at and t cellular

In this practical book written for busy software engineers with responsibilities along the full SDLC, you'll build a collaborative workspace application—similar to a hybrid of Slack and Figma—that expands in scope as you progress. As you explore this collaborative AI workflow, you’ll discover how to leverage Claude Cowork to securely access and edit files directly in your project directories. When you scale the system, you’ll see how to direct Claude Code on parallel workstreams, focusing your time on task design, validation, and architectural approvals! [Read more]

In this instantly-useful book, integration architect Dominik Włodarz draws on 15+ years of designing, evolving, and recovering enterprise and cloud-native systems to give you a durable framework for reasoning about trade-offs. He shows you how a team's capabilities, ownership model, delivery pressure, and operational costs change what a sound architecture choice looks like, rather than prescribing a universally “best” architecture. Włodarz’s recurring decision model of intent, forces, decisions, consequences, feedback, and adaptation helps you connect each architectural choice to what happens after it’s made. You’ll learn to record why a decision made sense, who owns its consequences, what evidence could invalidate its assumptions, and when it should be reconsidered. [Read more]

The book guides you in resolving “prompt debt” through the creation of version-controlled, auditable prompt repositories. These assets can undergo code review via pull requests, incorporate protections against prompt injection, and enforce access controls. By bridging the divide between initial prototyping and operational deployment, this guide delivers the practical methodologies required to partner effectively across engineering teams and sustain secure, reliable AI implementations. [Read more]

Practical from page one, this book mirrors a professional AI engineering workflow, taking you from simple classifiers to complex summarizers, advanced RAG, and agentic systems. This book guides you through constructing custom evaluation metrics, setting up multi-threaded test runners, and calibrating LLM-as-a-judge protocols to gather hard data. Instead of deploying prompts on a whim, you compile and optimize them against structured validation sets. The resulting programs are mathematically proven to be more accurate, more cost-effective, and remarkably resilient to underlying model drift. [Read more]

Just as importantly, Grokking Statistics teaches you to question data itself. You’ll learn to look for biased samples, questionable assumptions, misleading metrics, overfitting, p-hacking, and other ways statistical reasoning can go wrong. These are essential practical skills. They matter whether you’re evaluating a research finding, analyzing business data, building a software product, or assessing the performance of a machine learning model. [Read more]