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The Science Behind Cognitive Overload

Here's Why Talent Isn't Rare. This Is.

Let's learn about via these 66 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Software Design /Learn Repo 1. Resolving the Blocked a Frame with Origin "null" From Accessing a Cross-Origin Frame Error Learn how to resolve the "blocked a frame with origin "null" from accessing a cross-origin frame error." 2. Saying No - A Five-Point Framework Learn how to identify the signals that it’s time to say no 3. The Goals of Software Design A misguided software design can hurt both the business and the technology. It is therefore vital to understand an organizations goals and optimize accordingly. 4. Design Pattern: Strategy Pattern in Practice Learn strategy design patterns with practical examples. Why and how to use strategy patterns in software design. 5. Common Mistakes Made by Developers While Redesigning Mistakes made during software redesign - Perfection, following a trend, hitting with a big hammer 6. How Loop Is Building A Virtual Restaurant Operating System Sundar Annamalai and Vinod Pachipulusu about starting Loop as a virtual restaurant platform to help restaurants with their business. 7. How I Gather Technical Requirements before Building Systems Learn about how to gather technical requirements for the systems you build. 8. How to Use Low-code Tools to Fill Your Developer Talent Gap The demand for enterprise applications is growing faster than IT capacity due to the need for digitalization everywhere. Low-code is here to help! 9. Understanding Domain Driven Design (DDD) DDD is not about technology. Instead, it's about understanding the problem you’re trying to solve in a business context. 10. How Experienced Software Engineers Evaluate Designs Experienced software engineers learn what works after maintaining their work for years. 11. How to WOW Users With Intuitive UX Design 70% of online businesses fail because of bad usability. The solution to this challenge is an approach known as Intuitive UX Design 12. Python Interfaces: Abandon ABC and Switch to Protocols Reasons to use Python Protocols instead of ABC library for Python interfaces. 13. Write Great Unit Tests by Writing Unit Tests that Fail Any test that has never failed has no value, and therefore, can be deleted. In order for any test to add value, it must have failed at least once. 14. Redefining Serverless: A Change in Approach and Architecture Serverless is a fantastic way of building applications, but there is a lot more to consider than simply Lambda functions. 15. Distributed Computing: Illusion of Single System What is common between streaming movies on Netflix, searching for information on Google, buying clothes on Amazon? You interacted with data services built on distributed systems. You interact with the largest distributed system daily: the Internet. 16. What is Design Debt and Why is it Important? Design debt is a common yet rarely discussed problem startups face in iterative and incremental software development. Learn what it is, where it comes from, and why it is so important to prevent it. 17. How to Become a Better Engineer: Steel Threads Learn a software design approach that will make you a better engineer. 18. Software Design Principles: Doing Little Things Right Designing software systems is about tradeoffs and making tradeoff decisions is hard. You always feel like you are loosing one thing or the other but that’s not what we are going to talk about today. 19. 5 Main Things That Will Help You Understand Why Technology Is Important In Business Technology in business is a phenomenon that is taking over the world. Business is certainly not going anywhere anytime soon; technology is the only way to go. Business needs to have the right tools in place to compete in a competitive corporate environment. 20. The Complete Guide to Becoming a Software Architect "I want to become a solution architect. What are the resources to learn more about architecture?" - We are all asking the wrong question. 21. 5 Essential Software Architecture Design Principles This post will talk about popular design principles used in the software architecture world. Architecture is about the decisions you wish you could get right early in a product or project lifecycle. 22. From Monoliths to Microservices: Migration in Practice This isn't another "break down the monolith" article. This post is about making the applicable decisions, measuring and verifying the results 23. Software Architecture Basics: What, How & Why Software architecture, design and process of architecting explained using a case study. 24. The Clean Code Book for JavaScript Developers: A Quick Summary This blog post summarizes the book “Clean Code” written by Robert Martin on how to write a readable and maintainable code. 25. 10 Reasons Why You Should Learn How To Develop Video Games If you ask any software developer, there’s a decent chance that they have tried their hand at game development. It seems like a natural fit since it uses many of the same skills. What’s more, programmers come into it with the knowledge that most aspiring game makers lack: how to write good code and create effective software. 26. How to Test UX Design: UX Problem, Validation & User Testing The process of testing design is becoming more and more important in the UX world. It allows you to determine if your design will work for your users and solve 27. How to Build High-Performing Product Teams A product team is the backbone of any company with a digital offering. They're responsible for constructing goods or a service that not only provides value to users but is also a reflection of the organization they work for. Product teams spin many plates at once, spanning duties from product management, product marketing, user experience, and product analytics. In the below article, we talk through the 4 characteristics that make up a high-performing product team. 28. A Guide to Bidirectional Scrolling in Responsive Designs Incessant scrolling while using an application is perceived as a flaw on both mobile and desktop. To address this concern, a shift to horizontal scrolling of elements is in trend now. 29. Figuring Out When Not To Use DI, IoC, and IoC Containers in .NET C# Learn the best practice on when to use Dependency Injection (DI), Inversion of Control (IoC) and IoC Containers. DotNet .NET CSharp C# Design Architecture 30. How to Stop String.GetHashCode() in .NET C# from Driving You Crazy Know when to depend on String.GetHashCode() in DotNet (.NET) CSharp (C#), and when not. Coding Programming Software Development Engineering Architecture 31. Progress From Coding Software To Engineering It Before I learned how to build apps with code, I thought software development would be like building something out of wood... you learn a couple of basic skills, 32. Is it Enough to Define IMyInterface<T>? Do I Need IMyInterface as Well? Best Practice for designing Interfaces in DotNet (.NET) CSharp (C#). When to define a non-generic interface in addition to the generic interface. 33. The One and Only Software Design Principle If we build our entire paradigm on a single rule, we can keep it simple and make excellent models. 34. Major Aspects of Software Cost Estimation It is well-known that software engineering is at the lead of the services that use outsourcing and outside contractors. 35. Solar Architecture:  A New Perspective on Enterprise Software Development The development life cycles of applications in an enterprise environment often follow a common theme. An application begins life with a clear purpose in mind and over time has requirements brought in that cause incremental additions to the scope of the original concept. These additions could either be completely in sync with the original concept or perhaps the requirement just had no better home to belong in. As the application matures to an end-of-life state it has now grown far past its original intended purpose and is in dire need of a refactor — breaking down the monolithic beast into smaller and more succinct components. 36. Exclusive Integrated Development Environments to Code The Best In 2020 Software development or programming is not easy until you are a hardcore programmer. A while back, programmers had to code on dull command prompt screens, run the code on a separate compiler then debug it on some third platform. Today, although, programming needs the same dedication and efforts, it has become easier to code, compile, and debug in the same environment. All thanks to IDE-Integrated Development Environment platforms, which enable coders to perform all the major functions of programming in one place. 37. Technical Debt or Technical Fraud Technical Debt is one thing, but when let to fester for too long, it can become Technical Fraud. A much more dangerous and much more insidious predator. 38. The Untold Truth of Test Driven Development Your Practical Introduction to Test Driven Development 39. How ARTH Stablecoin Was Built and Designed to Protect The User's Purchasing Power Mitul Gajera, designer of the stable value coin ARTH explains and gives insight into how the cryptocurrency was designed to protect the user's purchasing power. 40. AI Graphic Design Software: The Top 7 Available Eye-catching and beautiful graphics rule digital media. Any business requires graphics in its brand promotion creatives to showcase their brand and convey their message clearly. Whether you wish to make a featured image for your blog or edit product photos for posting on social media, you need the best graphic design software to make or edit your graphics. 41. Coupling: The One and Only Software Designing Problem A root cause analysis of all failures of our software will find a single culprit with multiple costumes. The enemy is always there. Many times disguised as laziness, sometimes simplification, and very frequently with the optimization outfit. 42. Exception Handling with Examples Exception handling is common to most programming languages, and the mechanism and behaviors are similar in most languages: try\catch\finally. It is well documented. We are not going to discuss that. 43. NULL: The Billion Dollar Mistake He is not our friend. It does not simplify life or make us more efficient. Just more lazy. It is time to stop using null. 44. Mastering APIs: A Complete Guide for Beginners What actually are APIs and how do they work? Find out by looking at important APIs across the software stack and learn some computer science along the way! 45. What is (wrong with) software? Software is eating the world. Those of us who work, live and love software do not usually stop by to think about its meaning. 46. The 5 Principles of Agile Software Development Agile methodology breaks up the project into many phases and distributes the workloads across the teams. 47. Creating a React Application to Solving Every Sudoku Puzzle Peter Norvig, the legendary Google mogul and AI titan, wrote a python essay to solve every Sudoku puzzle 10 years ago. Back then, he may not have expected that his code will inspire so many other posts and to be ported to so many different languages. For JavaScript, the latest version of the solver that I found was einaregilsson/sudoku from 2014 48. Demystifying Software Engineering: Being Approached to Build a Feature or Tool (Part 1) The aim of this series of posts is to help demystify what it’s like to do software work and tasks in a professional environment. 49. Writing Software from the Outside In Whenever you’re developing software, a certain amount of refactoring and rewriting is inevitable. This is sometimes due to a new idea that will simplify the design or a change to the project requirements. But unfortunately, it often also happens because of a misunderstanding about how the software will connect and interact with its external environment. While it’s impossible to completely avoid such miscommunication, you can minimize it by using a principle I call writing software from the outside in. 50. Software Design Patterns Explained Software design patterns have been used to package solutions to many common design conundrums. Each one has its own advantages and drawbacks to consider. 51. Nude Models - Part I : Setters Ye olde Reliable Data Structures and Their Controversial (Write) Access. 52. How To Design Domain Model in Kotlin Type-safe domain modeling in Kotlin. If it compiles, it works. With Valiktor and Konad 53. Test-driven Development: How to Write Unit Tests Tests are a way to explicitly set expectations about code. You establish them to allow the machine to check whether your code meets the expectations. 54. A Better Implementation of Enhanced Repository Pattern in .NET C# Implement a better enhanced Repository Pattern in DotNet (.NET ) CSharp (C# ) following best practices Dependency Injection (DI) Inversion of Control (IoC) 55. The Main Equations That Can Make Paging/Partitioning Easy Learn paging/partitioning mathematical equations and how to apply them in JavaScript (JS) and DotNet (.NET) CSharp (C#) code. Coding Programming Development 56. Is it Crystal Clear for Everybody That a Date Should Not Mutate? To mutate is to evolve. It was proposed by Sir Charles Darwin and we use it in the software industry. But how mutable should software be? 57. Fail Fast Philosophy, Explained Failure is fashionable. Making is much easier than thinking and failures are not stigma, let’s take this idea to our code. 58. The Era of Opinionated Productivity Software: Superhuman, Roam, What’s Next? Productivity software is indispensable for all of us. Dedicated tools for creating spreadsheets, writing down notes, managing our to-dos etc. ensure that we are productive in our professions. Incumbent vendors (e.g. Microsoft, Google) have dominated this space for many years — yet, there is a chance for new players to enter the stage. New challengers such as the E-Mail client Superhuman or the note-taking tool Roam Research can compete by developing highly opinionated products which deliberately omit flexibility. Instead, the software is stringently crafted around a very particular way of approaching a process, which is inherently seen as superior by the firm. 59. 40+ Thought-Provoking Software Engineering Quotes Sometimes a short thought can bring amazing ideas. There are some great quotes I’ve been collecting. I use them as inspiration or motto for some articles. 60. 7 Best Product Design Tools Every Team Should Consider Yes, we've finally reached a point where design is such a huge piece of a product's creation and maintenance that a whole paraphernalia of software will definitely be an essential asset to your projects. Each program will fill different needs and facilitate the best and most efficient work deliveries, be it structure, visual design, illustrations, interactions or animations. 61. 7 Best AI Logo Maker Software Available Online For any brand, creating its own identity is a challenging task. Especially for a start-up and small businesses, brand recognition is even more essential. Brand recognition can go a long way in influencing the success of a business. Every scale business spends a huge amount on marketing their brand and creating their imprint in people’s minds. 62. Using the Builder Design Pattern in .NET C# to Develop a Fluent API A step-by-step guide to developing a Fluent API from scratch in .NET C# using the Builder Design Pattern. 63. How to Find the Stinky Parts of Your Code (Part V) Are we done on code smells? Probably never! 64. Difference Between Inheritance And Composition With the introduction of OOPs, Inheritance and Composition entered our senses and still confusing us. 65. Let's Build It: Real-time Session Invalidation How would we build an experience like the one above? Demo Repo Some applications need to limit users to a single client or browser instance. This post covers how to build, improve, and scale this feature. We begin with a simple web app with two API endpoints: 66. Singleton Pattern: The Root of All Evil Allowed global variables and supposed memory savings Thank you for checking out the 66 most read stories about Software Design on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

