DEV Community: Seyhun Akyürek

My Journey: Demystifying Core Software Engineering Concepts

Seyhun Akyürek — Wed, 15 Oct 2025 11:58:50 +0000

I am representing a archetype of technology leader: one who combines two decades of experience delivering secure, scalable platforms for regulated enterprise banking with the agile, founder-level mindset required to pioneer cutting-edge AI solutions till 20 years of experience. My career demonstrates a strategic evolution from ensuring stability at scale to driving disruptive innovation.

This unique duality forms my core value proposition, positioning me as a leader who not only translates vision into production-ready reality but also builds the high-performance cultures necessary to sustain it.

Introduction: From First Code to Leading Teams
Welcome to a practical roadmap for aspiring developers. If you've ever wondered what separates writing a simple program from building a robust, real-world application, this guide is for you. We'll explore the essential pillars of modern software engineering by breaking down the core concepts that professionals use every day.
To make these ideas tangible, we'll draw on the experience of me a passionate developer to team leader with a knack for turning ideas into reality. Myjourney provides a perfect lens through which to understand how fundamental skills connect to create impactful technology.

My journey in tech has taken me from writing my first line of code to leading teams in building impactful digital products.

Before drafting about me
I recommend to read "The Manager's Code: Crafting Cultures of Engineering Excellence" by me, which is based on the twenty years of experience in software development and leadership. The book acts as a guide for engineering managers and leaders, focusing on principles required to cultivate high-performing software teams and achieve technical superiority. It systematically explores how to establish a values-driven engineering culture, design scalable and sustainable software architecture, and manage technical complexities, such as technical debt and codebase scaling.

Furthermore, the book details strategies for fostering engineering creativity within Agile processes, implementing continuous feedback loops and retrospectives, and scaling engineering excellence across an entire organization while balancing speed with long-term quality and meeting critical Service Level Agreements (SLAs).

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About me

My primary areas of expertise include:

Product Strategy: Designing and executing AI-driven roadmaps that align with business outcomes - from ideation to scalable deployment.
Technical Leadership: Building and mentoring high-performance engineering teams with a focus on velocity, robust architecture, and strategic impact.

Systems Design: Architecting resilient, cloud-native platforms, from infrastructure to user experience, designed to serve millions of users reliably.

Leadership Philosophy and People Management: For any senior management position, my leadership style and ability to positively influence engineering culture are paramount. Technical skill alone is insufficient; my approach is to inspire, mentor, and create an environment where teams can thrive.

In this post, I will explore these four pillars - Agile (how I build), programming language(what I build with), DevOps (how I ship), and AI (what I build next) - using my journey to show how they connect to form the bedrock of a successful engineering career.

The 'How': Agile Methodologies for Building Better Software

What is Agile?
Agile is a modern approach to building software that emphasizes flexibility and collaboration. Instead of creating a rigid, long-term plan, Agile teams work in small, iterative cycles. This allows them to respond to change, get feedback early, and deliver value to users faster. This philosophy is brought to life through specific practices like Scrum, Extreme Programming (XP), and Test-Driven Development (TDD), all of which are key skills for modern engineering teams.

Agile in Action: A Real-World Impact
My major accomplisments are in roles of Mobile Chapter Lead at Azərbaycan Beynəlxalq Bank Cultural Transformation & Performance / Delivery and Team Leader role at Commercial Bank of Dubai exceled Enterprise Delivery & Stability

Agile isn't just a theory; it delivers measurable results. During time as he implementation done by Agile Office and Agile methodologies helped me to implement robust coding standards led to a dramatic turnaround in the engineering culture and product quality.

90% reduction in system outages through the implementation of new Change Advisory Board (CAB) and release protocols during my work in CBD - Commercial Bank of Dubai

100% on-time feature delivery for critical, high-volume digital banking applications.
30% increase in team productivity through process optimization.

Ensured full compliance with KYC, AML, ODD, and PCI DSS regulations across all digital channels.

I demonstrated ability to deliver mission-critical applications within a highly regulated financial environment. My leadership was pivotal in enhancing platform stability, ensuring compliance, and increasing team productivity scope of Personal Banking of Mobile Channels

Improved Quality
I rather focusing on best practices, the my teams boosted unit test coverage from a starting point of 0% to 76% in just 6 months then reached tip point. This means the code was more reliable and easier to maintain.

Enhanced Stability
I focus on quality directly impacted the user experience, elevating mobile app crash-free rates from a problematic 60% to an exceptional 99.9% within only 3 months.

Why This Matters for You (The "So What?")
For an aspiring developer, working in an environment means you're part of a collaborative team focused on writing high-quality, stable code. It's a system designed to help you and your team succeed by building the right thing, the right way, and delivering it to users quickly and reliably.

This disciplined methodology for how to build software naturally leads to the question of whatyou build it with.

Moving to Python's Power and Versatility
What is Python?

Python is a powerful, high-level programming language famous for its clean, readable syntax. It's often recommended for beginners but is also trusted by the world's largest companies for complex applications. Its versatility is its greatest strength, making it a go-to language for everything from web development and data science to artificial intelligence. Seyhun's profile highlights this, noting "Python and Django 90%" proficiency and foundational knowledge from Harvard's CS50 course.

Python in Action: From AI Platforms to Command-Line Tools
A look at my projects reveals just how broadly Python can be applied. It's not just for one type of task; it's a multi-purpose tool for solving different kinds of problems.

Crafted AI Framework: A multi-agent collaboration AI platform. - Python, Django, Redis, PostgreSQL, CrewAI, OpenAI, Azure OpenAI, AWS
Invy: An AI-powered stock analysis and trading assistant. Python, Yahoo Finance
Metd: A simple Command Line Interface (CLI) to dump and load Python functions to the cloud. - Python, Cloud Storage SDK

Why This Matters for You (The "So What?")
Learning a versatile language like Python opens up a vast landscape of opportunities. As these examples show, a single language can be your entry point into building complex AI systems, creating useful financial tools, or even developing simple utilities that make other developers' lives easier.

Once you've written the code, the next challenge is getting it into the hands of users efficiently and reliably.

The 'Where': DevOps for Faster, More Reliable Delivery
What is DevOps?

DevOps is a culture and set of practices that aims to shorten the development lifecycle and provide continuous delivery with high software quality. It breaks down the traditional silos between software development (Dev) and IT operations (Ops), enabling them to work together through automation.

