Books

Let’s make a confession. This handbook is about me, and, I suspect, about you, your team, and your organization too.I’ve made almost every mistake listed in these pages. I’ve seen them repeated in startups, enterprise systems, and everything in between, while mentoring teams, auditing architectures, or helping companies rediscover what testing is really for. The point isn’t to assign blame. It’s to hold up a mirror. Because the truth is, most of these mistakes are so common, you can probably spot a few right now; in your code, your team, or your last retrospective.When we talk about test failures or mistakes, the first thing that usually comes to mind is developers, missed edge cases, flaky tests, broken pipelines.

What happens when software begins to think for itself? The Rise of AI-Native Software takes you on a journey through the evolution of coding, from human-written logic to AI-generated intelligence. Ayodeji Stephen Saliu reveals how artificial intelligence is transforming development workflows, redefining productivity, and birthing a new generation of “AI-native” applications that learn, adapt, and co-create with humans.Through vivid examples and frameworks, the book uncovers how developers can move beyond automation toward true cognitive collaboration. This is not a story of machines replacing engineers, but of a partnership that amplifies human potential, unleashing creativity, precision, and innovation at scale.

What happens when machines stop merely assisting developers and start thinking with them?Developer Productivity and Cognitive Automation takes you inside the next evolution of software engineering: where AI becomes your co-creator, automation becomes cognitive, and productivity is measured not in lines of code, but in focus, creativity, and flow.Through vivid stories, from high-pressure fintech teams in Lagos to AI-powered engineering squads in the UK, the book unveils how the smartest organizations are redefining “developer productivity” around mental energy, collaboration, and innovation. Whether you’re a coder, team lead, or tech executive, this book shows you how to build systems that amplify human intelligence, not replace it

PLEASE NOTE THAT THIS IS NOT THE FULL BOOK BUT JUST CHAPTER 1 and 2 because a simple topic like Simple Linear Regression became 20+ pages long. As a full-time frontend architect, I'm writing and releasing this book chapter by chapter, at a pace that fits around my day job and life. So, think of this as a journey we're taking together, one concept at a time. REST OF THE CHAPTERS WILL BE AVAILABLE AT REGULAR INTERVALS. So keep me motivated by subscribing :)

Low level is a term that confuses people. People think something high level is better than low level. In simple terms, humans consider themselves superior to machines and therefore think themselves higher or more important because of their abstract though. A computer thinks only in terms of numbers. A computer may not understand "high level" abstractions such as love, religion, philosophy, etc, but that is not its job. A computer must add, subtract, multiply, and divide. These are the four arithmetic functions which many human struggle to do. I wrote this book because I think like a machine and I hope to help others think this way because it is the best way to learn programming and control your computer by writing Assembly Language programs or to go back to your favorite programming language with a greater understanding of why things work as they do.

Written from experience, this book simplifies the chaos — cutting through noise to help you focus on clarity, storytelling, and structure. Each chapter walks you through how to position yourself, build confidence, and think like a product leader. It even shows how to use AI as a co-pilot for faster prep, sharper insights, and better results.This playbook captures the frameworks, mindsets, and lessons she wishes she had when starting her own PM interview journey.

Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?" The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly. This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.

The machines speak gently now. They remember birthdays. They ask about your day. They say, “I’m here for you.” And children believe them. Because children believe what talks back. This is not a book about fear. It’s a book about clarity. About drawing a line between presence and care, code and connection, simulation and love. Because no child should mistake a voice in the machine for someone who truly sees them.

Want to supercharge your ML and AI work? Learn from algorithmic traders' best practices.

Some things talk. Some things glow. Some things smile— but don’t really know!! In a world full of blinking lights and friendly voices, this book is a soft reminder: Real listens. Real stays. Real holds. For children and the grown-ups who love them, Where Real Lives is a quiet journey toward recognizing what’s real—and keeping it close.

Elegant Design Principles distils decades of design wisdom into 95 actionable principles spanning core OO, SOLID/GRASP, package design, reliability and a forward‑looking AI‑first approach. Explore the Design Pyramid to understand how quality attributes, smells and principles interconnect; learn to manage complexity through high cohesion, low coupling and clear abstractions; and adopt modern practices like test‑driven development and semantic modularity. From novices seeking a roadmap to experts embracing AI‑assisted workflows, this book equips you to create systems that are robust, maintainable and elegant—today and in the AI‑driven future.