Glossary
A
A2A (Agent 2 Agent) Protocol for enabling communication and interaction between different AI agents.
Action In reinforcement learning, a decision or move made by an agent.
AGI (Artificial General Intelligence) A theoretical form of AI that would have human-like general intelligence, capable of understanding, learning, and applying knowledge across a wide range of tasks. Marketing hype often surrounds AGI claims, though true AGI capabilities remain unproven.
Agent In reinforcement learning, the learner or decision-maker that interacts with the environment.
AI (Artificial Intelligence) The simulation of human intelligence processes by machines, especially computer systems. The book focuses on practical applications of AI in software engineering.
AI Hallucination When AI generates information that appears plausible but is actually incorrect or made up. AI can create APIs that don't exist, generate code with runtime bugs, or ignore specific requests entirely.
AI Input vs Output A methodology where developers use AI for input (research, learning, inspiration) while maintaining critical review responsibility rather than blindly accepting AI-generated output without evaluation.
AI Scams Deceptive practices involving AI technology, including false AGI claims, wrapper solutions marketed as novel AI applications, and misleading marketing around AI capabilities.
Agents Autonomous or semi-autonomous systems that can perform tasks, make decisions, and interact with environments using AI capabilities.
Agentic Behavior Behavior exhibited by agents that allows them to act autonomously in pursuit of defined goals, making decisions based on environment state.
Aggregating Agent pattern involving combining multiple data sources or results into a unified response or output.
AlphaGo Game-playing AI system using reinforcement learning.
Amazon Q AWS coding agent that provides AI-powered assistance for software development tasks.
Amoeba Age Early developmental stage of LLMs described by Yuval Harari, characterized by basic capabilities that will improve over time.
Anomaly Detection Identifying unusual data points that deviate from normal patterns, commonly used in fraud detection and system monitoring.
API Key Authentication token required to access AI services programmatically for production deployment.
Attention Mechanisms Core component of transformer architecture enabling models to focus on relevant parts of input when processing sequences.
AudioLM Model developed by Google that can generate high-quality audio samples from text prompts.
Auto-complete on Steroids Description of Generative AI systems as advanced prediction engines based on pattern matching.
Autonomous Driving Application area for reinforcement learning in self-driving vehicles.
Automated Testing Practice of using software to test software automatically, replacing manual testing.
Attack Vector Potential security vulnerability pathway that could be exploited by malicious actors, especially relevant for MCP security.
B
Bash Orchestration Running Claude Code as a Unix/Linux process enabling pipeline and automation workflows.
Benchmark Gaming Practice where AI models exploit benchmark loopholes to achieve higher scores without genuinely improving capabilities.
BERT Large Language Model built upon the Transformer architecture.
Build Process Automated compilation and deployment process that can be monitored and optimized with AI assistance.
Bash Mode A Claude Code feature enabling direct bash command execution for system operations, also known as Interactive Mode.
bump-version.sh Script used to increment version numbers in the book publication process.
C
Caching Agent pattern for storing and reusing frequently accessed data or results to improve performance and reduce redundant processing.
cargo Rust's package manager and build tool, used to install mdbook.
Catastrophic Forgetting Challenge in fine-tuning where a model loses previously learned knowledge when adapting to new tasks.
ccstatusline MCP tool for customizing the Claude Code status line display.
Changelog Document tracking changes, additions, and fixes across software versions, can be automated using AI by analyzing git history.
Change and Adaptation The necessity for engineers and organizations to adapt to AI-driven disruption as AI becomes increasingly integrated into software development.
Chroma Vector database for storing and querying embeddings.
Cisco MCP Scanner Open source security tool for scanning and analyzing MCP servers for vulnerabilities.
.claudeignore File specifying which files Claude Code should not read, similar to.gitignore for version control.
CLAUDE.md Global configuration file for Claude Code behavior, containing project-specific instructions and preferences.
Claude Code A specific AI tool/platform (Anthropic's Claude used for coding tasks) covered extensively in Chapter 5.
Claude Opus Anthropic's LLM model.
Claude Skills Anthropic's approach to building coding agents by bundling text and scripts together to create specialized capabilities.
Claude Sonnet 4.5 Anthropic's LLM model with 200,000 token context window.
Claude Sonnet Corp Enterprise version of Claude with 1,000,000 token context window.
CLI Agents Command-line interface based coding agents that run directly on local machines.
Classification A Traditional AI/ML task involving categorizing data into predefined classes or categories based on learned patterns.