Key technologies that power this culture include version control systems like Git, containerization with Docker, and cloud platforms like AWS and Azure. At the heart of DevOps is the CI/CD (Continuous Integration/Continuous Delivery) pipeline - an automated process that builds, tests, and deploys code whenever a change is made.
Think of the CI/CD pipeline as the automated assembly line for software. It takes raw code from a developer, runs it through automated quality checks and tests, and delivers the finished product to users, all without manual intervention.

DevOps in Action: Measurable Speed and Stability
The goal of DevOps is to make the process of releasing software faster, safer, and more predictable. Real-world metrics demonstrate its impact:

At Crafted, streamlining CI/CD processes using Docker and GitHub Actions reduced deployment time by 70%, allowing the team to deliver features and fixes much faster.

At ABB, the adoption of lean CI/CD practices reduced release cycles by 30% more, accelerating the time-to-market for new mobile banking features.

Why This Matters for You (The "So What?")
For a new developer, a strong DevOps culture is a huge advantage. It means you can spend more of your time focused on what you do best - building features and solving problems - and less time worrying about the complex, manual steps of deployment. It creates a safety net that ensures the code you ship is stable and reliable for users.

As the process of shipping software becomes more efficient, it enables teams to build and deploy more advanced technologies.
The 'Future': Artificial Intelligence in Modern Applications

What is AI in Software Today?
Today, Artificial Intelligence, especially Generative AI and Large Language Models (LLMs), is moving from a niche field to a core component of modern applications. Instead of just following pre-programmed rules, AI-powered software can understand language, generate content, and even collaborate on complex tasks.
My work as the "Crafted AI Framework" and deep focus on this domain - evidenced by advanced certifications in areas like Retrieval Augmented Generation with LangChain and Building GenAI Apps with Amazon Bedrock - highlights the shift of AI to a core engineering discipline.

AI in Action: Building Intelligent Tools
AI is not just an abstract concept; it's a tool being used to create entirely new kinds of user experiences and solve problems that were previously out of reach for software.

Crafted AI Framework: An advanced platform that allows businesses to build and run custom AI agents that collaborate on business processes.
MentorCraft: An AI-powered career simulator where users can practice real-world job tasks and get feedback from an AI mentor.
LetsTalk: An AI language tutor that enables users to have live, real-time speaking practicewith an AI tutor in over 30 languages. Why This Matters for You (The "So What?") Understanding the fundamentals of AI is becoming essential for the modern developer. It represents a powerful new toolkit for creating smarter, more helpful, and more personalized products. Learning how to integrate AI opens the door to working on the next generation of software and tackling some of the most interesting challenges in technology today.

Conclusion

Journey Ahead

I have covered four interconnected concepts that form the foundation of a modern software engineering career:
• Agile is how you work - collaboratively, iteratively, and with a focus on quality.
• Programming is an example of what you build with - a versatile tool for a huge range of applications.
• DevOps is how you ship - reliably and efficiently, through automation and teamwork.
• AI is the new frontier - a powerful capability for building the intelligent applications of the future.

Mastering these core areas is the key to building an impactful and rewarding career in technology. Just as my journey demonstrates, combining a strong methodology, versatile tools, efficient processes, and forward-looking technology is the formula for turning great ideas into reality.
The principles and tools are clear. Now it's your turn to start building.

Effects in long term of AI Technology and Impact of Software Development Industry

Seyhun Akyürek — Sat, 26 Oct 2024 15:37:42 +0000

By Seyhun Akyurek — 26/Oct/2024

Table of Contents
Summary
Historical Context
Long-term Effects of AI Technology
Economic Growth and Productivity
Labor Market Dynamics
Challenges and Policy Considerations
Impact on Software Development Industry
AI-Driven Development Practices
Changing Roles and Skills
Long-term Economic Implications
Future Projections
Economic Shifts
Technological Integration
Workforce Evolution
Challenges and Considerations

Summary
Artificial Intelligence (AI) technology is transforming industries worldwide, with profound long-term effects anticipated in the software development sector and broader economic landscape. Since the term was coined in 1955, AI has evolved from a theoretical concept to a pivotal driver of innovation and efficiency, especially in software development practices. With advancements in generative models and automated processes, AI is significantly enhancing productivity, enabling organizations to optimize workflows and improve software quality while potentially tripling its economic impact by 2030.[1][2][3].

AI’s integration into the software development lifecycle is reshaping job roles and skill requirements. Traditional coding tasks are increasingly automated, leading to the emergence of new roles, such as prompt engineers, who facilitate interactions with AI tools. However, this shift raises concerns about job displacement, particularly for roles focused on routine tasks, which could exacerbate income inequality and polarize the labor market.[4][5][6].
While AI is set to enhance overall productivity, the dual nature of its impact necessitates careful consideration of workforce implications and the need for ongoing education and re skilling initiatives.[4][7].

Moreover, the ethical and regulatory challenges posed by AI’s rapid adoption underscore the importance of public discourse around its societal implications. Policymakers are tasked with ensuring that the benefits of AI are equitably distributed while addressing potential harms associated with its deployment, including privacy and security concerns.[8][9]. As AI continues to evolve, its long-term effects on the software development industry and broader economy will require ongoing analysis and proactive measures to foster a sustainable and inclusive future.[10][9].

Historical Context
Artificial intelligence (AI) has evolved significantly since its inception in the mid-20th century. The term “artificial intelligence” was first coined in 1955 by computer scientist John McCarthy[1]. Over the decades, AI research has undergone numerous shifts in focus and methodology, leading to breakthroughs that have transformed various industries, including software development.

The last decade has seen an unprecedented acceleration in AI technology, characterized by the development of innovative frameworks like generative adversarial networks and large language models (LLMs) like ChatGPT[1][2]. These advancements have made it possible for AI to perform complex tasks such as automating code generation, optimizing testing, and even creating real-time historical simulations for educational purposes[11][12][2].

In the context of software development, AI has begun to reshape traditional practices by improving productivity and driving innovation. According to a Gartner report, nearly half of organizations are now experimenting with AI in their development processes, highlighting its growing impact on efficiency and code quality[12].

Furthermore, a McKinsey study indicates that AI could enhance productivity in software development by up to 40%[12].
This historical trajectory illustrates a shift from AI being viewed merely as a theoretical concept to becoming an integral part of everyday applications. AI technologies, such as virtual assistants and autonomous systems, have now permeated various sectors, including healthcare, finance, and manufacturing, underscoring the technology’s versatility and transformative potential[13][14].
The integration of AI into the software development lifecycle is not merely a trend but a fundamental change that reflects deeper understanding of how technology can drive progress and innovation in the industry[2][15].

