📚 Digital Bookshelf

A curated list of books I’m reading or have recently read — focused on Gen AI, Go programming, system design, my love for rifles, and more.

🧠 System Design Interview: An Insider’s Guide

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— Alex Xu

A structured guide to mastering system design interviews through practical examples of real-world systems like URL shorteners and streaming platforms.

Established the standard framework for system design thinking, bridging theoretical CS knowledge with practical distributed systems skills required in modern tech.

🧠 The C Programming Language

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— Dennis M. Ritchie and Brian W. Kernighan

The definitive guide to C programming, written by the language’s creator Dennis Ritchie and co-authored by Brian Kernighan. This seminal work introduces the C programming language through clear explanations and practical examples, covering fundamental concepts like data types, control structures, functions, and pointers. More than just a tutorial, it serves as both a learning resource for beginners and a comprehensive reference for experienced programmers.

Without this book and the C language it documents, the digital world as we know it simply wouldn’t exist. C became the foundation upon which virtually every modern technology was built—from operating systems like Unix and Linux to programming languages like Python, JavaScript, and Go. The efficiency and portability of C made possible everything from embedded systems in your microwave to the servers powering the internet. Every smartphone, every web browser, every database, and every piece of networking equipment traces its lineage back to C.

The book’s approach of teaching through working programs revolutionized technical education and influenced countless programming texts. Its concise yet thorough coverage didn’t just teach a language—it shaped how entire generations of programmers think about software design, memory management, and system architecture. When you send a text message, stream a video, or make an online purchase, you’re using systems that exist because Ritchie created C and this book made it accessible to the world. In essence, this modest 272-page volume helped birth the Information Age itself.

🧠 The Go Programming Language

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— Alan A. A. Donovan and Brian W. Kernighan

The authoritative guide to Go programming, written by Google engineers Alan Donovan and Brian Kernighan (co-author of the legendary C programming book). This comprehensive text introduces Go’s clean syntax, powerful concurrency model, and practical approach to modern software development through hands-on examples and real-world applications. From basic syntax to advanced topics like reflection and low-level programming, the book demonstrates why Go has become the language of choice for cloud infrastructure, microservices, and distributed systems.

Go represents the next evolutionary step in systems programming for the internet age. Created at Google to solve the challenges of modern software development—massive codebases, distributed systems, and multi-core processors—Go fills the gap between the raw performance of C/C++ and the productivity of Python or JavaScript. This book didn’t just document a new language; it introduced the programming paradigms that power today’s digital infrastructure.

Without Go and the principles this book teaches, we wouldn’t have the seamless cloud services, lightning-fast container orchestration (Docker, Kubernetes), or the robust backend systems that handle billions of API calls daily. Go powers everything from Netflix’s content delivery to Uber’s real-time matching algorithms, from cryptocurrency networks to the very infrastructure that keeps the modern internet running smoothly. The language’s emphasis on simplicity, concurrency, and reliability—all thoroughly explained in this book—has fundamentally changed how we build software for the connected world. In many ways, this book documents the programming language that made the cloud-native revolution possible.

📊 An Introduction to Statistical Learning

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— Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

This is the gold standard textbook for anyone wanting to master the statistical foundations behind modern AI and machine learning. Written by Stanford and USC professors, it breaks down complex statistical learning concepts into digestible, practical knowledge without drowning you in heavy math. Whether you’re building AI models, analyzing data, or just want to understand what’s really happening under the hood of machine learning algorithms, this book delivers. Available in both R (ISLR) and Python (ISLP) editions with hands-on labs, it covers everything from basic regression to deep learning and survival analysis. Translated into 7 languages and trusted by data scientists worldwide—it’s the book that turns statistical learning from intimidating to intuitive.

🧠 Build a Large Language Model (From Scratch)

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— Sebastian Raschka

A comprehensive guide to understanding and implementing large language models from the ground up, written by machine learning expert Sebastian Raschka. This hands-on book demystifies the architecture behind ChatGPT, GPT-4, and similar transformer-based models by walking readers through every component—from tokenization and embeddings to attention mechanisms and training procedures. Rather than treating LLMs as black boxes, Raschka provides clear explanations and working code that illuminates how these revolutionary systems actually function under the hood.

This book arrives at perhaps the most pivotal moment in human technological history. We are witnessing the emergence of artificial general intelligence, and large language models represent the first glimpse of machines that can truly understand and generate human language at scale. Without understanding how these systems work, we risk being passengers in our own technological revolution. This book empowers readers to become architects rather than mere users of AI.

The transformer architecture and training methodologies explained in these pages are already reshaping every industry imaginable—from automating customer service and revolutionizing education to accelerating scientific research and transforming creative industries. The attention mechanisms, neural network architectures, and optimization techniques detailed here don’t just power chatbots; they’re the foundation for AI systems that are beginning to match and exceed human performance in reasoning, coding, mathematics, and creative tasks. By teaching readers to build these models from scratch, this book provides the deep understanding necessary to participate in—rather than simply observe—the AI revolution that is fundamentally redefining what it means to be human in an age of artificial intelligence. In essence, this book is a blueprint for understanding the technology that may well represent the most significant leap in human capability since the invention of writing itself.

🤖 The AI Pocket Book

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— Emmanuel Maggiori

This book is your no-nonsense guide to thriving in the age of AI—whether you’re a software engineer or just AI-curious. With clear, jargon-free explanations, it helps you understand how AI works, how to use it effectively in your job, and how to stay relevant (and irreplaceable) as the tech evolves. From decoding AI “hallucinations” to tackling hot topics like copyright and the future of work, it’s a must-read for anyone who wants to work with AI—not be replaced by it.

