Blue j lion (8 risultati)

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. Neuware - AI applications are useful, but they can also become unsafe in subtle ways.A normal application already needs authentication, authorization, input validation, logging, and careful handling of secrets. AI applications add another layer of risk: prompts can be manipulated, retrieved documents can overshare, tools can be exposed too broadly, and generated outputs can look safe while breaking policy.Securing AI Applications is a hands-on guide to building safer AI workflows.Instead of treating AI security as abstract theory, this book follows a small companion project: ai_security_lab, a fictional support-assistant application with support tickets, customer records, policy documents, uploaded content, sensitive tool actions, and audit logs. The project starts with weak boundaries and hardens them step by step.Inside, you will learn how to: - recognize prompt injection and untrusted input- treat retrieved documents as data, not instructions- reduce sensitive data before it reaches the AI workflow- design safer tool contracts and permission checks- add human approval gates for higher-risk actions- validate structured outputs before using them downstream- use audit logs to make AI actions reviewable- test security controls instead of leaving them as advice- prepare a practical deployment checklist for AI featuresThis book is not about securing a model in isolation. It is about securing the workflow around the model: prompts, retrieval, tools, outputs, approvals, logs, and boundaries.If you are building AI features into real applications, this guide will help you think clearly about what can go wrong - and how to make those risks visible, testable, and easier to control.…

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. Neuware - Ancient strategy. Modern software problems. Practical engineering judgment.Software teams rarely face neat textbook problems.They face risky migrations, fragile releases, noisy incidents, unclear ownership, vendor hype, technical debt, security pressure, and roadmaps shaped by both code and humans being human.The 36 Stratagems for Software Teams takes a classic Chinese set of strategic patterns and reimagines them for modern engineering, product, platform, security, and delivery work.This is not a book about manipulation, office politics, or pretending sprint planning is an ancient battlefield. Instead, it uses the Thirty-Six Stratagems as a practical lens for recognizing patterns that software teams already deal with every day: - when to move directly and when to move indirectly- when to wait, contain, simplify, or retreat- how to handle migrations without creating unnecessary disruption- how to use prototypes, feature flags, rollback paths, and stable interfaces- how to recognize hidden risks in tools, vendors, dependencies, and architecture- how to turn incidents, uncertainty, and technical debt into better decisionsEach chapter explains one stratagem in plain language, then connects it to a realistic software-team situation using examples from architecture, platform work, delivery, operations, security, and leadership.You will find practical lessons on: - legacy modernization- release planning- incident response- dependency risk- platform adoption- technical debt- security controls- team coordination- product and engineering tradeoffs- knowing when to stop, rollback, or walk awayPart of The Practical Tech Guide Series, this book is written for engineers, tech leads, engineering managers, product managers, platform teams, security teams, and founders who want a sharper way to think about messy technical decisions.…

Taschenbuch. Condizione: Neu. Neuware - Build practical AI agents - without the hype.AI agents are everywhere in today's tech conversations, but many explanations are vague, overly ambitious, or buried in abstraction.This book takes a practice-first approach.You will learn how practical AI agents work by building around a real Python project: a small EXIF privacy agent that reviews image metadata, decides when cleanup may be needed, asks for approval before file changes, and summarizes results in plain language.Instead of chasing vague autonomy, this guide focuses on the parts that actually matter: goals, planning, tools, safety boundaries, approvals, structured results, and feedback loops.What you will learn- What an AI agent is - and what it is not- How agents differ from tools, workflows, chatbots, and MCP servers- How to structure a small goal-driven agent loop in Python- How to turn plain-language intent into structured plans- How to use tools and capability layers safely- How to keep read-only actions separate from mutating actions- How approval gates and human-in-the-loop design make agents more trustworthy- How structured tool results improve agent behavior- How to test, evaluate, trace, and debug practical agent workflows- How to add model-backed planning and ReAct-style reasoning without giving up safety boundariesBuilt around a real projectThis book is centred on exif-privacy-agent, a practical Python agent that can: - Review one image or a folder before sharing- Detect privacy-sensitive metadata such as GPS fields- Decide when cleanup may be useful- Ask for approval before writing files- Run cleanup workflows through a clear safety boundary- Use either a direct local adapter or an MCP stdio capability layer- Summarize results for humans while preserving structured outputsWho this book is forThis guide is for: - Developers and builders exploring AI agents- Technical creators who prefer learning by doing- AI builders who want practical agent patterns without hype- Readers who want a clear companion to an MCP server project- Anyone trying to understand how planning, tools, approvals, and safety fit togetherThis is not a research-heavy survey of autonomous systems.It is not a book about agent swarms or vague AI magic.It is a focused, hands-on guide to building practical AI agents that are small enough to understand, useful enough to run, and safe enough to trust.…

Taschenbuch. Condizione: Neu. Neuware - Build real MCP servers - without the confusion.Model Context Protocol (MCP) is quickly becoming a standard way for AI systems to connect with tools, data, and workflows. But most explanations are abstract, fragmented, or overly complex.This book takes a practice-first approach.You will learn MCP through a real Python project: a working MCP server that inspects and removes EXIF metadata from images. Along the way, you will see how MCP works in practice: how clients discover capabilities, how structured calls flow through the system, and how to design servers that are safe, predictable, and useful.What you will learn- What MCP is and why it matters- How MCP servers communicate using JSON-RPC and transports like stdio and HTTP- How to build tools, resources, and prompts with clear, structured contracts- How to run and test a local MCP server step by step- How to connect your server to clients like VS Code and MCP Inspector- How to design safe, production-leaning tools with stable outputs- How to extend a real project into your own MCP servicesBuilt around a real projectThis book is centered on a practical, open-source MCP server that: - Reads and analyzes EXIF metadata from images- Detects privacy-sensitive fields such as GPS data- Safely removes metadata with clear, predictable behavior- Demonstrates batch workflows and structured resultsWho this book is for- Developers and builders exploring MCP- AI engineers working with tools and integrations- Technical creators who prefer learning by doing- Anyone looking for a clear, concrete example of a real MCP serverThis is not a deep Python tutorial or a computer vision book.It is a focused, hands-on guide to building MCP servers that actually work.…

Taschenbuch. Condizione: Neu. Neuware - Build practical RAG applications without getting lost in vendor noise.Retrieval-Augmented Generation, or RAG, is one of the most useful patterns for building AI applications that answer from real documents instead of unsupported guesses. But many explanations jump too quickly into tools, vector databases, and frameworks before explaining how the system actually works.This book takes a practical, project-first approach.You will build docs_rag_app, a small local RAG application in Python that answers questions over a folder of documents. Step by step, you will learn how to load documents, split them into chunks, retrieve relevant evidence, generate grounded answers, add citations, evaluate quality, and debug bad results.Along the way, the book explains the ideas behind the code: - why LLMs need retrieval- how RAG differs from fine-tuning, long context, memory, and tool calls- how chunking affects answer quality- how lexical and vector retrieval work- why reranking improves difficult cases- how citations and abstention reduce hallucination risk- how to evaluate and refresh a RAG system- why retrieved text should be treated as evidence, not trusted instructionsThe goal is not to build the largest possible AI system. The goal is to build a small RAG application that is easy to inspect, easy to debug, and strong enough to improve.If you can read basic Python and want to understand how retrieval changes AI application design, this guide gives you a clear path from first working prototype to practical local RAG app.…

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