Adrian j corwin (11 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware - Software development is changing. Ideas that once required weeks of scaffolding, syntax, and setup can now move from natural-language description to working software far faster than traditional workflows allowed. The difference is not magic. It is a new way of collaborating with AI systems that can generate, transform, and iterate on code at high speed. Vibe Coding Mastery teaches that method as a serious development practice.>This book shows how to move from idea to functional software without abandoning engineering judgment. It addresses the full lifecycle foundations, collaboration, prompt construction, controlled generation, rapid prototyping, systematic debugging, refactoring, modern workflows, low-code and automation, architecture and security, production readiness, real projects, and the practices that remain useful as tools evolve. Readers learn how to: - Direct AI systems with precise intent, context, and acceptance criteria- Generate and evaluate code rather than accept the first plausible output- Build functional prototypes that answer real product questions- Debug AI-assisted software through evidence instead of guesswork- Refactor fragile or duplicated generated code into maintainable structure- Design repeatable AI-native development workflows- Combine natural-language development with APIs, data, authentication, and external services- Integrate low-code, no-code, and automation platforms where they fit- Test, secure, and prepare applications for deployment- Recognize the limits of generated code and keep human oversight in controlAI-generated code can be wrong. It can invent APIs, omit edge cases, introduce security weaknesses, or create technical debt that surfaces later. This book treats those risks as expected. It teaches readers how to detect them, test against them, and correct them so that speed does not become carelessness.>Who this book is for: >The promise is straightforward. Learn how to use AI to move faster without becoming careless. Prototype without abandoning engineering discipline. Turn natural-language ideas into increasingly reliable software through deliberate human-AI collaboration. Vibe Coding Mastery gives readers a complete, practical framework for doing exactly that from the first clear statement of intent to software that can be tested, deployed, and improved with responsibility.…

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - GPT-5 Codex for DevelopersSoftware development is changing faster than ever. GPT-5 Codex is no longer just an AI code assistant-it is a powerful engineering partner capable of understanding complex codebases, generating production-quality implementations, automating development workflows, and accelerating every stage of the software lifecycle.The difference between developers who thrive and those who struggle will increasingly come down to one skill: knowing how to work effectively with AI.GPT-5 Codex for Developers is a practical, hands-on guide that shows you how to transform GPT-5 Codex into a reliable engineering tool for real-world software development. Instead of focusing on theory or isolated prompts, this book teaches production-ready workflows, proven prompting strategies, and complete implementations you can immediately apply to professional projects.Inside this book, you'll learn how to: - Build high-precision prompting frameworks for consistent code generation- Create AI-powered debugging and refactoring assistants- Develop intelligent Git workflow automation- Build terminal-based developer companions- Design multi-step autonomous coding agents- Analyze and optimize large repositories with AI- Generate documentation and APIs automatically- Secure, monitor, and deploy AI-powered developer tools- Reduce token costs while improving performance- Integrate GPT-5 Codex into modern CI/CD pipelinesEvery chapter includes practical examples, complete code, implementation guidance, testing strategies, common pitfalls, best practices, and reusable components that you can adapt to your own projects.Rather than presenting isolated techniques, the book follows a cumulative approach. Each chapter builds upon previous concepts until you've assembled a professional AI-powered development toolkit suitable for production environments.This book is written for software developers, backend and full-stack engineers, DevOps professionals, automation engineers, technical consultants, and technical founders who already have experience with Python, Git, APIs, and command-line tools. It is designed for professionals who want to increase productivity while maintaining high standards for security, scalability, maintainability, and code quality.AI-assisted software engineering is rapidly becoming the new standard. Developers who can build, automate, and maintain intelligent engineering workflows will have a significant advantage in the years ahead.If you're ready to move beyond simple AI code generation and start building production-ready developer tools, intelligent workflows, and scalable engineering systems with GPT-5 Codex, this book will give you the practical knowledge, reusable code, and professional techniques to make it happen. …

