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Simpson A: Python Automation For Dummies

Python Automation For Dummies

Introduction 1 About This Book 1 Foolish Assumptions 2 Icons Used in This Book 2 Beyond the Book 2 Where to Go from Here 3 Part 1: Getting Started with Python Automation 5 Chapter 1: Automating with Python 7 Choosing a Programming Language 8 Understanding Python Syntax 10 Getting Python 11 Identifying the hardware requirements 11 Installing Python 12 Chapter 2: Choosing a Code Editor 15 Installing VS Code 16 Installing Python Extensions 18 Creating a Folder for a New Project 19 Opening a project's folder in VS Code 20 Selecting your Python version 21 Opening the Terminal in VS Code 22 Checking your Python version 23 Using Virtual Environments 24 Creating a virtual environment 25 Activating a virtual environment 26 Installing Modules 28 Writing and Running Python Scripts 30 Writing a Python script 30 Running a Python script 32 Chapter 3: Python Basics for Automation 35 Understanding Python Comments 35 Mastering Variables and Data Types 36 Working with numbers 37 Working with text (strings) 38 Deciding true or false with Booleans 39 Using lists 39 Making immutable lists with tuples 40 Defining key-value pairs in dictionaries 40 Leaving things hanging with None 41 Formatting Output 42 Dealing with Dates and Times 45 Manipulating Data with Operators 47 Using arithmetic and string operators 47 Using assignment operators 48 Recognizing other operators 49 Getting Loopy with Loops 51 Looping with for 51 Looping for a while 52 Bailing out of loops 53 Making Decisions 55 Deciding with if else 55 Compacting decisions with a ternary operator 56 Deciding with match 57 Defining Python Functions 58 Defining default values for parameters 60 Using type hints in Python functions 60 Creating Classes and Objects 61 Handling Exceptions 63 Part 2: Automating Common Computer Tasks 67 Chapter 4: Automating Files and Folders 69 Demystifying the Buzzwords 70 Drives, directories, folders, and files 71 Absolute versus relative paths 72 Backslashes in Windows paths 73 Playing It Safe 74 Navigating Folders and Files 74 Organizing Files by Type 81 Using mkdir for subfolders 86 Moving files with shutil 86 Making the script your own 87 Renaming Files in Bulk 87 Renaming files with Python 90 Using the bulk renaming script 91 Chapter 5: Automating File Management 93 Deleting Old and Temporary Files 93 Identifying old files 96 Matching the file pattern 96 Sending files to the trash 97 Using the deletion script safely 97 Backing Up Files 98 Creating folders from Python 101 Copying files with Python 101 Personalizing the backup script 102 Finding and Removing Duplicate Files 103 Calculating a file hash 106 Finding duplicate files 107 Deleting duplicate files 108 Tweaking the find duplicates script 108 Compressing Files 109 Compressing files with Python 112 Setting your compression parameters 113 Decompressing Files 114 Unzipping files with Python 116 Using the decompression script 117 Chapter 6: Automating Image and Video Files 119 Resizing, Rotating, Flipping, and Cropping Images 120 Resizing images 125 Rotating images 126 Flipping images 126 Cropping images 127 Customizing the image processor 128 Converting Image File Types 129 Converting files with Python 131 Personalizing the conversion script 132 Extracting Frames from Video Files 133 Importing modules for video extraction 135 Looping through a video 136 Tweaking the video conversion script 138 Chapter 7: Automating Mouse and Keyboard 139 Granting Permissions on a Mac 140 Moving the Mouse, Clicking, Dragging, and Scrolling 141 Understanding screen coordinates 141 Controlling the mouse speed 141 Stopping a wild mouse 142 Finding the screen locations of things 142 Using mouse control with a specific app 144 Trying out mouse control 144 Typing Text with Python 146 Controlling the typing speed 147 Typing long passages of text 147 Pressing special keys 148 Pressing hotkeys 148 Detecting the operating system 149 Detecting Keystrokes 150 Creating Your Own Keyboard Shortcuts 