Quiet line press (10 resultados)

Paperback. Condición: new. Paperback. 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 learnWhat MCP is and why it mattersHow MCP servers communicate using JSON-RPC and transports like stdio and HTTPHow to build tools, resources, and prompts with clear, structured contractsHow to run and test a local MCP server step by stepHow to connect your server to clients like VS Code and MCP InspectorHow to design safe, production-leaning tools with stable outputsHow 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 imagesDetects privacy-sensitive fields such as GPS dataSafely removes metadata with clear, predictable behaviorDemonstrates batch workflows and structured resultsWho this book is forDevelopers and builders exploring MCPAI engineers working with tools and integrationsTechnical creators who prefer learning by doingAnyone 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Build more reliable AI coding workflows without chasing the perfect prompt.AI coding agents can write code, inspect repositories, run tools, and complete multi-step tasks. But useful output is not the same as reliable engineering.Harness Engineering is a practical guide to building the surrounding workflow that helps agents work more safely and predictably. Using a small companion project, agent_harness_lab, the book shows how vague tasks, weak context, loose permissions, and unclear verification can be improved step by step.You will learn how to: - Make repositories easier for agents to navigate- Turn vague requests into executable task briefs- Choose useful context without overwhelming the agent- Set safer tool and permission boundaries- Use plans, checkpoints, tests, and evidence more effectively- Apply human review where it adds the most value- Improve the harness over time by learning from repeated failuresThis is not a prompt-writing guide or a vendor-specific manual. It is a practical framework for software engineers, technical leads, and builders who want AI agents to become part of a more trustworthy engineering workflow.The tools will keep changing. The discipline behind a strong harness will last. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. Part of the Practical Tech Guide Series, this book focuses on what actually matters: how to take a folder of images and turn it into a working model you understand and control. You don't need a machine learning background. If you can run a command and open a browser, you can follow this guide from start to finish. Inside, you'll learn: - What image classification is and how it works (in simple terms)- How to prepare and organise your own image dataset- How to train models locally on your own machine- How to choose the right architecture (PyTorch and TensorFlow)- How to read accuracy, loss, and training results with confidence- How to improve your model with better data and tuning- When to use local training vs cloud or browser-based tools Everything is built around a simple, open-source training tool with a browser-based interface - so you can focus on results, not boilerplate code. No deep math. No unnecessary theory. No black boxes. Whether you're experimenting with AI, building a small project, or working with sensitive data you don't want to upload to the cloud, this guide gives you a clear, practical path. If you want to train AI image models locally - and actually understand what you're doing - this book is for you. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. LLMs are changing how people write, learn, code, search, and build software, but they are often explained with too much hype or too much jargon.LLMs Explained is a practical, beginner-friendly guide to understanding large language models and using them wisely.This book explains what LLMs are, how they fit into the broader AI landscape, what they do well, where they struggle, and how to work with them in real-world tasks and applications. Instead of focusing on heavy math or vendor-specific tricks, it gives you a clear map of the ideas behind modern AI tools: prompts, tokens, context windows, hallucinations, structured outputs, retrieval, embeddings, tool use, MCP, skills, agents, privacy, safety, and evaluation.Inside, you will learn how to: understand what an LLM is and how it generates responsessee how LLMs relate to AI, generative AI, and modern assistant productswrite clearer prompts and instructionsrecognize where LLMs are useful and where they are riskyunderstand tokens, context, memory, and model limitsreduce hallucinations and verify important answersuse structured outputs for more reliable AI workflowsunderstand embeddings, semantic search, retrieval, and RAGsee how tools, function calling, MCP, skills, and agents fit togetherthink about privacy, security, cost, and responsible useWhether you are a developer, product manager, student, writer, creator, or simply curious, this book gives you a practical foundation for understanding LLMs without getting lost in buzzwords. LLMs Explained is a clear starting point for anyone who wants to understand modern AI tools and use them with more confidence, clarity, and care. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Build more reliable AI coding workflows without chasing the perfect prompt.AI coding agents can write code, inspect repositories, run tools, and complete multi-step tasks. But useful output is not the same as reliable engineering.Harness Engineering is a practical guide to building the surrounding workflow that helps agents work more safely and predictably. Using a small companion project, agent_harness_lab, the book shows how vague tasks, weak context, loose permissions, and unclear verification can be improved step by step.You will learn how to: - Make repositories easier for agents to navigate- Turn vague requests into executable task briefs- Choose useful context without overwhelming the agent- Set safer tool and permission boundaries- Use plans, checkpoints, tests, and evidence more effectively- Apply human review where it adds the most value- Improve the harness over time by learning from repeated failuresThis is not a prompt-writing guide or a vendor-specific manual. It is a practical framework for software engineers, technical leads, and builders who want AI agents to become part of a more trustworthy engineering workflow.The tools will keep changing. The discipline behind a strong harness will last. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. 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 learnWhat an AI agent is - and what it is notHow agents differ from tools, workflows, chatbots, and MCP serversHow to structure a small goal-driven agent loop in PythonHow to turn plain-language intent into structured plansHow to use tools and capability layers safelyHow to keep read-only actions separate from mutating actionsHow approval gates and human-in-the-loop design make agents more trustworthyHow structured tool results improve agent behaviorHow to test, evaluate, trace, and debug practical agent workflowsHow 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 sharingDetect privacy-sensitive metadata such as GPS fieldsDecide when cleanup may be usefulAsk for approval before writing filesRun cleanup workflows through a clear safety boundaryUse either a direct local adapter or an MCP stdio capability layerSummarize results for humans while preserving structured outputsWho this book is forThis guide is for: Developers and builders exploring AI agentsTechnical creators who prefer learning by doingAI builders who want practical agent patterns without hypeReaders who want a clear companion to an MCP server projectAnyone 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. LLMs are changing how people write, learn, code, search, and build software, but they are often explained with too much hype or too much jargon.LLMs Explained is a practical, beginner-friendly guide to understanding large language models and using them wisely.This book explains what LLMs are, how they fit into the broader AI landscape, what they do well, where they struggle, and how to work with them in real-world tasks and applications. Instead of focusing on heavy math or vendor-specific tricks, it gives you a clear map of the ideas behind modern AI tools: prompts, tokens, context windows, hallucinations, structured outputs, retrieval, embeddings, tool use, MCP, skills, agents, privacy, safety, and evaluation.Inside, you will learn how to: understand what an LLM is and how it generates responsessee how LLMs relate to AI, generative AI, and modern assistant productswrite clearer prompts and instructionsrecognize where LLMs are useful and where they are riskyunderstand tokens, context, memory, and model limitsreduce hallucinations and verify important answersuse structured outputs for more reliable AI workflowsunderstand embeddings, semantic search, retrieval, and RAGsee how tools, function calling, MCP, skills, and agents fit togetherthink about privacy, security, cost, and responsible useWhether you are a developer, product manager, student, writer, creator, or simply curious, this book gives you a practical foundation for understanding LLMs without getting lost in buzzwords. LLMs Explained is a clear starting point for anyone who wants to understand modern AI tools and use them with more confidence, clarity, and care. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Part of the Practical Tech Guide Series, this book focuses on what actually matters: how to take a folder of images and turn it into a working model you understand and control. You don't need a machine learning background. If you can run a command and open a browser, you can follow this guide from start to finish. Inside, you'll learn: - What image classification is and how it works (in simple terms)- How to prepare and organise your own image dataset- How to train models locally on your own machine- How to choose the right architecture (PyTorch and TensorFlow)- How to read accuracy, loss, and training results with confidence- How to improve your model with better data and tuning- When to use local training vs cloud or browser-based tools Everything is built around a simple, open-source training tool with a browser-based interface - so you can focus on results, not boilerplate code. No deep math. No unnecessary theory. No black boxes. Whether you're experimenting with AI, building a small project, or working with sensitive data you don't want to upload to the cloud, this guide gives you a clear, practical path. If you want to train AI image models locally - and actually understand what you're doing - this book is for you. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. 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 learnWhat MCP is and why it mattersHow MCP servers communicate using JSON-RPC and transports like stdio and HTTPHow to build tools, resources, and prompts with clear, structured contractsHow to run and test a local MCP server step by stepHow to connect your server to clients like VS Code and MCP InspectorHow to design safe, production-leaning tools with stable outputsHow 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 imagesDetects privacy-sensitive fields such as GPS dataSafely removes metadata with clear, predictable behaviorDemonstrates batch workflows and structured resultsWho this book is forDevelopers and builders exploring MCPAI engineers working with tools and integrationsTechnical creators who prefer learning by doingAnyone 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…