Taschenbuch. Zustand: Neu. Neuware - Mastering Professional AI Systems Design equips technically proficient engineers, architects, and creators with the complete blueprint for building reliable, scalable, and monetizable AI applications. This book bridges the gap between experimental prompting and production-grade systems by delivering a systematic approach to AI workflow engineering, context management, agentic orchestration, and enterprise deployment. Through progressive chapters, readers master the transition from ad-hoc model interactions to sophisticated architectures built on LangChain, LangGraph, Retrieval-Augmented Generation, vector databases, and structured human-AI collaboration. The content covers core foundations including professional prompt engineering, repeatable workflow systems, persistent knowledge libraries, and decision frameworks that ensure trustworthiness at scale. Subsequent sections demonstrate practical application across real-world domains: end-to-end content production pipelines, business operations automation, client delivery platforms, AI-powered marketing systems, and unified productivity ecosystems. Advanced topics address critical production concerns such as evaluation and iteration, quality assurance, scaling without complexity, and future-proofing against evolving model capabilities. The final chapters translate technical mastery into sustainable business value through productization strategies, pricing models, recurring revenue systems, and the creation of a personal AI operating system for long-term creator leverage. Every concept receives thorough explanation before implementation. Complete, runnable code examples follow current best practices, accompanied by detailed architectural reasoning, trade-off analysis, performance considerations, and production guidance. The book emphasizes modularity, observability, maintainability, and responsible design essential attributes for systems that deliver compounding returns rather than fragile prototypes. Written for experienced Python developers and software architects seeking mastery-level expertise, this volume provides the mental models, reusable patterns, and operational frameworks necessary to design AI solutions that enhance human capability while meeting rigorous professional standards. Whether building internal tools, client-facing platforms, or commercial digital products, readers gain the systematic discipline required to succeed in production environments. Transform AI from a collection of tools into a core competitive advantage. This book delivers the complete roadmap.…

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Most developers try GitHub Copilot the same way: accept a few suggestions, paste a prompt into chat, and hope the output is good enough. Sometimes it helps. Sometimes it produces fluent code that is wrong, insecure, incomplete, or disconnected from the real system. The difference is not luck. It is method. GitHub Copilot Mastery is a practical guide to using Copilot as part of disciplined software engineering, not as a novelty autocomplete tool. It shows how to move from occasional experimentation to deliberate AI-assisted development across the full lifecycle: planning, implementation, code understanding, refactoring, debugging, testing, Git and GitHub workflows, pull requests, code review, CI/CD, security, automation, and agentic development. This book is built for working engineers. It assumes programming experience and focuses on the skills that actually determine whether AI assistance improves outcomes: framing the problem clearly, supplying the right context, imposing constraints, evaluating generated output, verifying behavior with tests, and retaining human responsibility for architecture, quality, security, and production decisions. Faster generation is not the goal. Better engineering decisions are.Inside this book, readers learn how to: - Treat Copilot as an AI-assisted engineering capability rather than a prompt toy- Provide high-quality context from files, repositories, requirements, and project conventions- Generate and refine production-oriented code without surrendering design control- Use AI assistance to understand complex codebases, diagnose failures, and improve tests- Apply Copilot in GitHub workflows, pull requests, CI/CD pipelines, and operational automation- Use agentic workflows with clear scope, oversight, and review standards- Establish team adoption practices, governance, and meaningful measures of valueWho this book is for: Software developers, software engineers, DevOps and platform engineers, QA engineers, technical leads, and engineering managers who want to integrate GitHub Copilot into real delivery workflows. It is especially useful for professionals who have already tried Copilot and want a more rigorous, repeatable approach. If the goal is to generate more code, almost any AI tool will oblige. If the goal is to ship software that remains correct, maintainable, and operable, Copilot must be used with engineering judgment. GitHub Copilot Mastery provides the methods, workflows, and decision frameworks needed to do exactly that.…