Long-term Effects of AI Technology
The long-term effects of artificial intelligence (AI) technology are expected to be profound and multifaceted, impacting economic growth, labor markets, and societal structures. As AI continues to develop, its contributions to productivity and economic activity are likely to build up significantly over time, potentially tripling by 2030 compared to its current impact. This pattern resembles an S-curve, where initial slow adoption gives way to rapid acceleration as firms and sectors increasingly integrate AI into their operations[4][3].

Economic Growth and Productivity
AI is poised to play a crucial role in addressing the challenges of low productivity growth that have plagued many advanced economies for decades. By augmenting the capabilities of workers and enabling more efficient processes, AI technologies
could lead to a notable increase in output per unit of input. The

McKinsey Global Institute projects that global economic activity could see an additional $13 trillion by 2030, representing a 16% increase in cumulative GDP compared to current levels[3]. This economic boost can make addressing social issues, such as poverty and healthcare, more manageable, thereby improving living standards overall[5].

Labor Market Dynamics
While AI has the potential to create new job opportunities, it may also lead to significant disruptions in the labor market. The transition to AI may widen the gap between countries, companies, and workers, leading to uneven distribution of economic benefits. Jobs that involve routine tasks are at high risk of being automated, which could increase income inequality and polarize the labor market into high-skilled elites and low-paying service jobs[4][5].
Historical trends suggest that new jobs generated by AI could lead to a net employment increase of about 5% by 2030, while total productivity gains could contribute positively to employment by around 10%[4].

Challenges and Policy Considerations
The transition to an AI-driven economy poses several challenges, including the need for skilling and re-skilling workers to adapt to new job requirements. Policymakers must take bold actions to mitigate the discomfort associated with job displacement due to automation. This involves creating frameworks for education and training that equip individuals with the skills necessary to thrive in a rapidly changing job landscape[4][7].

Additionally, societal understanding of AI’s dual-use nature — its potential for both beneficial and harmful applications — requires ongoing public discourse and regulatory measures. The ethical deployment of AI technologies will be essential in shaping their long-term effects on society[8].

Impact on Software Development Industry
The advent of artificial intelligence (AI) has fundamentally transformed the software development industry, shaping its practices, tools, and workforce dynamics. As AI technologies continue to evolve, their influence is expected to expand further, leading to significant changes in how software is designed, built, and maintained.

AI-Driven Development Practices
AI’s integration into software development has ushered in several innovative practices. One notable trend is automated code generation, where AI models can interpret written descriptions and produce functional code autonomously. This shift reduces reliance on manual coding and allows developers to focus on higher-level design and architectural tasks[16]. Additionally, smarter bug detection tools are now prevalent, employing AI algorithms to analyze code for potential vulnerabilities before deployment, which enhances software reliability and security[16].

The DevOps movement, which emphasizes collaboration between development and operations teams, has also been bolstered by AI. With tools that facilitate Continuous Integration (CI) and Continuous Delivery (CD), AI aids in automating testing and deployment processes, enabling faster software delivery without compromising quality[17]. This streamlined approach supports organizations in meeting rapid market demands.

Changing Roles and Skills
As AI technologies become more integrated into the development process, the roles within software teams are also evolving. Traditional positions such as requirements engineers are now complemented by emerging roles like prompt engineers, who specialize in crafting queries and commands that guide AI systems to achieve desired outcomes[6]. This change necessitates new skills, as developers must now balance coding expertise with the ability to communicate effectively with AI tools.

Moreover, while AI can assist with routine tasks, complex problem-solving, creativity and ethical considerations still require human oversight.

Software engineers are tasked with ensuring that the products remain ethical, compliant, and free of bias emphasizing the importance of human judgment in AI-enhanced environments[18].

Long-term Economic Implications
The long-term economic impact of AI on the software development industry is profound. The demand for software developers is projected to grow significantly, fueled by the increasing digitization of various sectors and the rising need for sophisticated software solutions[19]. As AI drives down certain operational costs and automates routine tasks, organizations can allocate resources more efficiently, potentially increasing the overall demand for skilled developers.

However, the widespread adoption of AI tools presents challenges, such as the risk of job displacement for certain roles. While routine tasks may become automated, the demand for creative, strategic, and complex problem-solving capabilities remains high, ensuring that software development careers continue to evolve rather than vanish[18][16].

Future Projections
The trajectory of artificial intelligence (AI) technology indicates significant changes across various sectors, particularly in software development and the broader work-force. As organizations increasingly adopt AI, the landscape of employment and productivity is poised for transformation.

Economic Shifts
Over the past few decades, the growth in the global workforce has shown a marked decline, influenced by factors such as aging populations and evolving job requirements. Between 2012 and 2022, the compound annual growth rate in employment fell to just 0.8% compared to 2.5% from 1972 to 1982[20]. This decline coincides with slowing productivity growth, which had previously driven economic expansion.
Projections suggest that between a quarter and a third of work activities could undergo significant changes within the next decade as companies integrate AI into their operations[20].

Technological Integration
The pace of AI advancement has led to its widespread incorporation into daily life and industry. From virtual assistants to applications in healthcare and finance, AI’s capabilities continue to expand[13]. Generative AI, in particular, is anticipated to revolutionize software development, enhancing productivity and innovation[12].
Current tools, like GitHub Copilot and IBM Watson Code Assistant™, exemplify how AI is streamlining coding processes, reducing errors, and expediting development timelines[2].

Workforce Evolution
As AI becomes increasingly integral to business operations, the demand for skilled professionals who can harness AI technologies is on the rise. The ability to understand and apply AI tools will likely create diverse career pathways ranging from AI ethics to advanced programming roles[13]. Educational institutions will need to adapt their curriculums to ensure students are well-equipped with both technical and soft skills necessary for an AI-enhanced workforce[13][12].

Challenges and Considerations
While the benefits of AI integration are substantial, several challenges remain. The Gartner Hype Cycle positions Generative AI at the “Peak of Inflated Expectations,” suggesting an impending transition into a phase of disillusionment as organizations grapple with its limitations and the real-world impacts of these technologies[10].
Furthermore, responsible development and deployment of AI systems is critical, as concerns about privacy, security, and ethical implications continue to mount[9].