151 Automating Screenshots 155 Taking screenshots with Python 157 Personalizing the auto screenshot script 158 Chapter 8: Automating the Office 159 Automating Microsoft Word 159 Naming your Word document 162 Defining your Word content 163 Automating Microsoft Excel 164 Specifying your workbook 167 Defining content for your workbook 167 Creating and Opening PDFs 168 Defining content for your PDF 172 Identifying your PDF 172 Watermarking PDFs 173 Creating your watermark image 176 Adapting the script to your needs 177 Part 3: Automating the Internet 179 Chapter 9: Interacting with APIs 181 Obtaining API Keys 181 Safely Storing API Keys 182 Creating a .gitignore file 184 Using an API key in your script 185 Handling JSON Data 185 Parsing and serializing JSON data 187 Reading and writing JSON files 188 Understanding REST APIs 190 Making API requests 191 Parsing API responses 193 Reviewing a Complete REST API Script 194 Chapter 10: Automating the Web 197 Automating Web Browsers 197 Loading drivers for your browser 198 Finding text boxes to fill 200 Automating Filling Forms Online 201 Finding a control 203 Submitting a form with Enter 204 Filling Multiple Text Boxes 204 Clicking a form's Submit button 207 Adapting the script to your needs 207 Filling Text Boxes from a File 208 Chapter 11: Scraping Web Pages 213 Picking the Right Tools for Web Scraping 213 Scraping Links from a Web Page 214 Sending a browser header 215 Parsing a web page 216 Extracting Data from a Web Page 217 Finding elements to scrape 218 Scraping data from the page 221 Automating Data Extraction 222 Determining whether a business is open 225 Scraping stock market data 227 Chapter 12: Automating Email and Text Messages 229 Sending Bulk Email Automatically 229 Collecting account information 230 Creating a .env file 230 Creating your email-sending script 231 Sending HTML mail 234 Putting email recipient addresses in a file 235 Automatically Sending Text Messages 236 Storing SMS account information 237 Defining your recipient list and message 239 Storing recipient numbers 239 Chapter 13: Automating Social Media 243 Acquiring API Keys and Modules 243 Automating Posting 244 Setting up your project 245 Making the script your own 249 Creating Content for Your Posts 249 Tracking Performance Metrics 251 Getting Instagram API access 251 Setting up your script 251 Defining your metrics and timeframe 254 Analyzing Trends 255 Viewing the trends 257 Setting your own keywords and timeframe 257 Part 4: Automating More Advanced Stuff 259 Chapter 14: Scheduling Tasks. 261 Using the Schedule Module 261 Understanding how the schedule module works 263 Scheduling tasks for intervals 264 Using the APScheduler Module 265 Using APScheduler with intervals 267 Using APScheduler with dates and times 268 Automating Python Scripts 271 Running scripts as subprocesses 271 Running scripts as imports 274 Chapter 15: Integrating with Artificial Intelligence 277 Accessing Free AI through an API 277 Warming Up to a Local Chatbot 282 Installing and running Ollama 283 Downloading AI models with Ollama 283 Building a simple local chatbot 284 Creating a Conversational Chatbot 287 Developing an AI Image Generator 290 Showing the generated image onscreen 295 Hitting up Hugging Face 301 Part 5: the Part of Tens 309 Chapter 16: Top Ten Zen of Python Guidelines 311 Beautiful Is Better than Ugly 311 Explicit Is Better than Implicit 314 Using type hints 315 Using comments 316 Handling errors 316 Simple Is Better than Complex 317 Complex Is Better than Complicated 320 Flat Is Better than Nested 321 Flattening nested conditionals 321 Using list comprehension 322 Sparse Is Better than Dense 324 Readability Counts 325 Special Cases Aren't Special Enough to Break the Rule 326 Practicality Beats Purity 328 Errors Should Never Pass Silently 329 Chapter 17: Top Ten Python Error Messages 331 Command Not Found 331 No Module Named 333 SyntaxError 334 NameError 335 TypeError 336 IndexError 337 KeyError 339 AttributeError 340 ModuleNotFoundError 342 FileNotFoundError 343 IndentationError 345 Index 347