Taschenbuch. Zustand: Neu. Neuware - Many engineers can get a container running on OpenShift. Fewer can explain why the Pod landed where it did, how traffic reaches it, what happens when a node fails, or how to recover when a deployment introduces a defect. Documentation and short tutorials often teach isolated commands. Production work demands something deeper: an understanding of OpenShift as an integrated enterprise platform built on Kubernetes. Red Hat OpenShift in Practice bridges that gap. It takes readers from containers and Kubernetes foundations through the full operational life of cloud-native applications on OpenShift deployment, networking, storage, security, scaling, delivery automation, observability, Operators, cluster lifecycle, and production operations. The book is written for practitioners who need more than a getting-started guide. It serves DevOps engineers, platform engineers, SREs, cloud and infrastructure engineers, Linux administrators, application developers working with containers, and Kubernetes practitioners transitioning to OpenShift. Newcomers receive the conceptual grounding required to follow the material. Experienced Kubernetes users gain a clear view of what OpenShift adds and how its architecture, security model, networking, and day-two services change real operational practice. Rather than presenting OpenShift as a collection of features or console clicks, the material follows a consistent progression: concept, architecture, mechanism, implementation, verification, operations, and troubleshooting. Readers learn not only how to deploy a workload, but how the control plane, scheduler, network, storage, and security controls interact; how to expose applications securely; how to design and diagnose networking paths; how to manage persistent and stateful workloads; how to implement identity, RBAC, and workload constraints; how to scale both applications and cluster capacity; how to build modern CI/CD and GitOps workflows; how to interpret metrics, logs, and events; and how to troubleshoot failures systematically. Special attention is given to the realities that appear after an application first starts successfully. Security, multi-tenancy, network isolation, storage durability, availability under failure, observability, automated delivery, Operator-based lifecycle management, upgrades, backup, recovery, and governance are treated as core engineering concerns rather than advanced afterthoughts. The goal is competence in operating OpenShift environments, not merely the ability to launch containers.>Whether the task is deploying the first multi-component application, securing a multi-tenant platform, diagnosing a production networking failure, planning a cluster upgrade, or designing a repeatable delivery pipeline, Red Hat OpenShift in Practice equips readers to understand the system, reason about its behavior, and operate it with confidence.…

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Master the engineering discipline that powers modern artificial intelligence. Large Language Model Engineering delivers a comprehensive, production-focused guide to designing, training, fine-tuning, deploying, and scaling reliable large language model systems using Transformers, Retrieval-Augmented Generation (RAG), Agentic AI, and PyTorch. As organizations increasingly rely on large language models for critical applications, the ability to build robust, efficient, and maintainable systems has become an essential skill for software engineers, machine learning practitioners, and technical leaders. This book bridges the gap between theoretical understanding and real-world implementation, providing the practical knowledge required to move beyond research prototypes to production-grade AI infrastructure. Readers follow a carefully structured, continuous engineering project that builds a complete LLM ecosystem chapter by chapter. The journey begins with foundational tensor operations and embedding layers, progresses through transformer architecture and attention mechanisms, and advances to full decoder-only model construction. Subsequent chapters cover efficient pretraining workflows, parameter-efficient fine-tuning techniques including LoRA and QLoRA, instruction tuning, and preference optimization. The book then integrates retrieval-augmented generation for grounded responses, develops agentic capabilities with tool calling and memory systems, optimizes inference through quantization and KV caching, and establishes comprehensive evaluation frameworks. Production engineering receives detailed attention throughout. Readers learn to implement scalable data pipelines, manage distributed training, design high-performance inference services, establish robust monitoring and observability, and architect enterprise platforms that support multi-tenancy, cost optimization, and governance requirements. Every major concept follows a consistent instructional approach: engineering motivation, architectural understanding, complete working implementations, production considerations, optimization strategies, common pitfalls, and professional best practices. This book distinguishes itself through its emphasis on building production-quality systems rather than isolated demonstrations. All code examples use stable APIs, follow modern engineering conventions, and integrate into a unified codebase that grows with the reader. The content prioritizes device-agnostic design, modular architecture, comprehensive testing, and operational excellence-practices that define successful AI platforms in industry environments.>For professionals ready to move beyond basic tutorials and develop genuine expertise in large language model engineering, this book provides the definitive hands-on path forward. Begin building the systems that will define the next generation of artificial intelligence.…