Clever Hans Effect A phenomenon where an observer unconsciously gives cues to a subject, leading to apparent intelligent behavior that is actually based on picking up subtle hints rather than true understanding.
Clustering A Traditional AI/ML unsupervised learning task involving grouping similar data points together without predefined labels.
CI/CD (Continuous Integration/Continuous Deployment) Automated software development practices for building, testing, and deploying code.
Code Review Critical process of examining AI-generated or any code for correctness, security, and alignment with requirements before deployment. Essential when using AI.
Codex OpenAI's coding agent available in both web sandbox and CLI versions, using specialized models for code generation.
Coding Agents AI agents specifically designed or trained to generate, modify, and work with software code.
Coding Dojo Practice environment where engineers work without AI using TDD, forcing manual skill development and maintaining proficiency.
Compact (Claude Code) Command that summarizes and reduces context window usage by condensing conversation history.
Configuration Ownership Responsibility for maintaining and managing configuration files and settings.
Container Registry Repository for storing and distributing container images, important for secure MCP usage.
Contrarian Feedback Learning technique where AI challenges your thinking to identify weaknesses.
Context Engineering Modern term focusing on providing the right context to LLMs to get the best possible response, replacing the older term "prompt engineering".
Context Window The amount of text/tokens an LLM can process at once. Larger context windows allow for processing more information simultaneously.
Context7 Service providing up-to-date library documentation to address LLMs' outdated training data, supporting over 49,317 libraries.
Cool Down Period Limitation period in subscription-based plans when token budget is exhausted.
Core Business Logic Critical business logic and intellectual property that should never be created through "vibe coding" due to quality and proprietary concerns.
Cosine Similarity Similarity measure used to compare embeddings based on the cosine of the angle between vectors.
Critical Thinking Ability to analyze information objectively and make reasoned judgments, essential when using AI.
Cypress End-to-end testing framework for web applications, can be used with AI to generate automated tests.
Custom Agents Agents specifically built or configured for particular use cases or organizations, stored in ~/.claude/agents/ directory.
Custom Commands Specific commands or workflows configured within Claude Code or similar tools for specialized tasks.
D
DALL-E OpenAI's model that generates images from textual descriptions using transformer architecture and GANs.
Data Structures and Algorithms Fundamental computer science concepts for organizing and processing data efficiently.
DBSCAN Density-Based Spatial Clustering of Applications with Noise, an algorithm for clustering that can identify outliers.
Decision Trees Machine learning algorithm using tree-like model of decisions for both classification and regression tasks.
Debugging with AI using Images Technique for troubleshooting code issues by providing AI with visual representations (screenshots/images) of problems.
Decision Criteria Framework for determining when and how to use AI effectively in different scenarios.
Devil's Advocate Critical thinking approach where AI challenges assumptions and arguments to identify weaknesses.
Determinism Property of producing identical outputs for identical inputs, which AI lacks due to its probabilistic nature.
Diffusion Models Advanced approach for video generation.
Dimensionality Reduction A Traditional AI/ML technique for reducing the number of variables/features in data while preserving meaningful information. Examples include PCA.
Disruption The transformative impact of AI on industries and engineering practices, similar to impacts of internet and mobile phones.
Documentation Using AI to generate and maintain code documentation, changelogs, and knowledge bases.
E
Early Stop Technique used in fine-tuning to prevent overfitting by stopping training before the model over-learns.
ElevenLabs Company providing APIs to generate high-quality speech from text using advanced neural network models.
End-to-End Tests Tests that validate entire application workflows from user perspective.
Embeddings Numerical vector representations of text, images, or other data that capture semantic meaning. Essential for RAG and vector databases. Can represent words, sentences, or entire documents.
Engineering with AI The practice of applying AI tools and methodologies to improve software development processes.
Enterprise Integration Patterns (EIP) Design patterns by Gregor Hohpe and Bobby Woolf that AI agent patterns derive from.
Environment In reinforcement learning, the external system with which the agent interacts.
Euclidean Distance Similarity measure used to compare embeddings based on the straight-line distance between vectors.
Evo by Snyk Commercial MCP scanning security solution for analyzing MCP servers.
Explore Agent Claude Code specialized sub-agent for fast codebase exploration.
F
faker.js JavaScript library for generating realistic test data, used in integration testing to create mock data for various scenarios.
Feed Phase First phase in RAG pattern where documents are ingested and converted to embeddings for vector database storage.
Few-Shot Training technique providing a small number of examples to help models learn tasks.