References
[1]: Modern AI evolution timeline shows a decade of rapid progress
[2]: AI in Software Development — IBM
[3]: Preparing Students and Teachers for a Future with AI
[4]: AI in Software Development: A Comprehensive Guide — Scaler
[5]: Preparing Students For A Tech-Driven World — The Importance Of AI …
[6]: How AI impacts software development | Google Cloud Blog
[7]: The Growing Impact of AI on Software Development | Adeva
[8]: Modeling the global economic impact of AI | McKinsey
[9]: How Will Artificial Intelligence Affect Jobs 2024–2030
[10]: The Macroeconomics of Artificial Intelligence — IMF
[11]: Ethical concerns mount as AI takes bigger decision-making role
[12]: The brief history of artificial intelligence: the world has changed …
[13]: Requirements and Prompt Engineering in AI-Driven Software Development
[14]: Charting the Roadmap: Top 10 Software Development Trends Shaping 2024
[15]: Unleashing developer productivity with generative AI — McKinsey & Company
[16]: How Will AI Affect Software Development? — Beetroot
[17]: How AI-powered software development may affect labor markets — Brookings
[18]: Economic potential of generative AI | McKinsey — McKinsey & Company
[19]: The Top Artificial Intelligence Trends — IBM
[20]: AI Index Report 2024 — Artificial Intelligence Index

Understanding In-App Provisioning and Digital Tokenization with Visa

Seyhun Akyürek — Sat, 28 Sep 2024 11:27:52 +0000

In-app provisioning and digital tokenization are two crucial concepts in the realm of mobile payments, and Visa, a leading payment technology company has developed innovative solutions to facilitate these processes.

In this explanation, I will dive into the world of in-app provisioning and digital tokenization, exploring their significance, benefits and how Visa's solutions can enhance the mobile payment experience.

In-App Provisioning

In-app provisioning is the process of adding a digital card to a mobile wallet, such as Apple Pay, Google Pay, or Samsung Pay, directly from app. This process enables users to easily add their credit or debit cards to their mobile wallets, allowing them to make contactless payments online or in-store. Visa offers two solutions to facilitate in-app provisioning: Visa Digital Enablement SDK and Visa In-App Provisioning API.

Visa Digital Enablement SDK

The Visa Digital Enablement SDK is a software development kit that enables developers to integrate in-app provisioning capabilities into their mobile apps. This SDK provides a seamless and secure method for cardholders to pass their credentials from the app to the digital wallet, eliminating the need to enter card information separately. The SDK supports both iOS and Android platforms and is designed to reduce the complexity and expense of multiple integration efforts for mobile app providers.

Visa In-App Provisioning API

The Visa In-App Provisioning API is a RESTful API that enables developers to generate an encrypted card data payload that can be passed to Apple Pay, Google Pay, or Samsung Pay for tokenization. This API eliminates the need for backend software development complexity and expensive server infrastructure to store encryption keys. The API provides a secure and efficient way to provision digital cards to mobile wallets, reducing the time and effort required for integration.

Digital Tokenization

Digital tokenization is the process of replacing sensitive card information, such as the primary account number (PAN), with a unique digital identifier, known as a token. This token is used to identify the cardholder and facilitate transactions, ensuring that sensitive information is not exposed. Visa Token Service (VTS) is a tokenization platform that enables the creation, management, and distribution of tokens.

Visa Token Service (VTS)

VTS is a cloud-based tokenization platform that provides a secure and scalable way to manage tokens. The platform enables issuers, acquirers, and merchants to tokenize card information, reducing the risk of fraud and improving the overall payment experience. VTS supports multiple tokenization schemes, including EMVCo, and is compatible with various payment systems, including Apple Pay, Google Pay, and Samsung Pay.

Benefits of In-App Provisioning and Digital Tokenization

The combination of in-app provisioning and digital tokenization offers several benefits, including:

Enhanced security: Tokenization replaces sensitive card information with a unique digital identifier, reducing the risk of fraud and data breaches.
Improved user experience: In-app provisioning enables users to easily add their cards to mobile wallets, streamlining the payment process.
Increased adoption: By simplifying the provisioning process, more users are likely to adopt mobile payments, driving growth and revenue for merchants and financial institutions.
Reduced complexity: Visa's solutions reduce the complexity and expense of multiple integration efforts, making it easier for developers to implement in-app provisioning and digital tokenization.

How Visa Offers Two Ways to Include In-App Provisioning in an App

As we discussed earlier, in-app provisioning is the process of adding a digital card to a mobile wallet, such as Apple Pay, Google Pay, or Samsung Pay, directly from app. Visa, a leading payment technology company, provides two solutions to facilitate in-app provisioning: Visa Digital Enablement SDK and Visa In-App Provisioning API.

How Visa Digital Enablement SDK Works

The Visa Digital Enablement SDK is a software development kit that enables developers to integrate in-app provisioning capabilities into their mobile apps. This SDK provides a secure and seamless method for cardholders to pass their payment credentials from the client's mobile application to the mobile or digital wallet of their choice, eliminating the need to enter their payment credentials separately.

The SDK manages integration with wallet APIs, such as Apple Pay, Google Pay, and Samsung Pay, and performs the required "Pay" Token Eligibility validations. It also encrypts the card using the Visa In-App Provisioning API, removing the need for the mobile app provider to interface directly with the "Pays" to provision a card directly from their app.

Visa In-App Provisioning API

The API provides a secure method for cardholders to provision their cards to mobile wallets, reducing the risk of fraud and improving the overall payment experience. It is the client's responsibility to build and maintain direct integration with multiple wallet APIs to perform eligibility checks and securely pass the encrypted payload to them.

Regional Availability

The Visa In-App Provisioning SDK and API are available in various regions, including North America, Asia-Pacific, Europe, CEMEA, and LAC. The regional availability may vary depending on the specific product and service.

Implementation

To implement in-app provisioning, developers can use the Visa Digital Enablement SDK or Visa In-App Provisioning API. The SDK is designed to be embedded within the mobile application(s) by the team responsible for the mobile app development, which may be the client or a party licensed by the client to provide their mobile solution.

The API, on the other hand, requires the client to build and maintain direct integration with multiple wallet APIs to perform eligibility checks and securely pass the encrypted payload to them.

The provided Mermaid sequence diagram illustrates the step-by-step process of implementing in-app provisioning using Visa's solutions within a mobile application. This process enables a user to add their payment card to a mobile wallet (such as Apple Pay, Google Pay, or Samsung Pay) directly from their app securely and efficiently. Below is a detailed explanation of each component and interaction in the diagram.