On today's web, performance isn't just a nice-to-have--it's essential. A slow or unstable experience drives users away, while a fast, reliable one builds trust and keeps them engaged. Web Performance Engineering in the Age of AI is a comprehensive, hands-on guide for developers, technical leads, and performance engineers focused on delivering high-impact, user-first web experiences. Written by Addy Osmani, director at Google Cloud AI, this book combines deep technical insight with a strong emphasis on usability and real-world effectiveness. Through case studies, modern optimization techniques, and a user-centered approach, you'll learn how to identify bottlenecks, debug performance issues, and apply improvements that make a measurable difference. Grounded in current browser internals and performance metrics, this book prepares you to design and maintain faster, more resilient websites at scale. <ul> <li>Understand how users perceive speed--and why it matters</li> <li>Evaluate and improve performance using key metrics</li> <li>Optimize for stability, responsiveness, and load time</li> <li>Identify and address the most common performance bottlenecks</li> <li>Apply real-world strategies that lead to lasting results</li> </ul>

Mastering Speed and Quality for Ai-Generated Applications

Web Performance Engineering in the Age of AI

Build stable, production-grade systems with Rust. The Rust Programming Language, 3rd Edition, teaches you to write code that the compiler can verify, teams can maintain, and systems can evolve safely over time. Written by longtime Rust community members, this book shows you how to work effectively with Rust’s type system, concurrency model, and tooling, using patterns and idioms chosen for long-term stability. Learn how to: <ul><li>Design programs that communicate their invariants to the compiler</li><li>Use ownership, lifetimes, and traits to model real-world constraints</li><li>Write concurrent and multithreaded code with confidence and clarity</li><li>Build, test, document, and refactor projects using Cargo effectively</li><li>Handle errors explicitly and idiomatically</li><li>Apply expressive pattern matching to simplify complex logic</li></ul> Three substantial project chapters—focusing on a number-guessing game, a command-line tool, and a multithreaded server—demonstrate how these concepts work together in complete, real programs. Whether you’re new to Rust or already using it in production, this book helps you write code that scales safely and makes its guarantees explicit. New to this edition: <ul><li>Complete async programming chapter</li><li>Miri for analyzing unsafe code</li><li>Built on the Rust 2024 Edition</li><li>Modern Rust idioms, tooling, and practices</li></ul>

Build stable, production-grade systems with Rust. The Rust Programming Language, 3rd Edition, teaches you to write code that the compiler can verify, teams can maintain, and systems can evolve safely over time. Written by longtime Rust community members, this book shows you how to work effectively with Rust’s type system, concurrency model, and tooling, using patterns and idioms chosen for long-term stability. Learn how to: <ul><li>Design programs that communicate their invariants to the compiler</li><li>Use ownership, lifetimes, and traits to model real-world constraints</li><li>Write concurrent and multithreaded code with confidence and clarity</li><li>Build, test, document, and refactor projects using Cargo effectively</li><li>Handle errors explicitly and idiomatically</li><li>Apply expressive pattern matching to simplify complex logic</li></ul> Three substantial project chapters—focusing on a number-guessing game, a command-line tool, and a multithreaded server—demonstrate how these concepts work together in complete, real programs. Whether you’re new to Rust or already using it in production, this book helps you write code that scales safely and makes its guarantees explicit. New to this edition: <ul><li>Complete async programming chapter</li><li>Miri for analyzing unsafe code</li><li>Built on the Rust 2024 Edition</li><li>Modern Rust idioms, tooling, and practices</li></ul>

The Rust Programming Language, 3rd Edition

Good architecture is more than just good code--it's clear communication. The C4 Model: Visualizing Software Architecture is a practical guide to creating diagrams that help teams understand, build, and talk about software systems more effectively. Developed by Simon Brown and used by thousands of teams worldwide, the C4 model breaks architecture down into four simple levels (context, container, component, and code), making it easier to see the big picture and the details. Whether you're onboarding a new teammate, presenting to stakeholders, or just trying to make sense of a complex system, this book shows you how to create visuals that actually make an impact. With approachable guidance, real examples, and tips for tailoring diagrams to different audiences, it's a must-have for any developer or architect who wants to make their ideas easier to understand. <ul> <li>Learn how to apply the C4 model from high-level views to low-level details</li> <li>Create diagrams that support collaboration and clarity across teams</li> <li>Understand who each diagram is from and when to use it</li> <li>Go beyond C4 with other useful visual tools like dynamic views and deployment maps</li> <li>Build better conversations around how your software works and why</li> </ul>

Visualizing Software Architecture

The C4 Model

Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'll learn how to build, evaluate, and deploy AutoML models that reduce complexity and accelerate innovation. Author Kerem Tomak shares insights on how to integrate models into end-to-end deployment workflows using popular tools like Kubeflow, MLflow, and Airflow, while exploring cross-platform approaches with Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai. Real-world case studies highlight applications across finance, healthcare, and retail, while chapters on ethics, governance, and agentic AI help future-proof your knowledge. <ul> <li>Build AutoML pipelines for tabular, text, image, and time series data</li> <li>Deploy models with fast, scalable workflows using MLOps best practices</li> <li>Compare and navigate today's leading AutoML platforms</li> <li>Interpret model results and make informed decisions with explainability tools</li> <li>Explore how AutoML leads into next-gen agentic AI systems</li> </ul>