66 Stories To Learn About Software Design

In an era where technology is essentially reprogramming the DNA of almost all industries, artificial intelligence emerges as a revolutionary titan. Particularly in the adrenaline-charged theater of forex trading, AI is not just a supporting act; it has become a headliner catalyzing transformations as exhilarating as they are intricate. With every tick of the clock, AI codes hold the power to go through a maze of data, that do not just amplify gains but also radically shift the landscape for experts and newbies alike. making decisions However, the rise of AI is not a one-sided narrative of triumph. It incorporates a double-edged sword, opening doors to a world of prospects while simultaneously tossing in new complexities that traders cannot afford to ignore. This article digs deep into the multifaceted impact of AI on the ever-fluctuating universe of online trading, revealing how it is shaking things up and what traders should be aware of in this swiftly evolving domain. How Artificial Intelligence can impact the future of online trading “Artificial intelligence will . Follow that out further, by about 2045, we will have multiplied the intelligence - the human biological machine intelligence of our civilization - a billion-fold.” – Ray Kurzweil, American inventor, and futurist. reach human levels by around 2029 In the sprawling digital expanse, a newcomer is painting the canvas of online trading, i.e., Artificial Intelligence. It is the artisan blending the brushstrokes of human cognition, endowing software with the ability to self-learn and accomplish delicate tasks. Particularly in the realm of online trading, AI is increasingly viewed as an indispensable asset for sifting through market data, discerning patterns, and forecasting looming market shifts. While AI is still a budding affair, its enchantment in trading is unmistakable. The numbers, as presented by Fortune Business Insights, are a testament. The algorithmic trading market's valuation was already at a staggering USD 2.03 billion in 2022, and it is slated to burgeon to a jaw-dropping USD 3.56 billion by 2030. This heralds a Compound Annual Growth Rate (CAGR) of 7.2%. The takeaway? An escalating reliance on AI-led algorithmic strategies by traders. What AI brings to the trading table is a dexterity beyond human means. The remarkable agility of AI in real-time market assessment enables it to deliver rapid and meticulous insights. Analyzing copious amounts of market data in real time, AI mechanisms swiftly pinpoint market dynamics, determine trends, and unveil golden trading opportunities. This immediate data processing empowers traders, allowing decisions that outpace conventional manual analysis. Another prominent role of AI in forex trading is its capability to facilitate novice traders. For the greenhorn traders, the forex market often appears as a complex maze. AI is like a mentor that substantially bridges the historically challenging learning chasm in forex, granting newcomers real-time market analytics, predictive tools, and more. With AI as the guide, the uncharted terrain of trading becomes less intimidating, opening avenues that were once exclusive to the trading elite. Overall, Artificial Intelligence is not just another buzzword; it is the harbinger of a new era in digital trading. As the wheels of innovation churn, the melding of AI and online trading promises a horizon filled with untapped opportunities, setting the tone for a ground-breaking epoch in trading. Here is an in-depth overview exploring the various ways artificial intelligence is shaping the industry, with a focus on its long-term impact in the years ahead. Enhanced trading analysis and data processing – Improved predictive power The vastness of the financial world presents data in unimaginable quantities, from decades of historical price data to the daily dose of news articles. AI thrives in this space, effortlessly going through mountains of information. Where a human might struggle to keep up, AI consistently and efficiently processes data, identifying repeatable patterns and trends that often remain obscured to us. The result? Traders are equipped with sharper insights and clearer pictures of market landscapes. Additionally, predicting market movements has always been the Holy Grail of . With AI, this task comes closer to reality. By diving deep into historical and current data, AI showcases an uncanny ability to forecast future currency shifts. Gone are the days of pure speculation; today's traders, armed with AI-driven predictions, can make decisions rooted in robust data analysis, greatly reducing the unpredictable risks once synonymous with forex trading. trading Also, it is more than mere digits and numbers on a graph; today's trading scene is heavily influenced by the sheer power of headlines and the social media realm. AI, with its natural language processing capabilities, can gauge the digital world's emotional ebb and flow, crafting those raw sentiments into actionable trade decisions. In a universe where a single tweet or post can tip financial scales, AI can ensure traders always have the upper hand. Eventually, from streamlining complex analyses to providing notable predictive power, AI is birthing a system that is swifter, more intuitive, and data-reliant than we have ever glimpsed. Automated trading systems and AI robots AI algorithms present the online trading world with another revolutionary tool: Automated trading systems. Pulsating with AI codes, these automated systems unearth trading opportunities, liberating traders from the tediousness of manual ventures. Not only do they execute trades with impeccable timing based on set criteria, but they also bring the potential for consistent performance by back-testing and fine-tuning trading strategies. Envision a trading confidant, attuned to your ambitions, your thresholds, and your risk boundaries. These new-age AI bots do just that. They resonate with the traders and are customizable allies, able to reflect a trader's aspirations. In other words, they are dynamic — adjusting their strategies to the market situation, ensuring they are always poised for maximum profitability. Another essential consideration is that automated trading systems, much like the financial markets, never rest. Their unwavering vigilance stands as a hallmark feature. Whether it is midnight or noon, these systems are always alert, ready to react instantly to any market shift. The beauty of it? No human intervention is required most of the time. While traders rest, the AI-imbued system continues to safeguard and grow their investments. High-Frequency Trading High-frequency trading refers to the rapid execution of a large number of trades within split seconds and AI is taking it to the next level. For traders, the fusion of AI with high-frequency trading can be a game-changer. Whether it is the ability to capitalize on tiny price differences that were previously impossible to catch, or the chance to dive into the market during times of amplified volatility, the opportunities can be vast with high-frequency algo trading. AI-powered risk management Traditional risk management often feels like navigating through a fog—there is only so much a human can see. Here AI functions as the light that cuts through the fog by analyzing complex market data. From identifying precarious market conditions to sniffing out market manipulation, AI algorithms provide a comprehensive risk assessment. New traders, especially, find this an invaluable shield against the hidden pitfalls of forex trading. Moreover, being humans, emotions can sometimes get the better of us—often at the least opportune moments, like during trading. AI serves as an emotional firewall, making data-based decisions devoid of human biases and glitches. The outcome? Significantly enhanced risk management, where every move is calculated and rational, not reactive. By combining unparalleled data analytics with an immunity to emotional sway, AI is definitely fortifying the future of risk management in online trading. Some emerging challenges related to AI and forex trading While AI offers a promising horizon in online trading, it is also essential to recognize and navigate the accompanying challenges with foresight. Some of them are: Just as the human mind thrives on quality education, AI codes flourish on high-quality data. An AI's prediction is as good as the data it consumes. It is imperative to have diligent data management processes, ensuring data's accuracy, reliability, and timeliness. Furthermore, in an age of increasing cyber threats, the security of financial data is paramount. One breach and trust can crumble. Data Quality and Security: The magnetism of AI can sometimes overshadow human intuition. Overdependence on AI tools might marginalize human judgment, which remains crucial. An unforeseen situation, or a mere technical fault in the AI system, can amplify risks if traders sideline their instinct. Over-reliance on Technology: As the fusion of AI and finance thickens, so do concerns about its . Questions of job displacement, privacy infringements, and adherence to ethical norms surface. As we thread this new era, a balanced approach—emphasizing transparency, accountability, and oversight—is crucial to harness AI's potential without discarding ethical considerations. Ethical and Regulatory Concerns: societal impacts Speed is of the essence in trading and with AI-driven high-frequency trading, it is supersonic. While this rapidity can be an asset, it is also a potential destabilizer. AI algorithms can set off colossal buy or sell tides in a moment, leading to unpredictable market movements like flash crashes or sudden price surges. Market Volatility: Bottom line Artificial Intelligence is undeniably making waves in the world of online trading, unlocking opportunities that were previously unimaginable. Its adeptness at churning through heaps of data and pinpointing hidden trends presents an enhanced dimension of market analysis. Yet, amidst this AI-driven metamorphosis, the seasoned wisdom, gut feeling, and strategic foresight of human traders cannot be pushed aside. For the ultimate proficiency in online trading, it is not about choosing one over the other, but rather cultivating a harmonious blend of AI precision with human insight. The true promise of the future lies in this balanced fusion.