Participants in the Diagram

User: The individual who wishes to add their payment card to a mobile wallet using their app.
Application: The mobile application provided by the user's financial institution.
VisaSDK (Visa SDK/API): Represents Visa's Digital Enablement SDK or In-App Provisioning API used within the app to facilitate secure card provisioning.
MobileWallet: The digital wallet application (Apple Pay, Google Pay, or Samsung Pay) where the user wants to add their card.
VisaVTS (Visa Token Service): Visa's cloud-based tokenization platform that handles the creation and management of payment tokens.

How Apple, Samsung, and Google Wallets Work with Visa Enablement SDK

As we discussed earlier, Visa Enablement SDK is a software development kit that enables developers to integrate in-app provisioning capabilities into their mobile apps. This SDK provides a secure and seamless method for cardholders to pass their payment credentials from the client's mobile application to the mobile or digital wallet of their choice, eliminating the need to enter their payment credentials separately.

Apple Wallet (Apple Pay)

Apple Wallet, also known as Apple Pay, is a mobile payment and digital wallet service by Apple Inc. that allows users to make payments using their iPhone, Apple Watch, iPad, or Mac. To integrate Apple Wallet with Visa Enablement SDK, developers can use the Visa In-App Provisioning API to generate an encrypted card data payload that can be passed to Apple Wallet for tokenization.

The Visa In-App Provisioning API provides a secure method for cardholders to provision their cards to Apple Wallet, reducing the risk of fraud and improving the overall payment experience. The API eliminates the need for backend software development complexity and expensive server infrastructure to store encryption keys.

Samsung Wallet (Samsung Pay)

Samsung Wallet, also known as Samsung Pay, is a mobile payment service by Samsung Electronics that allows users to make payments using their Samsung Galaxy devices. To integrate Samsung Wallet with Visa Enablement SDK, developers can use the Visa In-App Provisioning API to generate an encrypted card data payload that can be passed to Samsung Wallet for tokenization.

The Visa In-App Provisioning API provides a secure method for cardholders to provision their cards to Samsung Wallet, reducing the risk of fraud and improving the overall payment experience. The API eliminates the need for backend software development complexity and expensive server infrastructure to store encryption keys.

Google Wallet (Google Pay)

Google Wallet, also known as Google Pay, is a digital wallet service by Google LLC that allows users to make payments using their Android devices. To integrate Google Wallet with Visa Enablement SDK, developers can use the Visa In-App Provisioning API to generate an encrypted card data payload that can be passed to Google Wallet for tokenization.

The Visa In-App Provisioning API provides a secure method for cardholders to provision their cards to Google Wallet, reducing the risk of fraud and improving the overall payment experience. The API eliminates the need for backend software development complexity and expensive server infrastructure to store encryption keys.

How it Works

The process of integrating Apple Wallet, Samsung Wallet, or Google Wallet with Visa Enablement SDK involves the following steps:

The user initiates the provisioning process from within the mobile app.
The Visa In-App Provisioning API generates an encrypted card data payload that can be passed to the wallet of choice (Apple Wallet, Samsung Wallet, or Google Wallet).
The encrypted payload is passed to the wallet, which then tokenizes the card information.
The tokenized card information is stored securely in the wallet, eliminating the need for the user to enter their payment credentials separately.

Conclusion

In conclusion, Visa offers two ways to include in-app provisioning in an app: the Visa Digital Enablement SDK and the Visa In-App Provisioning API. Both solutions provide a secure and efficient way to facilitate in-app provisioning, reducing the complexity and expense of multiple integration efforts for mobile app providers.

By understanding the benefits and mechanisms of these solutions, developers, financial institutions, and merchants can create a seamless and secure payment experience for users, driving growth and innovation in the mobile payment industry.

Key Takeaways

Visa offers two solutions for in-app provisioning: Visa Digital Enablement SDK and Visa In-App Provisioning API.
The SDK provides a secure method for cardholders to pass their payment credentials to mobile wallets, eliminating the need to enter their payment credentials separately.
The API generates an encrypted card data payload that can be passed to Apple Pay, Google Pay, or Samsung Pay for tokenization.
Both solutions reduce software development complexity, eliminate encryption key management costs, and improve the user experience.
The regional availability of the SDK and API may vary depending on the specific product and service.
In-app provisioning enables users to add their cards to mobile wallets directly from apps.
Digital tokenization replaces sensitive card information with a unique digital identifier, reducing the risk of fraud and data breaches.
Visa's solutions, including the Visa Digital Enablement SDK and Visa In-App Provisioning API, provide a secure and efficient way to facilitate in-app provisioning and digital tokenization.
The combination of in-app provisioning and digital tokenization offers several benefits, including enhanced security, improved user experience, increased adoption, and reduced complexity.

Buy Now, Pay Later (BNPL) - How It Works, Benefits and Design

Seyhun Akyürek — Sat, 13 Jul 2024 07:55:37 +0000

Buy Now, Pay Later (BNPL) is a form of digital lending. BNPL services offer customers a way to finance their purchases by splitting the cost into smaller, manageable installments, typically without requiring an upfront payment. This digital lending model involves assessing the customer’s creditworthiness, approving the loan, and managing repayments, all through online or mobile platforms. BNPL leverages digital technology to provide a seamless and convenient borrowing experience, making it a subset of the broader digital lending ecosystem.

Buy Now, Pay Later (BNPL in short) services provide a convenient payment option for customers, allowing them to make purchases and pay for them over time.

Here’s a detailed breakdown of how BNPL works, I like to explain in step by step both in business and technical perspective.

Journey

Customer Makes a Purchase

The process begins when a customer selects items they want to purchase and proceeds to the checkout on a merchant’s website or physical store. At the checkout stage, the merchant offers BNPL as one of the payment options. The customer then selects the BNPL option to split the cost into manageable installments.

Customer Signs Up or Signs In

Once the BNPL option is selected, the customer is prompted to sign up for the BNPL service if they don’t already have an account, or to sign in if they do. Signing up involves providing personal information and agreeing to the terms and conditions of the service. This step ensures that the service provider has the necessary details to perform credit and identity checks.

Instant e-KYC and Credit Risk Analysis

To ensure the customer’s eligibility, the BNPL service provider performs an instant electronic Know Your Customer (e-KYC) verification and credit risk assessment. This involves checking the customer’s identity and evaluating their creditworthiness based on various data points, including credit history and financial behavior. This step is crucial for minimizing the risk of defaults.

Approval and Payment

Based on the results of the e-KYC and credit risk analysis, the BNPL service provider decides whether to approve the transaction. If approved, the BNPL provider pays the merchant on behalf of the customer, allowing the customer to complete the purchase without paying the full amount upfront. This immediate payment to the merchant helps ensure a smooth transaction and quick delivery of goods.