Automating ML Pipelines with Autogluon, Leading Frameworks, and Real-World Integration

Learning Automl

Craft your own dynamic websites Programming websites is a valuable skill that can set you apart. With PHP, MySQL®, &amp; JavaScript® All-In-One For Dummies, you'll learn some of the most common web coding languages. Get a handle on the basics of HTML and CSS, then launch into creating your own fully functioning websites. This edition gives you a primer on concepts like DevOps, containerization, and microservices-big words that will put you in high demand in the workplace! This Dummies All-in-One breaks it down, in terms you can understand. 7 Books Inside... <ul><li>Getting Started with Web Programming</li> <li>Designing with HTML5 and CSS3</li> <li>Coding with JavaScript</li> <li>Programming Dynamic Websites with PHP</li> <li>Using MySQL</li> <li>Creating Object-Oriented Programs</li> <li>Building Websites with PHP Framework</li></ul>

PHP, MySQL, & JavaScript All-In-One For Dummies

This book takes a hands-on, simplified approach to building scalable robotic systems using Terraform for infrastructure and Python for logic. This book eliminates unnecessary complexity, offering practical techniques such as storing binary data in Amazon S3 to trigger AI-based workflows-allowing you to move swiftly from prototype to production.


 


In this book, you'll learn how to set up a ROS2-based camera with servo motors, process images using AI models hosted on AWS, and trigger automated actions with Lambda functions and DynamoDB. Each chapter walks you through real-world implementations, providing Terraform code for deploying serverless components. You'll discover how to manage data streams, run AI-powered object detection, and send movement commands to ROS2 devices-all while maintaining a fully integrated cloud-robotics workflow. Rather than isolating cloud and robotics concepts, this book presents a practical, end-to-end approach with code snippets and best practices for seamless deployment.


By the end of this book, you'll have a repeatable framework for using AWS services and AI agents to control and manage ROS2 devices-without the need for complex IoT setups. Whether you're a robotics enthusiast or a cloud developer, you'll gain the confidence to build scalable, cost-effective, real-time robotics applications that respond to real-world events effortlessly.


 


What you will learn:


 How to set up a simplified serverless pipeline using AWS, Terraform, and Python 

 Explore integrating ROS2 devices with AI agents for real-time event handling 

 How to deploy AI-driven object detection and servo control logic 

 How to manage data flows efficiently with minimal overhead 





Who this book is for:


DevOps engineers, robotics enthusiasts, and AI/ML practitioners . Developers looking to integrate ROS2 devices with cloud services. Software architects seeking practical serverless solutions

This book takes a hands-on, simplified approach to building scalable robotic systems using Terraform for infrastructure and Python for logic. This book eliminates unnecessary complexity, offering practical techniques such as storing binary data in Amazon S3 to trigger AI-based workflows—allowing you to move swiftly from prototype to production.&nbsp;In this book, you’ll learn how to set up a ROS2-based camera with servo motors, process images using AI models hosted on AWS, and trigger automated actions with Lambda functions and DynamoDB. Each chapter walks you through real-world implementations, providing Terraform code for deploying serverless components. You'll discover how to manage data streams, run AI-powered object detection, and send movement commands to ROS2 devices—all while maintaining a fully integrated cloud-robotics workflow. Rather than isolating cloud and robotics concepts, this book presents a practical, end-to-end approach with code snippets and best practices for seamless deployment.By the end of this book, you’ll have a repeatable framework for using AWS services and AI agents to control and manage ROS2 devices—without the need for complex IoT setups. Whether you’re a robotics enthusiast or a cloud developer, you’ll gain the confidence to build scalable, cost-effective, real-time robotics applications that respond to real-world events effortlessly.&nbsp;What you will learn:<ul><li>How to set up a simplified serverless pipeline using AWS, Terraform, and Python &nbsp;</li><li>Explore integrating ROS2 devices with AI agents for real-time event handling &nbsp;</li><li>How to deploy AI-driven object detection and servo control logic &nbsp;</li><li>How to manage data flows efficiently with minimal overhead &nbsp;</li></ul>Who this book is for:DevOps engineers, robotics enthusiasts, and AI/ML practitioners . &nbsp;Developers looking to integrate ROS2 devices with cloud services. Software architects seeking practical serverless solutions &nbsp;&nbsp;