AI-Powered Online Trading: Opportunities, Challenges, and the Road Ahead

AI-চালিত অনলাইন ট্রেডিং: সুযোগ, চ্যালেঞ্জ এবং সামনের রাস্তা

KI-gestützter Online-Handel: Chancen, Herausforderungen und der Weg in die Zukunft

Comercio en línea impulsado por IA: oportunidades, desafíos y el camino a seguir

Trading en ligne alimenté par l'IA : opportunités, défis et chemin à parcourir

एआई-संचालित ऑनलाइन ट्रेडिंग: अवसर, चुनौतियाँ और आगे की राह

AI を活用したオンライン取引: 機会、課題、今後の道のり

AI 기반 온라인 거래: 기회, 과제 및 앞으로의 길

Negociação on-line baseada em IA: oportunidades, desafios e o caminho a seguir

Интернет-торговля на основе искусственного интеллекта: возможности, проблемы и дальнейший путь

Yapay Zeka Destekli Çevrimiçi Ticaret: Fırsatlar, Zorluklar ve Önümüzdeki Yol

Giao dịch trực tuyến được hỗ trợ bởi AI: Cơ hội, thách thức và con đường phía trước

人工智能驱动的在线交易：机遇、挑战和未来之路

Let's learn about via these 67 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Devops Principles /Learn Repo 1. How We Scaled To Microservices Architecture With A Data Flow-driven Approach Microservices architecture: It was proposed to develop a data flow-driven mechanism for microservices — oriented decomposition. Data work separately with the site 2. Top DevOps Myths Debunked - [DevOops Part 1] Today, we are debunking some of the most popular myths about DevOps. 3. 5 Ways to Overcome CI/CD Challenges 5 Challenges of CI/CD - how you can solve them! 4. Why Backend and DevOps Roles May Become One in the Future I believe that the job duties of “back-end” and “DevOps” engineers will coalesce to include almost everything that “the user doesn’t see”. 5. How Shift-Right Testing Can Build Product Resiliency Shift-right testing improves product resiliency by uncovering issues that  surface under heavy user traffic and are difficult to simulate in test environments. 6. Strategies for Using Config as Code This post explains some good practices for using Config as Code and how to adjust your strategy in different situations. 7. Revealed: The Most Important DevOps Tools For Newbies DevOps is practice/tools to run the services and applications on Cloud infrastructure. Scripting,git,Jenkins,Ansible/Puppet/Chef,Docker,GCP,AWS,Azure 8. 5 DevOps Security Challenges in 2020 In the beginning, a single developer worked on 100% of the code base that lived on their machine.  Inevitably, at some point, they wanted to share their code, or back it up, or even create a simulated testing environment.  On that day, they began the process of moving from Dev to DevOps.  Since then, the world has changed drastically. 9. Can we apply DevOps principles to Project Management? It is always worth remembering that good ideas are contagious. Other minds have wrestled with problems similar to yours before and effective solutions tend to bubble up and can be re-applied to new initiatives in ways that can often appear obvious in hindsight. 10. BizDevOps (DevOps 2.0) Is the New Iteration of DevOps BizDevOps breaks divisions between Business, Development and Operations teams to deliver business value fast. Discover the principles and benefits of BizDevOps. 11. Tracing: An Essential Component of Observability In this article, I am going to shed light on one important aspect of observability - tracing. 12. An Introduction to DevOps In this post, we take a surface-level look at the parts that make up DevOps. We explore the concepts, tools, and unique roles that make it work as it does. 13. Eugene Cozac on Creating His Own Framework and Becoming a Top-tier Developer Today, to become a junior-level web developer you don’t need even a college degree. There are tons of bootcamps and short-term programs for those who would like 14. Three DevOps Cultural Changes You Need To Adopt The Three Most Important Lessons that anyone in DevOps needs to learn to get better: Set clear goals, Create Safe Environment, Structure Your Technology 15. The Necessity for DevOps Improvement in Government DevOps provides a collaborative approach to development and operations so government IT can adapt to today’s challenges and prepare for new digital threats. 16. Should DevOps Engineers Write Code? “DevOps” is one of the most misunderstood terms in the software development industry. 17. CI/CD Best Practices for Salesforce DX With digital transformation and the move to online operations, companies are increasingly relying on Salesforce to manage many of their operations. New business needs and opportunities constantly increase the workload of Salesforce teams. In this context, teams are turning to Salesforce CI/CD to automate manual workflows, which help them handle these growing workloads and quickly deliver quality work to their end-users. This article gives you a short overview of how to implement CI-CD in Salesforce DX. 18. How to Fail at DevOps 19. 8 Debugging Techniques for Dev & Ops Teams This blog highlights eight debugging techniques that will help Software engineers adopt the DevOps approach to debugging a code. 20. DevOps as-a-Service OR Do you Really Need a DevOps Team DevOps as a service is a rising philosophy in purpose development. DevOps as a method moves customary teamwork to development and process team to the make unclear, where lots of the method can be robotic using stackable virtual expansion tools. 21. 7 Common Misconceptions about DevOps Metrics Metrics are crucial to DevOps and Continuous Delivery as part of the process of continuous improvement. However, you must balance collecting and displaying data 22. DevOps 2.0: The Value of SRE Mixing DevOps and SRE gives a brilliant outcome. Learn more about site reliability and its principles to ensure business growth and top tech performance. 23. Why Your Project's 1st Commit Should Have a Working App & CI/CD Pipeline Automating The Automation Behind DevOps 24. So You Took Down Production - Now What? You're pushing to main, something you've done 100s of times before, and by the time you go to refresh your production environment, you notice something's wrong 25. Can You Land a Junior Role as a Dev Ops Engineer? You'll see "junior" level DevOps and SRE roles. You'll also see several people asking how to get into DevOps or SRE as a junior-level candidate. 26. The Top 8 Registries for Containers There are many container registry options on the market, serving customers of different types, sizes, and needs. Let's look at our top 8. 27. What is a Service Mesh? In our previous post on API Gateways we discussed how services handle external client to service (North-South) traffic. 28. Closer Than You Think: Bridging the DevOps-Security Gap The advantages of moving security into the DevOps lifecycle early are well-studied. For example, Puppet’s 2019 State of DevOps Report details numerous ways that both security and DevOps improve when security is integrated into DevOps earlier (aka: Shift Left). 29. DevOps Principles: Culture Vs. Tooling One of the last questions asked in a DevOps interview is usually "so, what does DevOps mean to you?" I think this is a smart question, because DevOps is wildly misunderstood by the greater IT community. Some may answer that it's Continuous Integration and Releases, another may say it's having everything in Git, and the last might say that it's having tests available. All of these technical solutions do represent a key aspect of DevOps which is the tool chain, but it is the least important. 30. The Long and Winding Road to Kubernetes A look at why enterprises are slow in moving to Kubernetes. 31. DevOps at Scale through Efficient Software Distribution The article describes the software distribution in brief, what it is, use cases, and challenges involved in achieving DevOps at scale. 32. How to Use Zeet to Take Your Devops from 0 to 100 What happens when you combine an awesome idea with a driven leader? You get an exciting product, like Zeet, which automates your DevOps tasks. 33. The Importance of DevOps in Mobile App Development DevOps is growing vital in modern application development, operations, testing, and delivery. It is no longer a niche strategy. DevOps methodology is embraced by a quarter of leading names in the Forbes Global 2000 list. The mobile application connected with DevOps bridges the gap between development and operations. It emphasises over communication and collaboration, constant integration, QA, and delivery with automatic deployment. The developing, testing, releasing, and upgrading of mobile apps can occur swiftly and more certainly. 