Product Delivery

With the payment process completed, the merchant proceeds to ship the product to the customer. The customer receives the product just as they would if they had paid in full at the time of purchase. This seamless experience enhances customer satisfaction and encourages repeat business.

Repayment Schedule

After the purchase, the customer is provided with a repayment schedule outlining the installment amounts and due dates. The repayment period and frequency of installments can vary depending on the terms set by the BNPL provider. This schedule helps customers manage their finances by spreading the cost over time.

Customer Makes Repayments

The final step involves the customer making payments according to the provided schedule. Payments can be automatically deducted from the customer’s bank account or made manually through the BNPL platform. These installments are usually interest-free if paid on time, though late payments may incur additional fees.

Key Players and Their Roles
The key players in the BNPL ecosystem include the customer, the merchant, and the BNPL service provider. The customer benefits from flexible payment options, allowing them to manage their finances better. The merchant benefits from increased sales and reduced cart abandonment rates, as BNPL options can attract a wider range of customers. The BNPL service provider facilitates the transaction, pays the merchant upfront, and manages the repayment process, generating revenue through interest, late fees, and merchant fees.

Benefits of BNPL
For customers, BNPL services provide financial flexibility, enabling them to acquire products without paying the full price upfront. This can be particularly useful for budgeting and managing high-value purchases. For merchants, offering BNPL can lead to higher conversion rates and average order values, making it an attractive option to increase sales. Lastly, BNPL providers benefit from building a customer base and generating revenue through various fees.

This detailed explanation highlights how BNPL services work, detailing each step of the process and the roles of the key players involved, providing a comprehensive understanding of the BNPL ecosystem.

The provided diagram titled “How Does Buy Now Pay Later Work?” outlines the sequential steps involved in a Buy Now Pay Later (BNPL) process.
Now lets find out, with simple explanation of each step in the diagram:

Customer makes an online purchase at a merchant site: The process begins with a customer selecting products and making an online purchase on a merchant’s website.
Merchant offers BNPL as a payment option at checkout: At the checkout stage, the merchant provides the customer with the option to choose BNPL as a payment method.
Customer signs up/signs in to the BNPL service providing platform: The customer either signs up for a new account or signs in to an existing account on the BNPL service provider’s platform.
Payment is debited from the BNPL platform: Once the BNPL option is selected and approved, the payment for the purchase is debited from the BNPL platform. This means the BNPL provider pays the merchant on behalf of the customer.
Product is delivered to the customer: The merchant processes the order and delivers the product to the customer.
Instant e-KYC and credit risk analysis is done by the BNPL service provider: The BNPL service provider performs an electronic Know Your Customer (e-KYC) process and conducts a credit risk analysis to assess the customer’s creditworthiness.
BNPL service provider approves the credit: Based on the results of the e-KYC and credit risk analysis, the BNPL service provider decides whether to approve the credit. If approved, the customer can proceed with the BNPL payment plan.
Customer pays the balance to the BNPL service provider at the end of credit period: The final step involves the customer repaying the BNPL service provider. The customer pays the outstanding balance as per the agreed credit terms at the end of the credit period.
Business Use-Cases for Buy Now, Pay Later (BNPL) Systems
Buy Now, Pay Later (BNPL) systems offer versatile financing solutions across various industries, enhancing customer satisfaction and driving sales.

In e-commerce platforms, BNPL increases conversion rates and average order values by providing flexible payment options. Retail stores can enhance customer loyalty and attract budget-conscious shoppers by offering BNPL at the point of sale.

The travel and hospitality sector benefits from increased bookings and revenue by making travel more accessible through installment payments.

Healthcare services can improve access and manage cash flow by allowing patients to finance medical procedures. Educational institutions can boost enrollment and retention by enabling students to pay tuition fees in installments.

Subscription services, luxury goods, automotive industry, home improvement, and event ticketing also see significant benefits. BNPL reduces upfront costs, making products and services more attainable, driving sales, and fostering customer loyalty.

System Design To Incorporate In Architecture
To design a comprehensive BNPL system architecture and incorporating best practices for modern, scalable, and secure systems, lets create a detailed architecture. This system will include various components and services, focusing on modularity, event-driven architecture, and robust integration.

Full Version

Mermaid Diagram

Key Components:
Frontend Components
The Customer Portal is the interface through which customers can browse products, sign up for BNPL services, apply for loans, and manage their repayments. Technologies: React.js (for building the user interface), Redux (for state management), Tailwind CSS (for styling), and Next.js (for server-side rendering and static site generation).

The Merchant Portal allows merchants to integrate BNPL services into their checkout process, manage transactions, and view analytics. Technologies: React.js, Redux, Tailwind CSS, Next.js are scalable options.

The Admin Portal is used by administrators to manage the BNPL system, monitor performance, handle customer and merchant queries, and perform configurations. Technologies: React.js, Redux, Tailwind CSS, Next.js. Backend and Microservices components are involved.

Loan Origination System (LOS)
Manages the loan application processwa, including customer sign-ups, application submission, and initial verification. Technologies: Node.js (runtime environment), Express.js (web application framework).

Decision Engine evaluates loan applications using predefined business rules and machine learning models to perform instant credit decisioning. Technologies: Node.js, Express.js.

Loan Management System (LMS) Manages active loans, including disbursements, repayment schedules, and the overall loan lifecycle. Technologies: Node.js, Express.js.

Collections Service Handles payment collections, sends reminders, and manages overdue payments. Technologies: Node.js, Express.js.

Product Service Maintains a catalog of BNPL products, including terms, repayment schedules, and interest rates. Technologies: Node.js, Express.js.

Consumer Service manages customer profiles, including KYC (Know Your Customer) data and credit information. Technologies: Node.js, Express.js.

Partner Onboarding Service registers new merchants, manages their profiles, and facilitates their integration with BNPL services. Technologies: Node.js, Express.js.

Paylater Service Provides core BNPL functionalities during the checkout process, including initiation and approval. Technologies: Node.js, Express.js.

Notification Service sends notifications to customers and merchants about application status, payment reminders, and other updates. Technologies: Node.js, Express.js. Cloud Services (AWS)

Technology Stack
In this study, selection of tools to be incorporated via dedicated Amazon Services to simulate ideal infrastructure and tools. Lets list down the required components of technical stack.

API Gateway manages API requests, providing API management functionalities and integration with AWS services and AWS Lambda executes serverless functions for various backend operations, reducing infrastructure management overhead.