🔢 Essential Math for Machine Learning

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— Deisenroth, Faisal, Ong

Teaches linear algebra, probability, and calculus foundations needed for ML with clarity and relevance.

Provides the mathematical foundation essential for understanding modern machine learning algorithms and their applications.

🧠 A Compact Guide to Retrieval Augmented Generation

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— Databricks eBook

Explains enhancing LLMs with external data retrieval, including implementation examples and enterprise use cases.

Addresses LLM limitations by showing how to ground AI responses in factual data, crucial for enterprise AI applications.

🐹 100 Go Mistakes and How to Avoid Them

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— Teiva Harsanyi

Identifies and explains common Go programming mistakes with solutions, ideal for developers transitioning from other languages.

Accelerates Go adoption by helping developers avoid pitfalls, improving code quality and development efficiency.

🧵 Concurrency in Go

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— Katherine Cox-Buday

Deep dive into Go’s concurrency primitives, patterns, and pitfalls for building scalable applications.

Unlocks Go’s most powerful feature for building high-performance, concurrent systems that handle modern web scale.

☸️ Kubernetes in Action 2nd Edition

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— Marko Lukša

Comprehensive guide to container orchestration with Kubernetes from deployment to scaling production applications.

Enables mastery of the platform that revolutionized software deployment and scaling, essential for modern DevOps.

🐍 Automate Boring Stuff With Python

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— Al Sweigart

Learn to automate repetitive tasks like email handling, PDF processing, and spreadsheet manipulation using Python.

Democratized automation by making Python accessible to non-programmers, increasing productivity across industries.

🤖 Practical Machine Learning With Python

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— Dipanjan Sarkar

Hands-on guide for building end-to-end ML pipelines using Python with real-world datasets and practical applications.

Bridges theory and practice in ML implementation, providing actionable guidance for deploying machine learning solutions.

📊 NumPy, SciPy, Matplotlib, and Pillow

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— Ronald T. Kneusel

Reference guide covering essential Python libraries for data processing, scientific computing, visualization, and image manipulation.

Comprehensive resource for Python’s scientific computing ecosystem, enabling data science and research applications.

💻 Let’s Go v1.24

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— Alex Edwards

A comprehensive, hands-on guide that teaches you to build complete web applications in Go using only the standard library and minimal third-party packages. Rather than relying on heavy frameworks, this book demonstrates how Go’s built-in tools provide everything needed for even moderately complex projects. Through a practical, step-by-step approach, you’ll build a real web application from the ground up, learning essential patterns for organizing workspaces, managing sessions, implementing authentication, securing servers, and writing comprehensive tests.

Bridges the gap between Go basics and production-ready development by showing how different components of a web application work together, preparing developers to handle real-world challenges and edge cases in Go web development.

🚀 Let’s Go Further v1.22

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— Alex Edwards

Advanced Go tutorial building Greenlight, a complete JSON API backend with authentication, validation, and deployment.

Advanced Go web development techniques for building production-ready APIs and microservices.

🏠 Automate Your Home Using Go

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— Gerardi & Riley

Shows how to create a personal data center using Go, Docker, Prometheus, and Raspberry Pi for home automation.

Demonstrates practical IoT and home automation using modern development tools and practices.

🔄 Learn Concurrent Programming with Go

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— James Cutajar

Teaches Go’s concurrency model through real-world examples, focusing on goroutines, channels, and concurrent patterns.

Specialized focus on Go’s concurrency features, essential for building high-performance, scalable applications.

🐍 Python Crash Course

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— Eric Matthes

Beginner-friendly, project-based introduction to Python programming with strong emphasis on practical applications and real projects.

Gateway to programming for beginners, combining solid fundamentals with engaging projects that maintain motivation.

🎯 Mauser Rifles

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In 1898, Paul Mauser created a rifle so perfect it would change the world. More than 125 years later, his Model 98 is still considered the master blueprint for the modern rifle.

A Design That Couldn’t Be Beaten: The Mauser 98 was simple, strong, and incredibly safe. It was built with one goal in mind: to not fail. Copied by thousands but never truly matched, it became the foundation for nearly every bolt-action rifle that followed.

Its genius was a special mechanism that gripped each bullet tightly, from the magazine to the chamber and back out again. This made the rifle almost impossible to jam, giving it legendary reliability.

A Weapon That Ruled the Battlefield: Adopted by the German army, the Mauser 98 dominated the battlefields of two world wars. Its superiority was obvious. Early on, soldiers using Mausers easily outgunned American troops armed with older rifles. Over 100 million were built, arming more than 30 countries.

The Rifle America Had to Copy: The Mauser’s design was so good that the U.S. government copied it for its own famous M1903 Springfield rifle—and was forced to pay Mauser royalties for the idea. America’s beloved Winchester Model 70, “The Rifleman’s Rifle,” was also a direct copy of the Mauser 98.

The Gold Standard for Dangerous Hunts: When facing lions or bears, a hunter’s life depends on their rifle. Because the Mauser 98 would not jam, it became the top choice for dangerous game hunting, a standard by which all other rifles are still judged today.

A Legend That Lives On: Can you think of any other product from the 1800s that is still being made and celebrated as the best of its kind? The Mauser 98 isn’t just a piece of history. It is a timeless masterpiece of engineering that remains the peak of rifle design.

🦌 Insiders Guide to Selecting the Best Caliber for Hunting

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Simplifies the complex process of choosing appropriate hunting calibers based on game size, hunting conditions, and shooter requirements.

Practical guidance for hunters to make informed caliber choices, improving hunting success and ethical shot placement.