34. DevOps vs SDLC: Optimizing the Software Development Cycle If you're using a traditional software development life cycle (SDLC) you may have questions about where DevOps fits in. 35. Avoiding In-House Development Tools Now May Pay Off Later Software engineers are builders - so it’s only natural for them to want to build in-house proprietary software tools, customization and integrations. But that instinct can often be problematic for businesses. Here are some reasons you should think twice before letting your engineers build their own internal software: 36. Why ML in Production is (still) Broken and Ways we Can Fix it Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines. 37. Why Doesn't Your CI Pipeline Have Security Bug Testing Software Engineering has Changed with CI/CD 38. A Real Life Example of CI/CD Pipelines in Action CI/CD Pipeline – Case Study 39. What We Learned By Dockerizing Our Applications A universal truth we’ve experienced as both an IT software vendor and as an application delivery team is that application owners are constantly trying to deliver better software faster. 40. The State of the Continuous Delivery Developer Ecosystem Continuous delivery (CD) is a software development practice that enables developers to release small but frequent software updates reliably and safely. Closely 41. Serverless Computing Benefits for DevOps: A BTS You Should Not Miss! Serverless fits perfectly with DevOps and provides the edge over your competitors. 42. The Next 11 Things You Should Do For CI/CD Pipeline Optimization Many engineering teams are making the switch to a DevOps culture. This has a lot to do with the way the software development cycle has changed over the years. It used to take months to implement a new feature, get it through testing, fix any issues, and finally get it to production. 43. 10 Great Ways to Evaluate your Minimum Viable Product (MVP) Do you know the main purpose of building Minimum Viable Product or MVP? The answer is to validate the product that you will be launching in the market. 44. The Cloud-Native & DevOps Approach in Regulated Industries The DevOps practices are helping companies build software that will function properly in a more automated world & this is what building cloud-native software is all about. While the cloud-native approach is becoming more critical and making life easier for everyone involved in software development, there are still industries that are laggards and kind of scared to move out of their legacy practices. Yes, we are talking about the highly regulated industries like Healthcare, Financial corporations, government agencies, etc. When it comes to adopting new technologies of building software, these industries have always remained slow because of the limitations they carry with them. But recently we have seen so many traditional banks and hospitals moving towards DevOps and cloud-native practices. 45. Here's How We Train Our DevOps Skills Internally Our team started to apply DevOps practices long before we registered Mad Devs as a company. Specifically, I ran into this approach more than 10 years ago in a different company where I worked as a System Administrator. 46. All You Need to Know About Continuous Integration, GitHub Actions, & Sonar Cloud When it is done badly, the CI process can turn this amazing tool into a nightmare. CI should make our lives easier, not the other way around. 47. How to Extend your DevOps Strategy For Success in the Cloud? DevOps and Cloud computing are joined at the hip, now that fact is well appreciated by the organizations that engaged in SaaS cloud and developed applications in the Cloud. During the COVID crisis period, most of the organizations have started using cloud computing services and implementing a cloud-first strategy to establish their remote operations. Similarly, the extended DevOps strategy will make the development process more agile with automated test cases. 48. Simple Yet Effective Advice To Handle Large Releases with DevOps Effortlessly When you start working on large scale enterprise systems, handling releases can become complex. You'll have to think about your front-end, microservices, third-party services, and other services. Making sure these things get deployed in the right order and pass integration tests can be tricky once you start working with asynchronous tasks. 49. Equivalence Class Partitioning And Boundary Value Analysis in Black Box Testing 1. What is black box testing 50. DevOps Brings out the Best in Developers DevOps brings the entire application development life cycle to a whole new level. 51. What I Learned About DevOps in 2019 It was a big year for DevOps adoption. Most of the organizations than ever before are ditching their leadership philosophies, old methodologies, and legacy processes in favor of DevOps to realize speed and agility in today’s constantly evolving technology landscape. 52. GraphQL Is Not a Silver Bullet GraphQL is not a configuration language, also not a transformation language, a replacement for SQL, and definitely not Terraform. 53. DevOps: I Measure The Metrics A Bit Differently. Here's Why The four key DevOps metrics are an exciting set of measurements. They’re getting more and more relevant since the book Accelerate has been published. I firmly believe they’re essential for engineering teams seeking effectiveness and efficiency. 54. How Developers Can Use DevOps - Introduction and Version Control Improving our DevOps skill can help us become better developers, teammates and managers. Learn DevOps principles and a different perspective on git. 55. Job Cycle and on Demand Tools of Devops Programming Engineer What are the roles and responsibilities of DevOps engineer. 56. Chaos Engineering: What You Don't Know With cloud native applications, there's always a chance that something could interrupt your services. Maybe a wire gets unplugged and that brings down your server or one of your services loses network connections that you depend on. 57. Measuring DevOps Metrics: A How-To Guide DevOps is supposed to help streamline the process of taking code changes and getting them to production for users to enjoy. But what exactly does it mean for the process to be "streamlined"? One way to answer this is to start measuring metrics. 58. Deploy on Fridays, or Don't. There seems to be a debate that has gone on for quite some time now on the Twitters about whether or not you should do Friday deploys, and whether there should be Friday moratoriums, etc.  There are a lot of accusations being thrown around about fear, testing, time to recover, and the like.  To be very clear, I am not a big fan of Friday deploys.   That opinion is not based on merely how I feel about deploying on Friday, but also based on the science of it, as well as my learned experience. 59. The DevOps Mindset: A Step-by-Step Plan to Implement DevOps Discover what DevOps exactly is, five DevOps best practices, essential DevOps benefits like faster software releases, and nine key steps to implement DevOps. 60. Containers 101: Kube Explained Part 2 In our previous post, Kube Explained: Part 1, I described how the introduction of the cloud resulted in CI/CD, Microservices, and a massive amount of pressure to standardize backend infrastructure tooling. 61. Cloud-Native DevOps: A Basic Primer DevOps, the method of automating processes between development and operations, is one of the essential factors of successfully implementing a cloud-native approach. 62. Why DevOps is Important for IT Businesses Know everything about the different phases of devops lifecycle. This will enable us to understand its value to IT businesses. 63. What is DevOps and How Does it Influence the Digital Transformation of Companies? When we talk about DevOps we mean the set of process automation practices in the software and IT departments. DevOps is a movement, or work philosophy, also defined as a culture of shared responsibility or systematic thinking, whose ultimate objective is none other than to work based on collaboration between members of the software development and IT / operations teams to achieve better and more agile results. 64. Top 12 DevOps Tools for your DevOps Implementation Plan DevOps is a software development and delivery process that helps in emphasizing communication along with cross-functional collaboration between product management, software development, and operations professionals. 65. How to Make a DevOps Strategy DevOps is a deployment process that helps automate a lot of processes that used to take a team hours to days to get through. When you are trying to get that process going, you need to know that everyone is on-board or else it won't work. Switching from Waterfall deployments to DevOps is a huge organizational change. 66. Top 15 Basic DevOps Interview Questions: 2021 Edition If you are looking for DevOps Interview Questions and answers, you are on the right page and below are some Real-time DevOps Interview Questions common in 2020. Go through all of them to crack your next interview. 67. Kubernetes vs. Docker Swarm Compared: How to Pick One Over the Other Containerisation is growing and evolving rapidly, and when it comes to container orchestration, the two names that come to mind are Kubernetes and Docker Swarm. Thank you for checking out the 67 most read stories about Devops Principles on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