Amazon DynamoDB provides a NoSQL database for storing transaction data with fast and predictable performance and Amazon RDS (PostgreSQL) manages relational database operations for transactional data that require complex queries.

Amazon S3 offers object storage for storing documents, reports, and other static content.

Amazon EventBridge facilitates event-driven architecture by managing events and enabling services to communicate via events.

Amazon SQS provides message queuing for decoupling services and ensuring reliable communication.

AWS Fargate runs containerized applications without managing the underlying servers.

AWS WAF protects web applications by filtering and monitoring HTTP requests.

AWS Secrets Manager securely stores and manages sensitive information like API keys, database credentials, and other secrets.

AWS IAM manages identity and access control for AWS resources.

Integration Layer
In this study, IBM API Connect is chosen for its comprehensive API management, advanced security and compliance features, scalability, and performance. It seamlessly integrates with various backend systems, provides robust analytics and monitoring tools, and supports developer engagement through an easy-to-use portal. Its flexibility in deployment (on-premises, cloud, or hybrid) and cost-efficient API monetization and management make it ideal for the complex needs of financial services and digital lending systems.

IBM API Connect manages and exposes APIs to external partners, providing API lifecycle management.

Enterprise Service Bus (ESB) integrates with on-premises core banking systems, handling legacy SOAP/XML communication.

On-Premises Core Banking Services
Finacle API is chosen for this study, per it is robust banking capabilities, seamless integration with core banking systems, and advanced security features. It offers high scalability, performance, and compliance with financial regulations. Additionally, Finacle API provides comprehensive analytics, easy developer onboarding, and flexibility in deployment options, making it ideal for modern banking needs.

Finacle provides core banking functionalities such as account management and transaction processing. Technologies: Finacle API.

KYC Services
KYC (Know Your Customer) services are essential for compliance with legal and regulatory requirements, preventing money laundering and fraud. They help financial institutions assess and manage customer risk, enhancing overall security by verifying customer identities. Ensures customer verification and compliance with regulatory requirements.

Additional Tools and Services
Docker containerizes applications, ensuring consistency across development, testing, and production environments. Technologies: Docker.

Kubernetes orchestrates containerized applications, managing deployments, scaling, and operations. Technologies: Kubernetes.

Terraform manages infrastructure as code (IaC), ensuring consistent and reproducible infrastructure deployment. Technologies: Terraform.

Jenkins facilitates continuous integration and continuous deployment (CI/CD), automating the build, test, and deployment processes. Technologies: Jenkins.

Grafana and Prometheus provides monitoring and alerting capabilities, offering insights into application performance and system health. Technologies: Grafana, Prometheus.

These components collectively ensure a robust, scalable, and secure BNPL system, leveraging modern cloud infrastructure and best practices in software architecture.

Conclusion
This BNPL system architecture ensures a robust, scalable, and secure solution by integrating modern cloud services with legacy on-premises systems. Key components include customer, merchant, and admin portals, various microservices for loan processing and management, and essential AWS services for API management, data storage, and security.

The integration layer and core banking services ensure seamless operation and compliance, providing a comprehensive, efficient BNPL solution.

Software Development Managers And Improving Communication With The Teams

Seyhun Akyürek — Sun, 30 Aug 2020 17:40:37 +0000

Managers need to be able to communicate effectively, either by speaking or writing. They also need a certain degree of flexibility in their thinking and problem-solving skills.

One way to achieve this is by encouraging the participation of more developers in management. This way, managers can have a better sense of what needs to be done, and how it can be done.

Also, the manager must be able to think ahead and create a vision for how things will proceed. This takes deep thought and reflection.

To do these things, the manager must be able to think independently and creatively.

Another important aspect is the ability to communicate with others. A manager must be able to effectively explain his or her ideas, so as not to create confusion.

Also, managers must be able to communicate their ideas neutrally. This means that they can't try to force their own opinions on others. Instead, the manager needs to explain how things work and what is necessary for them.

Last week, an employee of mine was arguing with me about how managers should be able to communicate effectively. To him, it seemed obvious that the manager must be a good communicator and that's why he became one in the first place. I found myself trying to explain to him that there is no reason for this idea; at least not objectively so.

Let's start with the definition of communication. The Oxford English Dictionary defines it as 'The imparting or exchange of information, ideas, opinions, etc., by speech.' Or in other terms: we communicate when we share information.

Now, what happens when we share information? What goes on in our minds when we do so?

I would say that most of the time when we are communicating with someone else, we do not actively think about what information to impart or how it should be imparted. Most of the time this is either intuitively obvious (like in a language which you speak fluently) or something that has been learned and internalized over years (i.e., something like a standard procedure for work). As such, I would suggest that communication is not really an activity but more so a reflex.

So how does this relate to managers?

I would argue that managers should not be good communicators. Not because of some philosophical idea but because managers have no need to communicate anything.

It is clear that the software development process should be improved by managers. No one can argue against this.

Even if the developers themselves don't know what they are doing, managers can still manage them successfully and ensure that their process is a success. Managers should be placed in every software development team.

Managers are better than developers at managing software development because they know more about what is going on. Managers understand the process far better, which also means that they can be very successful.

I do not believe that developers should manage software development. Developers are far too inexperienced in the process and cannot manage it successfully. If developers were placed in charge of managing their own development, they would be a complete failure.

The software development process should be improved with managers because they know how to manage the developers and ensure that the software is good. Developers are not able to do this successfully.

Also, I do believe that the software development process should be improved with developers with defining pipelines/processes with their managers in transparently and collaboratively.

Leadership In Software Development And Leading Developers

Seyhun Akyürek — Sat, 29 Aug 2020 11:09:33 +0000

First, everyone in the software industry must admit. I see that software developers are human beings, just like everyone else. Therefore they have strengths and weaknesses. There is no one who is perfect in every way, so there will be certain aspects of their work where they perform poorly.

Their work is also not something that they do for free but instead requires compensation. A developer will need to be properly compensated for their time and effort in order to have a positive attitude towards the task.

If they are not being properly compensated for their time and effort, then it will negatively affect the quality of the work that they do. If a developer is unhappy with how they are being treated or underpaid in some way (or simply tired from working too many hours), this can cause them to quit or burnout.

Therefore, management and leadership is an important consideration. If a leader can provide incentives for his employees to work hard on tasks, as well as treating them fairly and providing the necessary infrastructure to support their work, then this will help ensure that the development process goes smoothly.