Designing Drone Detection and Defense Systems Under Fire

Building Serverless Robotics with AWS, AI, and ROS 2

Unlock the full potential of quantitative trading with Prescriptions for Quant Traders Using R-a practical, hands-on guide for turning data into trading decisions.This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. Organized into ten structured parts, it delivers step-by-step "prescriptions" using R-script-driven tasks that solve real-world trading problems. Each prescription is designed to be immediately actionable, so readers can move from concept to implementation without guesswork.What makes this book different is its clarity, utility, and its link to online video walk-throughs of R scripts. The text flows seamlessly without interruptions, yet a comprehensive bibliography ensures depth and rigor. Every chapter blends explanation with executable code, enabling traders to:<ul> <li> Design systematic trading strategies in R - and understand why they work.</li> <li> Apply robust statistical and econometric methods to financial data for better predictions.</li> <li> Evaluate risk and performance metrics to refine strategies with confidence.</li> <li> Automate workflows so insights move faster from model to market.</li> </ul>Whether you are a retail trader aiming to compete with professionals, a financial analyst seeking sharper models, or a data scientist expanding into trading, this book bridges the gap between theory and practice.Readers will find special value in the prescription-based approach. Just as a doctor prescribes remedies, these scripts deliver targeted solutions to trading challenges-from portfolio optimization and volatility forecasting to Bayesian inference and machine learning integration.In a marketplace where retail participation continues to grow, Prescriptions for Quant Traders Using R equips you with the knowledge, tools, and confidence to approach trading systematically. The result: deeper insights, stronger discipline, and strategies that stand the test of real-world markets.Whether you're coding your first model or refining a sophisticated strategy, this book provides the prescriptions you need to succeed.

Videos & Scripts

Prescriptions for Quant Traders Using R

A forward-looking primer on how AI models and systems work, the attacks that can disrupt them, and what security measures the industry uses to keep them safe. Artificial intelligence now underpins everything from chatbots to national infrastructure, but with new capability comes new risk. Attacks like prompt injection, data poisoning, and model theft are already targeting the systems we rely on. Practical AI Security is a comprehensive foundation to the field—a 0-to-60 guide to everything you need to know at the intersection of AI and cybersecurity. Drawing real-world experience securing deployed systems, Harriet Farlow demystifies how modern AI works, why it’s vulnerable, and how to protect it. You’ll learn how AI systems differ from machine learning models, why that matters for security, and how to defend both. Through clear explanations, real-world examples, and over 30 hands-on Python demos, you will: <ul><li>Understand how different kinds of machine learning models—from computer vision and language models to signal models—are built and how their architectures create unique vulnerabilities</li><li>Explore how these models are integrated into more autonomous, agentic AI systems, and why deployment introduces new weaknesses and risks</li><li>Identify, exploit, and defend against dozens of weaknesses and attacks across the AI lifecycle, including data poisoning, model theft, and prompt injection</li><li>Use industry frameworks such as OWASP and MITRE ATLAS to threat model different types of AI systems</li><li>Design and execute AI-specific red teaming campaigns, and understand what makes them distinct from traditional security tests</li><li>Examine how AI itself can be weaponized in cybersecurity, including cases where AI attacks other AI</li><li>Build robust frameworks for AI risk management, assurance, and testing</li><li>Bridge technical and policy perspectives to strengthen AI security culture across organizations</li></ul> Covering fundamentals through to advanced topics—from adversarial machine learning and red teaming to risk management, governance, and AI safety—this book turns theory into skill. Even if you don’t think you’re technical now, you’ll finish with practical confidence and a security mindset. Whether you use, build, deploy, or oversee AI, this isn’t niche knowledge—it’s the foundation for defending the technologies that will define the next era of human progress.