Few-Shot Examples Training technique providing examples to help models perform tasks better without extensive retraining.
Filtering Agent pattern for removing unwanted data or responses before or after LLM processing.
Fine Tuning Process of training a pre-trained model on specific domain data to adapt it to particular use cases without full retraining.
G
GANs (Generative Adversarial Networks) Neural network architecture used in image generation models like DALL-E.
Gatling Stress testing and load testing framework for performance evaluation.
Gaussian Mixture Models Clustering algorithm that assumes data points are generated from a mixture of Gaussian distributions.
Gemini Google's LLM model used in various coding agents including Jules and Gemini CLI.
Gemini 2.0 Flash Google's LLM model with 1,000,000 token context window.
Gemini 2.0 Pro Google's LLM model with 2,000,000 token context window.
General-Purpose Agent Claude Code sub-agent for multi-step task handling.
Generative AI AI systems that can generate new content, including text, code, images, and other media. The book focuses on using Generative AI for software engineering tasks. Works through predicting next tokens/sequences.
Git Archaeology for Troubleshooting Technique using git history to understand code evolution and diagnose issues.
GitHub Actions CI/CD automation platform used for workflow automation, including book publishing.
GitHub Copilot One of the first coding agents, integrated into VSCode and other IDEs, supporting multiple LLM models.
GitHub Pages Static site hosting service used to publish websites and books online.
Google Jules Web sandbox coding agent by Google backed by Gemini LLM models.
GPT-3 Large Language Model built upon the Transformer architecture.
GPT-3.5 OpenAI's LLM model with 4,096 token context window.
GPT-4 OpenAI's LLM model with 8,192 token context window.
GPT-4-turbo OpenAI's LLM model with 128,000 token context window.
GPT-5 OpenAI's latest LLM model.
Gradient Boosting Machine learning ensemble technique that builds models sequentially to correct errors of previous models.
Grok 3 XAI's LLM model with 1,000,000 token context window.
Grok 4 XAI's LLM model.
Grok 4 Fast XAI's LLM model with 2,000,000 token context window.
H
Headless Mode Running Claude Code via command line without interactive interface for automation and scripting.
Hierarchical Clustering Clustering algorithm that builds a hierarchy of clusters using a tree-like structure.
Hooks (Claude Code) Event-driven automation triggers that execute scripts on specific events in Claude Code, allowing custom workflows.
HumanEval Software engineering benchmark for evaluating AI coding capabilities.
Hybrid Approach Video generation technique that can generate videos in seconds.
Hyperparameter Tuning Process of adjusting parameters in fine-tuning to optimize model performance.
I
IDE-based Agents AI coding assistants integrated into development environments like VSCode.
Idempotent Operations that produce the same result regardless of how many times they are executed, easier to test.
Incompetence Illumination Teaching technique where AI deliberately mixes wrong and correct answers to train critical thinking.
Integration Tests Tests that verify how different components work together, requiring test data and infrastructure setup.
Intellectual Honesty Detector AI teaching method that catches shallow understanding by asking learners to explain concepts back.
Internal Shared Libraries Reusable code libraries shared across projects within an organization, often problematic for ownership.
Image Generation Generative AI capability for creating visual content from text descriptions or other inputs.
Interactive Mode Claude Code feature enabling direct bash command execution, also known as Bash Mode.
Inventory (Migrations) Process of cataloging and assessing existing systems, code, and infrastructure before planning migrations.
J
Jenkins Automation server for CI/CD pipelines, mentioned as tech asset requiring ownership.
Jest JavaScript testing framework.
JetBrains Company producing IDEs like IntelliJ IDEA that integrate with AI coding assistants.
JUnit Java testing framework.
K
K-Means Clustering Popular clustering algorithm that partitions data into k clusters by minimizing variance within each cluster.
K6 Load testing framework for performance evaluation.
Kubernetes Container orchestration platform, mentioned in troubleshooting and ownership contexts.
Kiro AWS coding agent, a fork of VSCode implementing Spec Driven Development approach.
Knowledge Base Generation Using AI to create and maintain comprehensive documentation systems.
L
LDA (Linear Discriminant Analysis) Dimensionality reduction technique that finds linear combinations of features for classification.
Learning from AI Process of using AI as an educational tool while maintaining critical thinking and verification.
Leftovers Resources or code remaining after migrations that need cleanup.
Linear Regression Fundamental regression algorithm modeling relationship between variables using a linear equation.
LLaMA 3 Meta's LLM model with 8,192 token context window.