67 Stories To Learn About Devops Principles

This is the first post in our series, “Getting APIs on the Blockchain”. There is a famous David Foster Wallace commencement speech titled “This is Water” where he offers a little parable of fish discussing water and briefly explicates the title with: “The most obvious important realities are often the ones that are hardest to see and talk about.” In the first blog post of this series, we will be talking about APIs.¹ APIs are everywhere — they permeate our digital world. The concept of an API is so inescapable in computer software that it’s ironically difficult to appropriately define it. To further this difficulty, the term (API) has superseded its original meaning on quite a few occasions. Application Programming Interface What is an API? (Or: a very brief history of APIs) One could argue that the concept of an API was independently discovered several times, its early history being intrinsically linked to the instantly ubiquitous concept of a . That is, it’s difficult to discuss APIs separately from the (very fundamental) concepts of and in computer science. subroutine library modular programming abstraction This idea of a subroutine library was first formalized in 1948 by Herman Goldstine and John von Neumann in their widely-circulated, multi-volume treatise : Planning and Coding of Problems for an Electronic Computing Instrument² The importance of being able to do this [permit ] is very great. It is likely to have a decisive influence on the ease and the efficiency with which a computing automat of the type that we contemplate will be operable. This possibility should, more than anything else, remove a bottleneck at the preparing, setting up, and coding of problems … we envisage that properly organized automatic, high speed establishment will include an extensive collection of such subroutines… I.e. a “library” of records … in memory. the use of a coded sequence of a problem as a single entity, when that problem occurs as part of a more complicated one Essentially, Goldstine and von Neumann argued that most programs would have to utilize existing subroutine libraries in order for computers to grow to the capacity envisaged at the time (and would otherwise, due to errors and other issues, “be quite dangerous”). Subroutines were proposed as an essential method for speeding up development and decreasing errors by way of decreasing the amount of new code to be written. The treatise, unfortunately, didn’t offer much in the way of implementation specifics, instead opting for more of a theoretical and high-level description of subroutine construction and use. In a well-known presentation delivered by Joshua Bloch entitled “A Brief, Opinionated History of the API,” Bloch bluntly puts it: “they were peddling vapourware”.³ Goldstine and von Neumann’s preparatory routine … was a surprisingly pedestrian piece of work. It would have required extensive operator intervention and it is difficult to imagine that it would have ever been a satisfactory procedure in practice. — from Theory to Practice: The Invention of Programming, 1947–51 That is why, in that same presentation, Bloch opines that Maurice Wilkes, David Wheeler, and Stanley Gill are the true creators/discoverers of the API. Or — more accurately — the proto-API, given that the term was never actually used by them. The researchers built the EDSAC (an early British computer) and later authored Preparation of Programs for Electronic Digital Computers in 1951, in which subroutine libraries were effectively introduced.⁴ A few months after writing the initial orders and upon completing his first “real program”, Wilkes would write: By June 1949 … I was trying to get working my first non-trivial program … the realization came over me with full force that a good part of the remainder of my life was going to be spent in finding errors in my own programs. people had begun to realize that it was not so easy to get programs right — from Memoirs of a Computer Pioneer, MIT Press, 1985, p. 145 Wilkes’ fear of errors in his own programs largely inspired his development of subroutines as an organizing principle. The first time the term Application Programming Interface (API) was actually used was in 1968, in a paper⁵ about designing remote computer graphics libraries. In the abstract, the authors state: “it has been adequately demonstrated that computer graphics systems need not require the dedication of a large scale computer for their operation. Computer graphics has followed the trends of computing in general, where … has become a key phrase”. That is, the authors note that (by the late 1960s) it became clear to computer scientists that computer graphics systems . (The authors address how to properly interface with such systems in their paper.) remote access encapsulated and modular made more sense When searching for “Application Programming Interface” in Google Scholar for the years 1970–1995, we find the term used in numerous contexts (far beyond a simple wrapper for a subroutine): APIs for interacting with external systems (e.g. storage devices) APIs for accessing network functions nd, an extensive literature on APIs for databases By the early 1990s, APIs quickly evolved to very generally denote the communication layer between software abstractions (local or remote). Indeed, one definition from 1990 states an API is “a set of services available to a programmer for performing certain tasks”.⁶ Another from 1993 states an API is “a term for any language and format used by one computer program to help it communicate with another program”.⁷ The notion of an API expanded its meaning yet again with the dawn of web APIs. As opposed to what we just previously discussed—APIs that can be roughly categorized as and needing to be hardware/software compatible with whichever system they are used by — APIs are interfaces that connect publicly exposed endpoints of a network, typically via a predefined message-passing system and most commonly accessed using the HTTP protocol. library APIs web In his thesis (in which Representational State Transfer⁸ is introduced), Roy Fielding distinguishes between APIs and : library-based network-based APIs The design of the Web had to shift from the development of a reference protocol library to the development of a network-based API, extending the desired semantics of the Web across multiple platforms and implementations. Basically, . It didn’t matter what kind of machine the service operated on or which language(s) it was implemented in, it now only mattered that it’s services were accessible via the web (most commonly by means of an HTTP-based web server). Today, a web API is the most common connotation of “API”. network-based APIs offered a further abstraction from library-based APIs, because the implementation of the interfaced service was further abstracted and black-boxed from the interfacing user The earliest uses of web APIs were for sales and commerce management.⁹ SalesForce launched the first web API in 2000 under the umbrella of arguably the first SaaS product. Later in the same year, eBay launched the eBay API in order to “openly provide the tools that developers need to create applications based on eBay technology”¹⁰ by way of standardizing how applications would interface with eBay (since developers were already hacking away at eBay’s website and using the data in their own applications). In 2003, del.icio.us — an early social media site — offered users a simple API for sharing and tagging their internet bookmarks. Users had read/write access to bookmarks on del.icio.us via REST-like urls (e.g. http://del.icio.us/tag/[tag name]). In 2004, Flickr launched their API which allowed users/developers to, among other things: upload images, manage their “favourites” lists, query for users, and search photos. The launch of the API was motivated by the fact that Flickr couldn’t keep up with demands for their services and “established the API as a self-service way to deal with business development” which ultimately resulted in “Flickr quickly becom[ing] the image platform of choice for the early blogging and social media movement by allowing users to easily embed their Flickr photos into their blogs and social network streams”.¹² The launch of the Flickr API was the catalyst for “BizDev2.0”. … a small startup … wanted an introduction, but I said we (that is, Flickr) probably didn’t have the space on our schedules to engage with them in any meaningful way, but that they should feel free to apply for a Commercial API key and build something off the API. “BizDev 2.0” I call it. — Caterina Fake, co-founder of Flickr In 2006, Facebook, Twitter, and Google followed suit, launching their own APIs in hopes of encouraging BizDev2.0 on their respective platforms. And then came cloud computing … Amazon S3 is storage for the Internet. It’s designed to make web-scale computing easier for developers … It gives any developer access to the same highly scalable, reliable, fast, inexpensive data storage infrastructure that Amazon uses to run its own global network of web sites. The service aims to maximize benefits of scale and to pass those benefits on to developers. — from the AWS press release (March 14, 2006) The capability of web APIs did a quantum leap with the introduction of Amazon Web Services (AWS). AWS and its three initial offerings (S3, SQS, and EC2)¹² launched in mid 2006. This was the dawn of cloud computing. Amazon Web Services succeeded in abstracting away much of the day-to-day work of software engineers, making essential and complex services (like computing, storage, networking, and deployment) available via simple web API calls (albeit for a fee). With AWS, a developer could set up and deploy a server (for example) in minutes, something that would have previously taken days or weeks. Cloud computing quickly revolutionized the way developers build applications on the web. Where are we now? The role of software engineering has changed quite a bit, and coding isn’t even a huge part of it anymore … You know how in the old days people used to write in assembly code? And then we abstracted upon that, so more people started writing C++? And then people started abstracting upon that, and writing Java. And now we have Python, and we’ve abstracted upon that too… Most top apps [these days] support so much functionality, that it’s pretty much beyond the knowledge of a single person to handle all of those anymore. Now programmers are gluing pieces together through API endpoints and frameworks. — Ex-Facebook/Ex-Google tech lead and famous YouTuber, TechLead¹³ Nowadays we take for granted that software applications can and do communicate with each other with relative ease via each other’s APIs. Developers can now build complex applications at a rate never before seen. Integrating existing services to their applications through APIs has allowed developers to build increasingly complex and capable applications, which has led to the rise of giant Web services and mobile applications. Because of this, APIs are often referred to as the glue that holds the digital world together. Providing in-demand APIs has become quite the lucrative business, by way of businesses monetizing their data and services. As a result**, the concept of an API has transcended its meaning yet again.** The term no longer refers to the technical implementation of an interface, but to a full-fledged product that often includes the service it wraps.¹⁵ Global enterprises that provide APIs generate 25% of their organizational revenue from APIs on average.¹⁶ Companies such as Salesforce, Expedia, and eBay have reported to generate the majority of their revenue through APIs¹⁷, and even entrenched industries such as banking are expected to be disrupted by this movement.¹⁸ We are now at the cusp of fully API-centric business models.¹⁹ As APIs continue to encapsulate increasingly higher and higher abstractions of software services, the meaning of “API” will continue to evolve. What’s to come with the rest of this blog series? You might be wondering at this point when are we to address topics suggested by the second half of our series title (“… on the Blockchain”). Well, there is a very critical problem we introduce in our next blog post called “The API Connectivity Problem” which will take the remainder of this series to fully explicate and map out a solution to. Briefly, existing APIs are not natively compatible with decentralized applications — a blockchain network is essentially walled-off from communicating with these systems and protocols that permeate the rest of our digital world. How do we bring these interfaces onto the blockchain? How do we get blockchain applications to safely interact with the richness offered by existing APIs? Stay tuned for the rest of our series! Footnotes
[1]: Of course, Wallace’s speech was tender and moving whereas we’ll just be discussing APIs (for now), so please excuse the abrupt change in tone…
[2]: Goldstine, Herman Heine, John Von Neumann, and John Von Neumann. “Planning and coding of problems for an electronic computing instrument.” (1947).
[3]: [4]: Wheeler, Maurice V., and Stanley Gill. The preparation of programs for an electronic digital computer: with special reference to the EDSAC and the use of a library of subroutines. Addison-Wesley, 1951.
[5]: Cotton, Ira W., and Frank S. Greatorex Jr. “Data structures and techniques for remote computer graphics.” Proceedings of the December 9–11, 1968, fall joint computer conference, part I. 1968.
[6]: Malamud, Carl. Analyzing Novell Networks. John Wiley & Sons, Inc., 1991.
[7]: Bardige, Stephen I. “A Glossary of Computer Terms and Definitions.” Judges J. 32 (1993): 72.
[8]: REST is a very common software architecture style that defines a set of constraints to be used for creating web services.
[9]: [10]: [11]: [11]: [12]: S3, SQS, and EC2 are Amazon’s storage, queue, and compute services offered remotely via API (also known as “cloud computing services”).
[13]: [14]: MuleSoft, “Connectivity benchmark report,” 2020.
[15]: Collins, G., and D. Sisk. “API economy: From systems to business services.” TechTrends (2015).
[16]: MuleSoft, “Connectivity benchmark report,” 2019.
[17]: Iyer, Bala, and Mohan Subramaniam. “The strategic value of apis.” Harvard Business Review 1.7 (2015): 2015.
[18]: R. Narain, A. Merrill, and E. Lesser, “Evolution of the API economy: Adopting new business models to drive future innovation,” IBM Institute for Business Value, 2016.
[19]: Capgemini, Efma. “World FinTech Report 2019.” (2019). https://www.infoq.com/presentations/history-api/ https://history.apievangelist.com/ https://www.ecommercetimes.com/story/5403.html https://history.apievangelist.com/#Social https://history.apievangelist.com/#Flickr https://youtu.be/yzhnRt6ZDKM Previously published . here