Break AI Systems. Then Secure Them. If you’re a security practitioner learning to operate in AI environments, or an ML engineer who needs to understand what adversaries actually do, Practical AI Security gives you the technical foundation the field demands. Built from first principles, this book takes you from how models fail to how they’re exploited to how they’re defended and audited. Every technique includes clear explanations and real-world examples, and you can run the attacks and defenses yourself with over 30 hands-on Python demos. <ul><li>Understand how different kinds of machine learning models create unique vulnerabilities, and explore how these models are integrated into more autonomous, agentic AI systems to introduce new weaknesses and risks.</li><li>Identify, exploit, and defend against dozens of weaknesses and attacks across the AI life cycle, including data poisoning, model theft, and prompt injection.</li><li>Evaluate AI systems for safety failures, bias, and alignment risks using structured benchmarking.</li><li>Threat-model agentic systems, RAG pipelines, and multimodal architectures using MITRE ATLAS, OWASP, and the MAESTRO framework.</li><li>Design and execute AI-specific red teaming campaigns, and understand what makes them distinct from traditional security tests.</li><li>Conduct rapid risk audits and navigate AI governance frameworks for real deployments.</li></ul> Whether you use, build, deploy, or oversee AI, this isn’t niche knowledge—it’s the foundation for defending the technologies that will define the next era of human progress.

A Hands-on Guide to Attacking, Defending, and Securing Modern AI Systems

Practical AI Security

Tcl/Tk: A Developer's Guide, Fourth Edition is an essential resource for computer professionals, from systems administrators to programmers. It covers new Tcl features, expanded Tcl-OO coverage, web technology using Rivet and SQLite, and AI integration with AWS. The book also delves into Tcl's standard tools, multi-faceted nature, and extensibility, making it ideal for developing GUIs, client/server middleware, and web applications. Readers will quickly learn to code in Tcl and extend its capabilities with the inclusion of numerous code examples and case studies. The updated edition includes over 150 pages on the latest Tcl extensions, proven techniques, and tools for effective programming. Extensive code snippets and online tutorials enhance understanding, while case studies provide practical insights. The book also discusses Tcl's role as the hidden "secret sauce" in commercial applications, highlighting its graphics and control infrastructure. With a vibrant user community and evolving API, Tcl/Tk remains a powerful and versatile programming platform for both beginners and experienced programmers.

A Developer's Guide

Tcl/Tk

This concise yet comprehensive guide shows developers and architects how to tackle data integration challenges with MuleSoft. Authors Pooja Kamath and Diane Kesler take you through the process necessary to build robust and scalable integration solutions step-by-step. Supported by real-world use cases, Building Integrations with MuleSoft teaches you to identify and resolve performance bottlenecks, handle errors, and ensure the reliability and scalability of your integration solutions. You'll explore MuleSoft's robust set of connectors and their components, and use them to connect to systems and applications from legacy databases to cloud services. <ul> <li>Ask the right questions to determine your use case, define requirements, decide on reuse versus rebuild, and create sequence and context diagrams </li><li>Master tools like the Anypoint Platform, Anypoint Studio, Code Builder, GitHub, and Maven </li><li>Design APIs with RAML and OAS and craft effective requests and responses </li><li>Write MUnit tests, validate DataWeave expressions, and use Postman Collections </li><li>Deploy Mule applications to CloudHub, use API Manager to create API proxies, and secure APIs with Mule OAuth 2.0 </li><li>Learn message orchestration techniques for routers, transactions, error handling, For Each, Parallel For Each, and batch processing </li></ul>

Integrating Systems and Unifying Data in the Enterprise

Building Integrations with Mulesoft

Dance with the Dragon “A must-read for any reverse engineer.” ­—Max “Libra” Kersten Developed through more than a decade of research within the NSA, Ghidra was created to address some of the most challenging reverse engineering problems faced by the US government. With its open source release, this formerly restricted tool suite is now freely available to cybersecurity practitioners, researchers, and students worldwide. The Ghidra Book provides a clear, practical guide to understanding and using this powerful platform. In addition to introducing core reverse engineering techniques for software and malware analysis, the book explains Ghidra’s key components, features, and support for extensibility and collaborative analysis. Beginning with the fundamentals and progressing to more advanced workflows, you’ll learn how to use Ghidra effectively and adapt it to new challenges. You’ll learn how to: <ul><li>Navigate and interpret a disassembly</li><li>Use Ghidra’s built-in decompiler to expedite analysis</li><li>Analyze unfamiliar and obfuscated binaries</li><li>Extend Ghidra to recognize new data types</li><li>Build custom analyzers, loaders, and processor support modules</li><li>Script Ghidra tasks to automate analysis tasks</li><li>Set up a collaborative reverse engineering environment</li></ul> This fully revised second edition reflects the modern Ghidra platform as it is used in practice, with updated tooling, improved workflows, and hands-on real-world examples. Designed for beginners and experienced users alike, The Ghidra Book prepares you to tackle real reverse engineering problems with confidence. New to this edition: <ul><li>Behavioral analysis with BSim</li><li>Full Python 3 support via PyGhidra</li><li>Enhanced debugging and graphing tools</li><li>Modern container-based deployment</li></ul>