LLaMA 4 Meta's LLM model.
LLM (Large Language Model) A type of AI model trained on massive amounts of text data that can understand and generate human-like text. Examples include GPT, Claude, and other similar models. LLMs work by predicting the next sequence of tokens. Cannot truly "think" despite marketing claims.
llms.txt / llms-full.txt Text files at website roots helping LLMs navigate and understand site content, providing structured documentation.
Logistic Regression Classification algorithm using logistic function to model probability of categorical outcomes.
Loss Function Mathematical function optimized during model training to minimize prediction errors.
LSP (Language Server Protocol) Standard protocol that MCP is compared to for understanding its architecture and functionality.
LLMs as Slot Machines Concept by Cory Doctorow describing LLMs as fundamentally random/probabilistic systems predicting next tokens, not deterministic reasoning engines.
M
Markdown Lightweight markup language used to write documentation and book content.
mdbook A Rust-based tool for creating books from markdown files, featuring built-in search, unique URLs per page, and syntax highlighting for code snippets.
Make-A-Track Meta's model that generates music tracks from text descriptions.
Marketing and AI The often misleading promotion of AI capabilities, including false AGI claims and overstated application benefits.
Marketing Agent (Claude Code) Specialized agent for translating technical content to plain language.
MCP (Model Context Protocol) A protocol for connecting AI models to external tools and data sources, enabling extended capabilities. Created by Anthropic in 2024.
MCP Client Component within AI host that connects to MCP servers.
MCP Guardrails Security practices for safely using MCP servers including vetting, scanning, and isolation.
MCP Host The AI agent or tool that contains MCP clients.
MCP Scanner Security tool for analyzing MCP servers for vulnerabilities.
MCP Server External service providing tools and data to AI models via Model Context Protocol.
Mean Shift Clustering Clustering algorithm that finds dense regions by shifting data points toward mode of distribution.
MidJourney Independent research lab's model for generating visually appealing and artistic images from text prompts.
Migrations Process of moving systems, code, libraries, or data from one platform, language, or infrastructure to another.
Migrations in Phases Structured approach to performing migrations incrementally rather than all at once.
Milvus Vector database for storing and querying embeddings.
Mirror on Steroids Concept that AI amplifies existing abilities: good engineers become better with AI; poor engineers become worse.
MoCoGAN Model that separates motion and content to generate videos with coherent motion.
Mocks Test objects that verify interactions and method calls during testing.
Model In reinforcement learning, a representation of the environment that the agent uses to predict the next state and reward.
N
Naive Bayes Classification algorithm based on Bayes' theorem with strong independence assumptions between features.
Non-idempotent Operations that produce different results when executed multiple times, requiring special testing setup.
npm Node.js package manager, mentioned in bash mode execution.
Natural Language Processing (NLP) Field revolutionized by the Transformer architecture for processing and understanding human language.
O
Onboarding Process of integrating new engineers into teams and codebases, enhanced by AI as a private tutor.
OpenAI Company that provides LLM models and coding agents.
OpenCode Open source coding agent that works with multiple LLM models.
OpenAPI API documentation specification format that AI coding agents can read to generate tests and understand API structure.
Orchestration Agent pattern for coordinating multiple AI operations or tools in sequence or parallel.
Orphaned Resources Infrastructure or code assets without clear ownership or maintenance.
Overfitting Challenge in fine-tuning where a model learns training data too specifically and loses generalization ability.
P
Payable Tokens Tokens that incur cost in API-based pricing plans.
PCA (Principal Component Analysis) Dimensionality reduction technique that identifies principal components explaining maximum variance in data.
pgvector PostgreSQL extension for vector database functionality.
PII (Personally Identifiable Information) Sensitive personal data that should be excluded from AI processing for privacy and security.
Pinecone Vector database for storing and querying embeddings.
Plan Agent Claude Code specialized agent for planning tasks.
Playwright End-to-end testing framework for web applications.
podman Container management tool, mentioned as docker-compose alternative.
Policy In reinforcement learning, a strategy or rule that the agent uses to make decisions.
Postgres MCP MCP server for reading data from PostgreSQL database tables in plain English.
PR (Pull Request) Proposed code changes submitted for review before merging into main codebase.
Precision and Reproducibility Challenge with AI systems: inability to guarantee identical outputs for identical inputs due to probabilistic nature.
Private Teacher AI acting as personalized instructor providing tailored learning experiences.
Pre-trained Model Model that has already acquired knowledge during initial training phase, used as starting point for fine-tuning.