What is the Genuine Value of APIs

Technology is fundamentally, and the attributes necessary to build a successful business. reshaping the operating-model of financial institutions AI is weakening various components of incumbent financial institutions, thereby creating an opportunity for an entirely focused on the scale and sophistication of product, tech & data much more than the scale or complexity of capital. new operating-models and category-dynamics Unlike past ‘AI Springs’, the science and practice of AI is poised to continue an unprecedented multi-decade run of progress. A clear vision of the future financial landscape is critical for good governance and strategic decisions. Key trends in AI-driven Fintech for next 10 years: AI systems will eventually underwrite credit and insurance across the world. Future product experiences will be centered around AI, which automates most of the customers’ financial lives and improves their financial outcomes. AI capable of real-time analysis and decisions (e.g. credit adjudication) will trigger the “just-in-time” lending paradigm. Customers will get more for their money as insurance policies will become cheaper and coverage will be more complete. Analysts, lenders, underwriters, and money managers, — any job that involves crunching numbers to estimate a return — is at risk. 1) Death of manual underwriting and the dawn of ‘just-in-time’ lending paradigm. Both AI and blockchain involve technical complexity and the joint use of the two technologies might alter the tech and business paradigm significantly. Blockchain offers a source of immutable data that does not require centralized verification, which could be critical for identity management. AI will enable increasingly complex and automated smart contracts to be executed, allowing more blockchain use-cases to enter the mainstream. Some of the applications would be: 2) AI + Blockchain: Deus ex Machina- The ‘ultimate’ future. AI Marketplaces, Crowdsourced Predictive Models for Hedge Funds, Collective AI Investment Management Platforms. Risk and Compliance functions suffer from massive data load and exhaustive regulatory requirements. Using NLP to understand fintech regulation by local governments can help create context-sensitive policy tests (an unambiguous set of can-do and cannot-do lists that any fintech would have to follow in the jurisdiction. Very similar to how security policies are applied to AWS IAM users). Network analytics helps determine connections between individuals to evaluate risky parties and networks, leading to a reduction in financial crime as increased transparency and liquidity makes for easier anomaly detection. 3) Reduced Financial Crime, NLP and Compliance Automation: “AI will become a standard feature in applications, not a novelty” As payment platforms become ubiquitous, interoperability will be the norm. With increased payment volumes, manual processes will be automated. Intelligent systems will perform the onboarding and know-your-customer procedures, run payment backends, continuously scan millions of transactions for patterns of fraud and future industry trends as well as sales and marketing opportunities. In the future, when customers will interact with payment systems, in the first instance they will almost always do so through AI. 4) Payments will lose “intermediaries”: A true BaaS platform — managed service that lets developers and data scientists build and run superior machine learning models in production, and enabling them to build their own financial products, together with a fintech cloud (similar to ML cloud) would be the next step in infrastructure evolution. It lends superiority over traditional ML cloud because of scale and functionality. 5) True emergence of Fintech Cloud and Infrastructure (BaaS/IaaS): “Technology goes beyond mere tool making; it is a process of creating ever more powerful technology using the tools from the previous round of innovation.” –Ray Kurzweil DIGITAL DISRUPTIONS SINCE THE ADVENT OF THE PERSONAL COMPUTER AI is creating new differentiators, driven by access to unique datasets and virtuous cycles of data. Targeted towards , — intelligent diagnosis and technology to help the user cultivate stronger savings, deposits, pensions, dividend-income and long-term investment habits will be an important play. Example — A 6) Augmented financial wellness and security in an AI-driven paradigm. long term financial wellness and immediate-term financial security recommender engine that converts maximum user input into a customized and optimized full-stack investment plan. Voice is the future of Search, Commerce, and Payments. As becomes default UI, there is a huge opportunity for voice-first fintech companies solving for a variety of use cases. Example: 7) Platform shift and the ubiquity of Voice-First Fintechs: voice On-demand micro-credit — “Alexa, can I borrow $300 from A?” Transactions — “NEO, send George $200 from my DBS account.” Investments — “Alexa, buy $100 of Bitcoin for me.” We’re entering the age of intelligent applications, which are purpose-built and informed by contextual signals like location, hardware sensors, previous usage, and predictive computation. They respond to us when we need them to and represent a new way of interacting with our devices. Human-Voice will be the primary interface. “Voice may be the foundation to the whole thing” AI will personalize payroll, benefits, salary-linked offerings. Everyone manages their paycheck differently and the ‘straitjacketed’ options by banks and employers do not solve everyone’s problem. With AI, however, a deeper analysis of transaction history leads to customization and contextualization. As past methods of differentiation erode, AI presents an opportunity for institutions to escape a “race to the bottom” in price competition by introducing new ways to distinguish themselves from customers. 8) AI and the new battlefield for customer loyalty. While AI is being used to assist regulations and compliances, there is a fear that it holds back fintech. Regulations governing the privacy and portability of data will shape the relative ability of financial and non-financial institutions to deploy AI, thus becoming as important as traditional regulations to the competitive positioning of firms. Regulators will need to cope with the increasing complexity of data management, and consider their role in protecting consumers while facilitating innovation. 9) Increasing complexity, governance, and power of data regulators. More ML-related compute is going to happen on the edge How responsibly one spends the money will become a stronger indicator of creditworthiness than all bureau data score. Reason — bureau data changes when the user commits a mistake with their loan utilization and repayments. Spending-to-income indicator data can pre-empt potential default rates and therefore remove the need of having a credit score. This also democratizes credit as not everyone may have a credit score, but everyone has a spending habit. 10) Spending-to-income-habits indicator will overtake credit score: after the fact Independent Robo-advisers can disintermediate asset managers from the investment process, reducing the cost of managing portfolios for clients. At the composition level, an AI will perform significantly better sans human biases. There will be newer classes of investments that will have divergent goals like environment, philanthropy, etc with so-called “impact investments”. The demarcation between spending, borrowing and wealth management will break down as one service-based offering manages the flow of customers’ money across multiple accounts, leading to the creation of new connections between capital and assets. 11) Death of deposits, and growth of newer asset classes, and investment vehicles: Funds run by computers that follow rules set by humans account for 35% of America’s stock market, 60% of institutional equity assets and 60% of trading activity. The total value of American public equities is $31tn… computer-managed funds run 35% of this… human managers manage just 24%. : Collaborative solutions built on shared datasets will radically increase the accuracy, timeliness, and performance of non-competitive functions, creating mutual efficiencies in operations and improving the safety of the financial system. This is a way to escape the “race to the bottom” market paradigm. 12) Collaborative AI and collective solutions for shared problems Sundar Pichai “The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first.” - It is important to understand that AI does not exist in a vacuum — its capabilities will be intertwined with the development of all other technological innovations. Emerging technologies are mutually reinforcing, and the advances in any one technology will increase the capabilities of all other technologies that interact with it. Change is coming and it’s coming fast and it will only accelerate. Some other posts by Gaurav: Autonomous-Finance FinTech Wave-2 isn’t Digital, It’s Hybrid. Lessons from Wave-1 and What’s Next? VOICE is the new Platform and the Future of Search, Commerce, and Payments Credit is older than Money (and Fintech) Blood and Sand: The Moment-of -Truth for Mobile Wallets in Asia ChatB ots, API, Voice, Zero UI, and the future of Mobile Apps. h Artificial Intelligence (AI) and FinTec Wal-Mart Moment for the banks Of Suits, Hoodies, and Ripped Jeans- Transcending the “Expectation Chasm” in Banking

