ChatGPT for Beginners: A Quick Introduction to Prompt Engineering is a practical guide designed for anyone looking to unlock the full potential of ChatGPT and other advanced AI language models. This book provides a step-by-step introduction to prompt engineering, helping beginners craft precise, effective prompts that generate high-quality, context-aware responses.

From understanding the evolution of GPT models and the underlying Transformer architecture to mastering hands-on techniques for optimizing token usage, this book covers everything you need to start using AI effectively. You'll learn how to design prompts for real-world applications such as note-taking, content creation, coding assistance, and daily productivity tasks. Practical examples, actionable tips, and detailed walkthroughs make it easy to apply these strategies immediately.

The book also emphasizes the responsible and ethical use of AI, highlighting common pitfalls such as bias, repetitive phrasing, and over-reliance on AI outputs. Additionally, it explores the latest AI tools and integrations, including IDE plugins, productivity applications, and multimodal models for audio and video, giving readers insight into the broader AI ecosystem beyond ChatGPT.

Whether you are a student, professional, or hobbyist, *ChatGPT for Beginners* equips you with the knowledge and skills to harness AI safely, efficiently, and creatively, empowering you to become a confident user and prompt engineer in today’s AI-driven world.

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Agent Blueprint: Multi-Agent System Architecture Fundamentals is the next frontier of AI: distributed, collaborative intelligence. Unlike traditional single-agent models, multi-agent systems (MAS) leverage networks of autonomous agents that communicate, coordinate, and adapt to solve complex problems at scale. This book provides a deep technical dive into MAS architectures, protocols, and production-grade best practices.  

You will learn how to:

- Design hierarchical, flat, and hybrid agent architectures for diverse applications.

- Implement robust communication protocols like ACP, MCP, and FIPA ACL.

- Apply task allocation frameworks, including TDAG and Contract Net Protocol, for dynamic orchestration.

- Build real-world applications such as automated SDLC pipelines, enterprise workflow systems, and real-time infrastructure monitoring.

- Scale multi-agent systems safely and efficiently while maintaining explainability, trust, and reliability.  

Packed with examples and production-ready strategies, this book is ideal for software engineers, AI architects, and researchers seeking to implement intelligent multi-agent systems in real-world environments.

Unlock the full potential of web scraping with modern, AI-driven techniques and automation. The "Modern Web Scraping using AI and automation" is a practical guide that teaches you how to build robust, scalable, and intelligent scraping workflows using Python, Playwright, undetected-chromedriver, and the Chrome DevTools MCP. Learn how to extract structured data from complex, JavaScript-heavy websites, handle anti-bot defenses, and integrate Large Language Models (LLMs) for automated content understanding, summarization, and semantic reasoning.

Packed with hands-on examples, this book covers proxy strategies, CAPTCHA handling, network-level scraping, and serverless deployment, providing you with the tools to transform raw web content into actionable insights. Whether you’re a software engineer, data scientist, or AI enthusiast, this guide shows how to build ethical, compliant, and future-proof scraping pipelines that power analytics, machine learning, and AI applications.

Keywords: web scraping, AI-powered scraping, Playwright, browser automation, undetected-chromedriver, Chrome DevTools MCP, LLM scraping, Python scraping, data extraction, automation pipelines, ethical web scraping

About This Book

Agentic coding is what happens when an AI system stops completing your next line and starts executing a plan: reading your codebase, running commands, testing its own output, and coming back with a result instead of a suggestion. This book teaches that shift from the ground up: what an agent actually is, how to talk to one, how to break a task down into something small enough for it to handle, and how to check its work before you trust it.

Who This Book Is For

You're learning to program and want to build with AI agents from day one, instead of learning "the old way" first and bolting agents on later. Or you already know how to code but haven't worked with an agent that plans and executes multi-step tasks on its own, and want real grounding in how that changes the job.

Either way, if you want to get past typing prompts into a chat window and into an actual working practice, planning tasks, reviewing an agent's output, choosing tools, building small multi-agent systems, this book is written for you. You should be comfortable reading code, even if you're still learning to write much of it yourself. Nothing here assumes you've used an AI coding tool before.

What You’ll Learn

You’ll explore the foundations and practice of agentic coding through examples, walkthroughs, and conceptual discussions:

This book teaches you to work inside that shift, from someone who has never touched an AI coding tool to someone who can plan a task, hand it to an agent, and know exactly what to check before trusting what comes back.

You build real, runnable projects: a document summarizer and a retrieval-augmented FAQ agent against the Anthropic and OpenAI SDKs directly, then a small multi-agent pipeline where a planner, a writer, and a reviewer each run as a separate model call. Along the way you work with Claude Code, OpenAI's Codex CLI, GitHub Copilot, Cursor, and Pi, a minimal open-source agent short enough to read start to finish.

The book is honest about what changes fast and what doesn't. Specific tools, prices, and benchmark standings sit in dated callouts. What's built to last is the practice: decomposing a task, writing a requirements contract, reviewing an agent's output critically, and catching its mistakes before they ship, skills that carry over to whatever tool you're using two years from now.

Part I builds the mental model. Part II turns it into a practice, planning, tool use, memory, and your first working agents. Part III covers the patterns production teams actually run, including a chapter that tells you plainly when you're reading ahead of where you need to be yet.

Remember, Agentic coding doesn't replace all your engineering tasks, treat it as a assistive tool to make your life easy.

Why It Belongs on Leanpub

Leanpub’s iterative publishing model aligns perfectly with the spirit of agentic development. Every specific tool, price, and benchmark number in this book sits in a dated callout for a reason: this space moves faster than any print run. Leanpub's update model is what makes that honesty practical instead of theoretical, buy once, and future updates catch you up on what actually changed instead of leaving you with a book that quietly went stale.

How to Read This Book

Read it front to back if you're starting from zero. Jump to the chapter you need if you already have the basics down.

Open your editor, pick a real task, and put what's in this book to work on it. That's the only way any of it becomes a habit instead of a summary you read once.