://floating-point-gui.de/