Key Trends in AI-Driven Fintech: The New Paradigm

I Compiled a List of Tech’s “Next Big Things” So You Wouldn’t Have to

Excerpts are taken from Report by . Deloitte The wide world of has spewed up another one of its trademark, industry-disrupting creations: the . And everyone’s looking for the right way of blockchain implementation. In the past few years, the focus has shifted from , organizations, and chains, and the blockchain has been primarily responsible for this ginormous shift. technology Blockchain centralized to decentralized networks Key organizations and individuals within them have been exploring the possible use-cases of blockchain technology and how blockchain-based products can transform the way things work. As time has passed, the need for decision-makers, executives and high ranking officials to explore the applications blockchain has kept on increasing. Also Read: Blockchain for Food Safety The commercial and organizational impact of blockchain can potentially be one of a kind. The blockchain implementation can prove out to be something that helps a company stand out from the rest. With so much noise, decision-makers who have just started exploring the use cases of blockchain technology are often misleading with the hype around it. In our experience with the various blockchain projects with governments, enterprises as well as early-stage companies: Slapping the technology and it’s name at any random problem won’t solve it. Doing in-depth research and performing comprehensive experimentation meshed-up with rigorous business study will. So, in this blog, we decided to explain the roadmap on how to evaluate an use cases of blockchain to solve complex problems. From blockchain technology and it’s use case analysis to creating a for blockchain based products, everything will be discussed in this post. foolproof Proof-of-Concept In simple words, the blockchain is a secure and time stamped ledger, copies of which are available to all participants on the network. Understanding basics about the blockchain are instrumental before going ahead to discuss stuff like use case of blockchain or implementing blockchain. is extensively used to secure the data, and new transactions are linked to previous ones. Hacking into such a network is pretty much impossible, due to the fact that one would have to take over 51% of the network to take control. Our experience with creating successful has taught us the importance of cryptography. The blockchain is an exciting business and career opportunity in itself, with more and more companies shifting their focus onto how to exploit this technology to their benefit. Cryptography cryptocurrency exchange development Let us take a look at why the blockchain should mean so much to executives and decision makers. Why blockchain matters Get in touch with ! Sodio By 2025, it is expected that blockchain will result in USD 176 billion added business value, with the number creeping up to USD 3.1 trillion by 2030. The government of UAE too has issued a mandate which calls for all the by 2020. These magnanimous numbers alone are enough for some of the biggest leaders and entrepreneurs of the world to sit up and take notice. government departments and public services product to be on blockchain This tells how serious key decision makers are about blockchain implementation and that they’re actually exploring use cases of the blockchain. At the very least, blockchain promises to make the backend operations of companies cheaper, secure and easier to manage, with this potential use stretching out to perhaps replacing companies altogether. When combined with other technologies, the implementation of blockchain could seamlessly replace whole organizations. The Internet of Things, AI and blockchain when brought together could give rise to something that could totally eliminate, for instance, Uber or AirBnB and other platforms based on traditional technologies and processes. This is because of the nature of the blockchain to shift focus from an entity to software. Blockchain implementation in various processes would ensure that it is sped up and made more efficient. Many companies could one day see their demise right in front of their eyes, thanks to the blockchain. peer to peer networks that are used to create or transfer assets can be easily created on the blockchain. We realised the potential of these P2P networks while working on and solutions for our clients. These would not be run by any centralized parties and could be extensively used in the aforementioned point (replacing companies altogether). It may also make illiquid assets tradable or liquid, with new services and opportunities popping up in these areas. That is the sole reason as to why the banking and financial sector is looking forward to implementing blockchain for various processes that may become more efficient. When it comes to money or assets, Decentralized exchange development A step-by-step guide to the implementation of blockchain Get in touch with ! Sodio For executives and high ranking officials, it only makes sense that they keep afloat of all that there is to know about the blockchain, and how can it benefit their organization. Learning about use cases, proof of concepts, and scaling opportunities is all it takes to implement the blockchain effectively to benefit an organization. Let us go through a step-by-step overview on how to perform blockchain implementation in any commercial organization. 1. Business Use case analysis A typical use case analysis for a blockchain project involves a few major steps that we are about to discuss here. A quick look at these steps should be able to give you an overview about how to start the blockchain implementation in your organization. a. Learning where the blockchain makes sense Blockchain is not the be-all and end-all of problems. There are typical use cases where it , such as in inventory management and supply chain management. It cannot be put to use in every section or area of an organization’s work. Weighing the positives and negatives of incorporating blockchain into a certain area or functionality must be done before the actual blockchain implementation takes place. makes sense to use the blockchain b. Inventory all the use cases Once all possible use cases for blockchain have been identified, it is important to address them into an inventory, each of which address a certain business challenge. For instance, when more than one use cases solve similar problems, it would make more sense to group them into a single category, which would make further implementation of blockchain easier and more streamlined. c. Assessment For each use case, a thorough analysis and assessment needs to be carried out in order to identify the utility it will bring to the company. A blockchain based inventory solution isn’t really worth implementing if it does not increase the efficiency of inventory management and speed of item tracking, is it? Therefore, it becomes crucial to assess and rate all use cases based on the utility that they provide the organization with. Only then can further implementation of blockchain technology be thought of. d. Prioritize use cases Even after the assessment phase, it would be difficult to implement each and every use case inventoried in the above steps. Identifying 1–3 use cases, with the highest value offered to the organization, ranked on the basis of ease of blockchain implementation would be an ideal way to proceed. As a decision maker or higher ranked executive, you would have an overview of the type of resources that the company possesses. This would help you in prioritising the blockchain use cases that you want to implement and in which order. 2. Building a proof of concept Get in touch with ! Sodio Developing functional and technical architecture, and defining the minimum viable ecosystem are two of the most important steps of blockchain implementation that come under this step. The blockchain technology stack that is to be used will make all the difference, it could even perhaps make or break the chain. Although, the use case of blockchain and the industry in which it’ll happen would be a governing factor too. The architecture of the system to be used should be the priority here, i.e. choosing the best technical and functional architecture to suit the system’s and the organization’s needs. What kind of blockchain you want to utilize also needs to be decided during this process. Once all is said and done, get some developers to build and test the proof of concept for your use case. This would be a monumental exercise for your process of blockchain implementation. Either way, careful building and testing of the proof of concept is of vital importance, since this is the very concept that will give the blockchain network its unique nature. After doing the testing, a retrospective phase is normally carried out. Here, the entire process is reviewed and checked for any errors in judgement that might have crept in while the conception of blockchain use case and application. Implementation of blockchain is a long drawn out process, therefore, to ensure the integrity and correctness of the same, a retrospective phase is of prime importance. 3. Scaling Implementing a blockchain use case at a large scale is a totally different devil to deal with. Industrialization of the technology stack, the expansion of the Minimum Viable Ecosystem, creation and joining of consortiums, and many other steps highlight the process of scaling a blockchain use case. Development of governance systems and operating models is perhaps the first and foremost step to be performed in the scaling process. Not only this, this should be weighed as a crucial factor while evaluating the various use cases of blockchain. This is closely followed by testing and piloting the blockchain solutions in a real time production environment, and integration of the new tech with the legacy systems. Follow-up steps would include the industrialization of the technology stack being used, engagement of regulators for compliance if needed, and institutionalization of the operating architecture. All in all, the tri-forked process of use case analysis, proof of concept design, and scaling constitutes a standard blockchain implementation in any organization. Commercializing the blockchain: What a decision maker must know Get in touch with ! Sodio Although this part of the article warrants enough importance to have an independent piece, we are going to include a brief overview of the commercial aspects of the blockchain here itself. The truth is that organizations and firms must focus a lot of their resources on commercialization. We learned this while working on a blockchain implementation project with government organizations. Use cases of the blockchain that involve clear-cut steps to achieve commercialization are more likely to survive in this heated up competitive world. Keeping in mind that blockchain use cases do not need to be very industry specific to achieve commercial success, decision makers and executives must also focus on enterprise-ready commercial applications of the blockchain, in order to gain maximum throughput from this very intriguing technology. Blockchain implementation that solve unique value problems across the organization would be likelier to succeed, and if these use cases offer potential revenue sources in the near or long-term future, then it is all the better. How to assess the commercial potential of a blockchain use case? Executives can assess a few simple factors of each blockchain use case in order to evaluate its commercial relevance. These involve- a. Considering the achievement of the organization’s vision and objectives b. Evaluation of the implementation process, the roadmap, and how to maximize returns from these use cases c. Requirements to implement these use cases- special skills, personnel, equipment, and where to find them d. Potential for expansion and collaboration with other business partners, maybe as part of a consortium Once you have completely done the assessment of blockchain implementation in terms of commercial factors, standardization comes next. Standardization of the blockchain: imposing regulations and standards Get in touch with Sodio! With the growth of blockchain, a need for standards and regulations, at least within an organization, keeps increasing. Standardization of the technology involves certain great benefits that, if harnessed properly, could take any organization to the next level. Before talking about how to carry out the standardization process, let us look at the benefits it offers to individuals, employees and the organization as a whole. 1. Validation of the proof of concepts developed in the implementation stage 2. Sharing of enterprise solutions and collaborations with other organizations 3. Avoiding a ‘jack of all trades, master of none’ situation by focusing on one or two technologies rather than basic knowledge in all aspects Now, executives or decision makers might think: if there are no current worldwide standards for blockchain, should they wait for some to come up, or take the initiative and make standards themselves? Should the team at some XYZ organization work to define some standards and regulations themselves? The truth is, not all organizations are equipped to handle this. Some of them are straight-up not so influential in the blockchain or IT world to enforce standards formed by themselves. Internally, executives and high ranking officials may influence their organization towards standards that help achieve the organization’s mission and vision, but on a larger scale, this presents a different challenge altogether. In the near future, if some sort of standards are enforced, multiple value blockchains could even be integrated into a single chain. Solutions from different organizations may be clubbed together to form large scale, commercial products out of the blockchain. Current problems faced by this idea include the incompatibility of different blockchain protocols, for example, Ethereum and Hyperledger. It is difficult to integrate these two technologies into a single ecosystem, hence, multiple blockchain integration still has a long way to go. Where to start? How to carry out the whole process? A brief overview of the entire commercialization process for decision makers and executives has already been discussed in the previous sections. Here, we attempt to outline a step-by-step, clear-cut procedure to follow, in order to implement blockchain at both a scaled and a commercial level. a. Does your company actually need blockchain? As discussed before, blockchain is not a solution to everything. Researching and analysing whether blockchain will actually fit your ecosystem is probably the most important step when it comes to implementing this technology. It is important to understand the extent up to which blockchain can benefit your organization. b. Decide on a single use case and work towards it After you realize that a certain blockchain use case benefits your organization the most, dedicate yourselves fully to it. The path to blockchain commercialization often involves determination to stick with a certain use case that you believe has the potential to disrupt entire industries. c. Regulations and rules are made to be followed As multiple parties come together to work on the blockchain, and consortiums are formed, rules and regulations will be required at one point of time or another. It is best to have all entities know and recognize their roles and responsibilities, and follow all set standards and regulations. Clearly defined processes and rules will only aid further development of all entities involved. d. Hire quality talent to work on the blockchain As an executive, it is your responsibility to ensure that your organization has the best talent in the game working for it. Blockchain is still a new concept, therefore, a lot of the talent in the industry has not been snapped up by companies as of now. This is the time to pounce, and have some of the best minds in the sector working for you. Follow these steps to the letter, and in no time will you have a strong, commercial blockchain use case ready to do the magic for your company. Still have your doubts? Read on about some famous commercial blockchain projects that are sure to inspire you. Hyperledger A trade platform aimed at payments using blockchain, Hyperledger has fast become one of the most popular blockchain platforms out there. It runs through the IBM cloud, and is renowned for its scalability. R3 Blockchain enthusiasts must have heard of Corda, R3’s major project in the blockchain space. It is one of the first distributed ledger programs introduced to the industry. Focusing on governmental acceptance, R3 has fast become a popular choice for all blockchain institutions, especially financial. To sum it all up, as decision makers, and high ranked officials, knowing all about blockchain and its commercial implementation could reap rich rewards for organizations. Potential use cases, standardization, commercialization and other processes discussed in this article provide an overview of the entire process. If you as a decision maker are looking to implement blockchain into your organization, now is the time to act. Looking to Implement Blockchain? Let’s talk! Our process of use-case analysis and conducting an extensive business study to tailor robust and scalable blockchain based solutions has been widely recognized. Be it the security or the architectural issues, Sodio does it all in the best possible manner. Like what we say? Give us some claps! Get In touch with us: E-mail: contact@sodio.tech Twitter: Sodiotech Website: Sodio Technologies