Proficiency Level of skill and expertise required to perform tasks effectively without constant reference.
Prompt Engineering Craft of writing effective instructions for AI models to achieve desired outcomes, largely replaced by context engineering.
Prompt Library Collection of pre-written prompts demonstrating AI capabilities and best practices.
Prompts Instructions or queries given to AI systems to generate desired outputs.
Proof of Concepts (POCs) Small-scale implementations to test feasibility and validate ideas before full-scale development.
Proof Reader Using AI to check spelling, grammar, and document quality in written content.
R
RAG (Retrieval-Augmented Generation) Technique combining document retrieval with LLM generation, allowing AI to cite and incorporate external information into responses. Reduces costs, mitigates hallucinations, and provides up-to-date information.
Random Forest Ensemble learning method using multiple decision trees for classification and regression.
Regression Tests Tests ensuring existing functionality still works after changes.
Randomness in AI Inherent probabilistic nature of LLMs making outputs non-deterministic even with identical inputs.
Regression Traditional ML task of predicting continuous numerical values based on input features.
Reinforcement Learning (RL) Machine learning paradigm where agents learn by interacting with environments and receiving rewards or penalties.
Respect in Software Engineering Professional principle that code should be reviewed and understood by creators before sharing/deploying.
Responsible AI Usage Using AI as input/research tool while maintaining human judgment, code review, and verification responsibilities.
Retrieval Phase Second phase in RAG pattern where queries are converted to embeddings to search vector database for relevant documents.
Role Playing AI capability to assume different personas (architect, security expert, marketing specialist, etc.) to provide varied perspectives.
Reward In reinforcement learning, a scalar feedback signal that indicates how we'll the agent is doing.
Robotics Application area for reinforcement learning.
Routing Agent pattern for directing requests to appropriate services or models based on content or conditions.
Rust Systems programming language used for mdbook and mentioned in migration contexts.
S
Sandbox-Based Agents AI coding agents that operate in isolated environments separate from user's machine for security.
Sandboxing Running code in isolated environments for security, though noted as poor developer experience.
SDD (Spec Driven Development) Development approach implemented by AWS Kiro where specifications drive implementation.
Security and MCPs Considerations for safely integrating external tools and protocols with AI systems to prevent unauthorized access or malicious operations.
Selenium Browser automation framework used for testing web applications.
Self-Attention Mechanism Key innovation of Transformers that allows models to weigh the importance of different words in a sentence relative to each other.
Semantic Meaning The meaning captured by embeddings that allows for effective text comparisons.
Semi-Supervised Learning Machine learning paradigm combining labeled and unlabeled data for training.
Sentiment Analysis Analyzing emotional tone and intent in text, applicable to emails, customer feedback, and communication review.
Slack MCP MCP server for sending messages to Slack teams.
Smoke Test High-level test verifying critical system functionality without exhaustive coverage.
Socratic Interrogation Teaching method where AI asks progressively deeper questions instead of providing answers.
Solutions vs Wrappers Philosophy distinguishing genuine innovations from superficial API wrappers. Many AI startups are wrappers rather than true solutions.
SORA OpenAI's most advanced video generation model.
Sound Generation Generative AI capability for creating audio content.
Spectral Clustering Clustering algorithm using eigenvalues of similarity matrix to reduce dimensionality before clustering.
Splitting Agent pattern for dividing large tasks or data into smaller manageable pieces for processing.
SQS (Simple Queue Service) AWS message queue service, mentioned as infrastructure requiring ownership.
Stable Diffusion Open-source model by Stability AI for generating images from text descriptions using diffusion process.
Stubs Test doubles that provide predefined responses to method calls during testing.
Stability AI Company that developed Stable Diffusion image generation model.
State In reinforcement learning, a snapshot of the environment at a given time.
Status Line Customizable information display at bottom of Claude Code interface showing current state.
Sub-Agents (Claude Code) Independent agents spawned by Claude Code, each with their own 200k token context window.
Summarization LLM task of condensing long text into shorter summaries while retaining key information.
Supervised Learning Machine learning paradigm where models learn from labeled training data to make predictions.
Support Vector Machines (SVM) Machine learning algorithm for classification and regression using hyperplanes to separate data.
Swagger API documentation specification format, also known as OpenAPI.
SWE-bench Software engineering benchmark for evaluating AI coding capabilities.
Synthetic Data Generation Creating artificial but realistic test data for testing purposes.