Dance with the Dragon “A must-read for any reverse engineer.” ­—Max “Libra” Kersten Developed through more than a decade of research within the NSA, Ghidra was created to address some of the most challenging reverse engineering problems faced by the US government. With its open source release, this formerly restricted tool suite is now freely available to cybersecurity practitioners, researchers, and students worldwide. The Ghidra Book provides a clear, practical guide to understanding and using this powerful platform. In addition to introducing core reverse engineering techniques for software and malware analysis, the book explains Ghidra’s key components, features, and support for extensibility and collaborative analysis. Beginning with the fundamentals and progressing to more advanced workflows, you’ll learn how to use Ghidra effectively and adapt it to new challenges. You’ll learn how to: <ul><li>Navigate and interpret a disassembly</li><li>Use Ghidra’s built-in decompiler to expedite analysis</li><li>Analyze unfamiliar and obfuscated binaries</li><li>Extend Ghidra to recognize new data types</li><li>Build custom analyzers, loaders, and processor support modules</li><li>Script Ghidra tasks to automate analysis tasks</li><li>Set up a collaborative reverse engineering environment</li></ul> This fully revised second edition reflects the modern Ghidra platform as it is used in practice, with updated tooling, improved workflows, and hands-on real-world examples. Designed for beginners and experienced users alike, The Ghidra Book prepares you to tackle real reverse engineering problems with confidence. New to this edition: <ul><li>Behavioral analysis with BSim</li><li>Full Python 3 support via PyGhidra</li><li>Enhanced debugging and graphing tools</li><li>Modern container-based deployment</li></ul>

The Definitive Guide

The Ghidra Book, 2nd Edition

Software Studies

How the original ELIZA chatbot transformed ideas about AI and society’s response to them. As we reach the 60th anniversary of ELIZA’s public debut, Inventing ELIZA offers the first comprehensive critical analysis of Joseph Weizenbaum’s groundbreaking chatbot system through the lens of critical code studies. Drawing on extensive archival research at MIT, Stanford, and UCLA, this book presents the rediscovered original source code of ELIZA alongside previously unseen scripts that had been missing for decades, revealing a far more sophisticated system than previously documented. Sarah Ciston, David Berry, Anthony Hay, Mark Marino, Peter Millican, Arthur Schwarz, Jeff Shrager, and Peggy Weil trace ELIZA’s development (1965–1968), revealing that Weizenbaum created a chatbot within a conversational programming environment with previously unknown innovations well ahead of its time. Through close reading of both code and paratexts, the book reconstructs ELIZA’s conceptual evolution and situates it within the historical context of early AI development. The book’s website, https://findingeliza.org, includes a faithful recreation of the first chatbot and news about continued research.

How the original ELIZA chatbot transformed ideas about AI and society’s response to them. As we reach the 60th anniversary of ELIZA’s public debut, Inventing ELIZA offers the first comprehensive critical analysis of Joseph Weizenbaum’s groundbreaking chatbot system through the lens of critical code studies. Drawing on extensive archival research at MIT, Stanford, and UCLA, this book presents the rediscovered original source code of ELIZA alongside previously unseen scripts that had been missing for decades, revealing a far more sophisticated system than previously documented. Sarah Ciston, David Berry, Anthony Hay, Mark Marino, Peter Millican, Arthur Schwarz, Jeff Shrager, and Peggy Weil trace ELIZA’s development (1965–1968), revealing that Weizenbaum created a chatbot within a conversational programming environment with previously unknown innovations well ahead of its time. Through close reading of both code and paratexts, the book reconstructs ELIZA’s conceptual evolution and situates it within the historical context of early AI development. The book’s website, https://findingeliza.org, includes a faithful recreation of the first chatbot and news about continued research.

How the First Chatbot Shaped the Future of AI

Inventing ELIZA

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