The Ultimate Guide for Decision Makers to Blockchain Implementation

Disruptive and Transformational Technologies Technology is advancing like never before. With advancements in AI, machine learning, iOT, analytics, and so much more, it’s becoming more and more difficult for businesses to keep pace. Industries are being transformed and the world we live in is changing before our eyes. As an agency, we work with many tech clients who are in the midst of this change — racing the clock to get to market before the competition. And while many have worked tirelessly to build the best technology in their realm, they struggle with how to position and message their product or solution to their target audiences. Messaging for Disruptive and Transformational Technology is Hard require a different approach to messaging and positioning. Discussing features and benefits isn’t enough. In order to be truly disruptive, these companies need to change the perception of what is possible. Their messaging needs to articulate why potential customers should believe in their solution and how the technology will enable a different future. As such, B2B messaging needs to: Disruptive technologies Articulate that there is a problem with the status quo of today Ignite a shift in perception around about why customers should believe in a transformational way of thinking Position the product or solution in a way that both rationally and emotionally articulates the value proposition directly to key target audiences Clearly show audiences how your offering can transform their way of doing business These are challenges we often see our clients having a hard time knowing how to integrate brand-level messages that center on the “why” piece of messaging the story. Instead, they lead with the “what” and “how.” When things are so complicated and are so transformational in nature, the messaging needs to be holistic in nature and incorporate the entire story — a story centered around the customer, not the technology itself. This departure from product takes courage. Our role involves helping our clients make the complex simple. We find a way to unpack the technology and what it enables customers to do, and put it in more human terms. We make it easy for people to understand how the technology will help their business be more successful. Even the most innovative technology in the world will never see the light of day if people don’t understand know how or why to buy it. The Role of B2B Messaging To be successful today, tech companies have a lot to achieve. Selling product, influencing prospects, inspiring investors, building categories, building market share, driving revenue. Marketing teams are on the hook to deliver meaningful and differentiated messaging platforms to help support sales and ensure the brand presents a unique offering. This means B2B brands need a strong narrative and compelling messaging in place to cut through the noise. and In our work with high-growth tech companies, we’ve seen first-hand how the role of messaging can drive change. Because of this experience, it has become more important than ever to get it right. There is a growing need to differentiate complicated technology products into easy to see “value” in the form of solutions that meet target audience needs. Seems easy? Not so much. Much of our work is alongside CEOs who come from strong engineering backgrounds. They feel immense pride in the products they have worked so hard to engineer and develop. And naturally, they want messaging to lead with product. They see features and benefits as the only way forward. However, we know this isn’t the only way. My background is in selling technology. I know first-hand what sales teams need to be successful, and I see the role of messaging as critical to the sales organization’s success. I have sold technology on three continents and bring this to every client engagement by adding a unique of understanding technology, how to position it, and how to create a brand narrative and messaging — messaging that addresses the big idea of why the technology matters while demonstrating how it solves a customer’s business problems. sales-led approach That’s why, at Emotive Brand, we believe strong B2B messaging is about hitting the sweet spot between brand, sales, and marketing in a way that excites, resonates, and activates buyers. Getting it Right Getting messaging right is a real challenge. Most often, messaging is an output of a larger brand strategy. As such, it has to crystalize what you do, why you’re better, and what you can do that no one else can. It’s not easy to articulate these values to your target audiences in both rational and emotional ways that activate them to do something. It’s old news that a lot of B2B messaging — especially within the tech world — sounds the same. But there is a larger problem at hand — a problem we’ve seen happening for a while now. A lot of messaging used by B2B companies isn’t aligned with what the customers or the businesses they are trying to reach really value. What companies are missing is the articulation of something more than features and benefits: true opportunity, values, and meaning. Supporting Marketing and Sales We’ve talked again and again about the plays in supporting and driving business strategy. So when the time comes in the brand strategy process to develop messaging, it needs to align and support two major functions within a company: Marketing and Sales. role brand strategy It’s about driving two workflows within a company. Marketing needs B2B messaging to build out a go-to-market strategy and create sales enablement programs. Sales needs messaging to know how to talk to customers, position the product or solution, and close deals. If the messaging isn’t strong enough, marketing teams struggle to fill the funnel and convert leads. Sales has a hard time responding to RFPs and closing deals. In short, it’s detrimental to both teams’ success. Smart B2B messaging can drive sales and marketing forward, positioning you to better reach and connect with the businesses who matter to your success. To do so, make it: 1. Customer-centric A shift needs to happen from product-centric to customer-centric. This means increased focus on articulating the valuable, unique opportunities you can offer your target audiences. In the B2C world, it’s easy to focus on consumers. The mistake a lot of B2B companies are making today is thinking that the businesses they are trying to reach are only making rational business decisions. And while B2B does require a high level of rationale, it’s important to remember that there’s also a lot of emotions at stake for B2B buyers. Many businesses today feel that they have an important to role to play. They are mindful of cost, aware of how things are going to play together, and passionate about finding the right solutions fit to their business. 2. Research-led In our client work, the tightest and most meaningful messaging we’ve developed for clients has involved some level of research — both quantitative and qualitative. There’s a lot of value in talking to people one-on-one. It gives our team the ability, as a third party, to gain direct access to customers, prospects, and even lost deals to understand the audience’s needs first-hand. This level of empathy enables B2B messaging that can truly connect with people — with their rational needs on the right emotional dimensions. That’s why getting to the heart of their motivations, pain points, and needs is critical and can be the difference between thriving and failing. 3. Top-down Like many strategic elements, messaging has to start at the top. This means the leadership teams must be involved. Moving messaging beyond features and benefits is a process, and key leaders have to buy into a more customer-centric approach or it will never be fully embraced. Once top execs are on board, they can work to align the sales and marketing teams at the highest level. This reinforces the messaging systems you’ve built by ensuring that people are talking about your business in ways that align with it across people and platforms. The sales force is then equipped to utilize the B2B messaging in powerful, dynamic ways. They become more willing to provide feedback to marketing about what is and isn’t working. The collaboration between these two groups makes both the messaging stronger and the impact of the brand stronger. Driving ROI and sales. B2B Messaging as a Driver of Business Messaging can drive brand awareness, equity, and buying decisions by bringing your value proposition to life for your key audiences. With strong B2B messaging in place, your business can then develop a that brings all aspects of the business, brand, and vision together. powerful corporate narrative In B2B, messaging can be a vital solution in humanizing your brand and business. It can make it easier for your sales and marketing team to connect with your future clients in important ways that will fuel your business forward. The transition from solely rational to a blend of both emotional and rational can be a challenge. When you take the lead and embrace the process of identifying what really matters to your audiences beyond just the functional elements of your product, sales will take off, marketing will start to pay-off, and your business will be positioned to stand out and thrive. Emotive Brand is a San Francisco brand strategy and design agency.

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