System Prompt Instructions defining how an AI model should behave and respond to user inputs. Sets the behavior and style of the LLM.
T
t-SNE (t-Distributed Stochastic Neighbor Embedding) Dimensionality reduction technique particularly effective for visualizing high-dimensional data in 2D or 3D.
Tagging Practice of labeling resources and assets for organization and ownership tracking.
TDD (Test-Driven Development) Development methodology writing tests before implementation to ensure code quality.
Team Erosion Phenomenon where team knowledge degrades over time as people leave, leading to orphaned tech assets.
Technical Debt Accumulated shortcuts and suboptimal solutions in codebases that make migrations and maintenance more difficult.
Terraform Infrastructure as code tool, mentioned as tech asset requiring ownership tracking.
Test Doubles Generic term for objects used in testing to replace real dependencies (includes stubs, mocks, fakes).
Test Induction Proper testing infrastructure setup for comprehensive test coverage.
Testing Creating test coverage as prerequisite for migrations and code changes.
Testing Interfaces Custom APIs created specifically to expose and manipulate application state for testing purposes.
Text Generation Generative AI capability for creating written content from prompts or other inputs. LLMs process system and user prompts to generate responses.
TGAN (Temporal Generative Adversarial Networks) Model that focuses on generating videos by modeling temporal dynamics.
Token Budget Allocated amount of tokens for AI processing, with ultrathink using up to 10,000+ tokens.
Tokens The basic units that LLMs process. Text is broken down into tokens, and LLMs predict the next token in a sequence. Understanding tokens is key to understanding how Generative AI works and costs.
Traditional AI Earlier AI/ML approaches including regression, classification, clustering, and dimensionality reduction.
Transformer Architecture Neural network architecture introduced in "Attention is All You Need" paper by Vaswani et al. in 2017, revolutionizing NLP.
Transformers Neural network architecture underlying modern LLMs, enabling efficient processing of sequences through attention mechanisms.
Translation LLM task of converting text from one language to another.
Troubleshooting Using AI to help diagnose and resolve problems in code, systems, and architectures including log analysis and debugging.
Two Steps Forward, One Step Back Characterization of AI progress: advancement comes with limitations, hallucinations, ignored requests, and mistakes.
U
Ultrathink A Claude Code feature providing extended reasoning capabilities with larger token budget up to 10,000+ tokens for complex problems, allowing more thorough analysis than normal thinking mode.
Underfitting Model training problem where model is too simple to capture data patterns, requiring more complexity or features.
Unsupervised Learning Machine learning paradigm where models find patterns in unlabeled data without predefined categories.
User Prompt The actual input from the user that the LLM responds to, processed along with system prompt.
V
VALUE Function In reinforcement learning, function estimating how beneficial it's for an agent to be in a given state.
Vector Databases Specialized databases storing and querying embeddings efficiently, essential infrastructure for RAG systems. Examples include Pinecone, Weaviate, Milvus, Chroma, and Postgres pgvector.
Vending (Vetting + Defending) Security practice of thoroughly checking MCP servers before use, combining vetting and defending.
VERSION File File tracking the current version number of the book.
Vibe Coding A practice coined by Andrej Karpathy where developers generate prompts and do not look at the generated code, assuming AI will handle everything correctly. The book argues this is a bad practice for serious software engineering, especially for core business logic.
Vintage Coding Practice of coding without AI assistance to maintain core skills and proficiency, often practiced in coding dojos.
Vibe Payments A satirical concept introduced in the book suggesting that if developers use "vibe coding" without reviewing code, their payments should also be random and unpredictable, reflecting the lack of due diligence in their work.
Video Generation Generative AI capability for creating video content from text descriptions or other inputs. Still experimental and not ready for production.
VideoPoet Large Language Model for zero-shot video generation.
VQ-VAE-2 Hierarchical model using vector quantization to generate high-quality videos.
VSCode (Visual Studio Code) Microsoft's development environment and IDE, frequently forked and extended for coding agents like GitHub Copilot and AWS Kiro.
W
Waymo Self-driving car project that demonstrates the gap between demo and production, taking over 11 years from initial demo in 2014.
Weaviate Vector database for storing and querying embeddings.
Whisper OpenAI's automatic speech recognition system that transcribes spoken language into text.
X
XAI Company that provides Grok LLM models.
Z
Zero to Demo Rapid prototyping phase made faster with AI assistance.
Demo to Production Complex productionization phase that remains difficult despite AI assistance, requiring careful engineering.
Zig Systems programming language mentioned as a learning target.