But if these things are not done, then there is a good chance that the development process will be negatively affected. This can result in crashes and broken code or other similar problems that affect the team and the entire company in the long term.

A good leader, a good manager should be able to provide good leadership, not like a boss, so as to ensure that the processes go smoothly.

One of the most important things for leadership is to know when to listen. This is something that many people do not understand, especially concerning certain social roles such as a leader or someone in a higher position than others. A good leader will have great insight into how much input he/she needs from his followers and also how much he should give them.

The trick is learning how to balance listening with giving your own opinion enough weight so that you can be effective but without being too overbearing on those around you.
Another important thing about leadership is to learn how much of an opinion you need to have and then when it needs to be expressed.

If you are in a situation where everyone around you is new, or they do not know what the best approach is for completing a task, it may be your job as the leader or more experienced person on the team to make suggestions.

However, if there is someone with greater experience than yourself who has already come up with a strategy that will work well enough, then your contribution should probably just be suggestions. Being a tough leader, in characteristically set high standards and, are competitive and professional executors.

In summary, leadership is a delicate thing. You need to constantly be aware of how much input you have and when it might be time to give in or step up.

Software Engineering Management and Why Context Matters

Seyhun Akyürek — Sat, 29 Aug 2020 06:33:35 +0000

Large projects suffering management problems different from small ones because of the organization's issues. The administrative level is too far away to understand what's going on in the organization, so there is a lack of communication between those who make decisions and those who are affected by them.

Lack of communication is due to a gap in understanding, which is induced by the fact that people are not the same. This diversity results from the differences between individuals and causes major problems when it comes to managing any organization. People can't understand how things work on the lower level because they don't use their imaginations enough; instead, they rely too much on words.

This lack of understanding leads to a desire for control, which is the opposite of the anarchy that would occur if everyone worked in their own way. This situation arises because individuals have different agendas and interests; therefore they cannot be trusted not to work against each other.

Here's the thing: I'm going to assume that by "communication," you mean verbal communication, as written and non-verbal forms of it do not usually lead to misunderstandings. Verbal communication is an incredibly important part of human society; in fact, humans use a large portion of their brain just for speech production and recognition. It also helps keep track of information. So we can't remember everything at once, so we assign meanings to words or symbols which represent concepts such as objects in our environment or abstract ideas like events or actions.

When it comes to misunderstandings, they occur for several reasons. The most common is the difference between literal and metaphorical language; the second most common is a lack of context in which an action occurs or a statement was made.

Literal language is the most common form of communication, and while it's useful for conveying factual information, it can be misleading. If you say "The sky is blue," that statement has meaning in terms of literal facts it reflects reality; but if I said to someone "You're an angel" or "That was a close one," we know those sentences are not meant literally.

Context is extremely important to communication because it provides the information necessary to understand a statement. If I said "It's raining" while I was standing outside in the rain with wet hair and clothes, then that sentence has meaning as an observation of reality; but if a friend said it when they were sitting on their couch watching television, we would know something is amiss.

What's interesting is that a lot of the time, humans are themselves responsible for their own misunderstandings. We can't remember everything so we make assumptions based on what information we have -- but since we don't know if those assumptions are correct until they're confirmed, it gives us almost no way to verify whether our guesses were accurate.

Do's and Don'ts in Software Development

Seyhun Akyürek — Mon, 24 Feb 2020 06:18:22 +0000

In this post, I like to share the do/don't a checklist for the software application development, these are actually best/worst practices I facing every day in my software management career.

Which some of them I learned in practice, some of them I found in blogs and articles, proven and tested practices so In this post, I wrapped up in all of them. IMHO of course.

Let's get started;

Do's
Upfront

Gather all required materials up-front.
What's more, is that all the requirements must be explained as clearly as possible.
Start with giving a thought to it after understanding its functional, user interface, integration, and nonfunctional requirements. This would give you a better perspective on how finally it should look like.

Architectural

Try to break down it into smaller chunks and come up with a design for - small components.
Do not use hardcoded strings in code.
Do not write boilerplate code and reinvent the wheel.
Don't develop code if you can't maintain for mid/long term.
Do not think about premature optimizations.
Don't forget to documentation, write code descriptions. If you are using company Wiki (i.e Confluence) this is better.
Avoid over-engineering, over design.
Deploy small, deploy often is crucial.
Always keep it user-centric.
Make a decision from the start, follow them
Always think of multiple designs for a requirement.
Test, Test, Test. Thinking about different unit test scenarios helps in understanding & imagining the various real-life scenarios and how this design is going to handle them in a production environment if that scenario arises.
Take refactoring tasks to avoid technical debt.

Design

Follow KISS (Keep it simple, silly) principle while writing code.
Do you have a difficult time finalizing decisions? Keep it simple, understandable as possible.
To avoid confrontation to the point of making extra work for yourself or others.
Do you have trouble saying no, even to tasks that detract from your core responsibilities, say no?
Always take time to carefully consider every aspect of potential projects; try to break smaller tasks.

Environmental

Consider running time environment situations; thread-safe operations, parallel execution, server topology, number of hops/point of failures, server date/timezone considerations, memory requirements, server/database settings, number of concurrent users, etc. Your code must be well configured for the test, pre-prod and prod environment to execute well, you need to handle well in different environments.

Scrum

Do when new tech inevitably sparks off new ideas and improvement opportunities, it would be better to keep such process revisions out of the Sprint sessions and addressed with the business process owners. So when user stories from the have initial feature set, high-level requirements and expand them through design.
To cross-validate the user stories with different stakeholders.
Rationalize and prune. Park the shortlisted futuristic requirements for a later Sprint.
To start with well understandable, well-defined tasks must be taken first. In the Scrum framework, the team gives a rough software estimation. Only for the first two iterations, the team can make more or less accurate predictions. Also, in the beginning, it's common that even clients don't know what they want from the development. The details of features aren't still well-thought-out, so it's almost impossible for the team to accurately predict software estimation under such circumstances.

Don'ts

Don't create for yourself.
Don't start unprepared before starting to project.
Don't change suddenly
Don't set inaccurate estimates, time expectations for complex features.
Don't start coding right now, first build a "Proof of Concept" but the end of completing most of the coding.
Don't skip documentation
Don't overcomplicate. Never ever complicate design for addressing rare complex scenarios. complex requirement scenarios will always be for every feature and don't try to address each one of them.
Don't skip the coding convention guideline.
Don't be so optimistic. Software engineers are chronically too optimistic so Usually, anything unforeseen slows things down.
Don't keep user stories open-ended and liable to multiple changes during the Sprint.