Artificial Intelligence

As smart factories evolve and artificial intelligence takes center stage, many organizations are asking: Do we still need leaders walking the shop floor? This book answers with a resounding yes.

Part of Lean Foundations & Advanced AI Applications SeriesWhether you're in healthcare, government, education, or manufacturing, AI-Powered Lean helps you reimagine how value is created—and how waste is eliminated—with the aid of intelligent tools.

What's inside:33 ranked optimization patterns from foundational table design through advanced governance. A complete partition and clustering playbook with six Tier 1 patterns.Shuffle reduction techniques built around five patterns that eliminate the most expensive operations. The On-Demand vs Reservations decision framework with diagnostic SQL.A full Guardrails Playbook 

AWS replaced Amazon Q with Kiro — an AI IDE that writes specs before code. 14 chapters covering spec-driven development, AI agents, hooks, MCP integrations, AWS deployment, and the honest comparison with Cursor and Copilot. The definitive guide.

The tech job market changed. Entry-level dropped 73%, AI screens resumes, interviews test system design. This is the no-BS guide: portfolio, resume, LinkedIn, interviews, negotiation, and your first 90 days. Written by someone who hires engineers.

Kubernetes exposes plenty of state, but operators still have to reconstruct operational meaning from scattered Pods, Services, EndpointSlices, Events, PVCs, owner references, and status fields. This ebook uses `kctx`, a small read-only Kubernetes context engine, to show how deterministic entities, relations, signals, graphs, namespace snapshots, CRD adapters, and stable JSON contracts can turn raw cluster data into reusable context for humans, tools, and AI agents. It is written for SREs, platform engineers, Kubernetes operators, infrastructure developers, and AI tooling builders who want better primitives than raw YAML and improvised troubleshooting pipelines.

Artificial Intelligence is no longer just a tool for generating text.Modern AI systems can understand documents, analyze images, process audio, interpret structured data, and assist with complex reasoning tasks. Gemini 3 represents a significant advancement in multimodal AI, enabling students, researchers, and professionals to interact with information in more intelligent and productive ways.This practical guide explores how Gemini 3 works, how multimodal AI systems process different types of information, and how users can integrate these technologies into learning, research, communication, and professional workflows.Inside this book, you will discover:✓ How multimodal AI processes text, images, documents, and audio✓ Effective prompt engineering strategies✓ AI-assisted learning techniques for students✓ Research workflows powered by AI✓ Professional productivity and knowledge management applications✓ Responsible AI usage and risk awareness✓ Step-by-step working models for real-world implementationWhether you are a student preparing for the future, a researcher managing large volumes of information, or a professional seeking greater productivity, this book provides a practical roadmap for leveraging Gemini 3 in an AI-driven world.

Artificial Intelligence is changing how we learn, research, create, and work.But what exactly is GPT-5?How do modern multimodal AI systems understand text, images, documents, and other forms of information simultaneously?How can students, researchers, educators, and professionals use these tools responsibly and effectively?In GPT-5 and Multimodal AI: A Practical Guide for Students, Researchers, and Professionals, author Anshuman Mishra provides a clear, practical, and educational roadmap for understanding the next generation of intelligent systems.From neural networks and transformer architectures to AI-assisted learning, academic research, professional productivity, and responsible innovation, this book explains both the technology and its real-world applications.Readers will discover how AI can support personalized learning, research workflows, software development, business productivity, and creative problem-solving while maintaining the importance of human judgment, ethics, and critical thinking.Whether you are a student preparing for the future, a researcher seeking new tools, an educator exploring innovative teaching methods, or a professional adapting to digital transformation, this book provides the knowledge and practical skills needed to thrive in the age of AI.The future belongs not to those who fear AI, but to those who understand it.

Artificial Intelligence is no longer a distant possibility—it is becoming a collaborative partner in education, research, software development, and professional decision-making.But how do frontier AI systems actually work?What makes safety-centered AI different from earlier generations of language models?How can students, researchers, educators, and professionals prepare for a future shaped by increasingly capable AI systems?In Claude 4 (Anthropic): Safety-First Frontier AI in 2026, author Anshuman Mishra presents a comprehensive and academically grounded exploration of one of the most influential frontier AI systems of the modern era.From Constitutional AI and advanced reasoning architectures to multimodal intelligence, agentic workflows, educational transformation, governance frameworks, workforce implications, and future AI research directions, this book provides readers with a balanced understanding of both opportunities and challenges.Rather than focusing on hype or speculation, the book emphasizes evidence-based analysis, responsible innovation, ethical deployment, and human-centered AI development.Whether you are a student seeking AI literacy, a researcher exploring frontier models, a professional adapting to technological change, or a policymaker shaping future governance frameworks, this book offers the knowledge needed to engage thoughtfully with the next generation of intelligent systems.The future of AI will not be determined by technology alone.It will be shaped by how responsibly humanity chooses to use it.

How does a machine recognize a face?How can AI distinguish speech from noise?Why do modern computer vision systems still rely on mathematical techniques developed decades ago?The answer lies in Fourier and Wavelet Analysis.In Fourier and Wavelet Analysis in Artificial Intelligence, Anshuman Mishra reveals how frequency-domain representations, multi-resolution analysis, and signal-processing techniques continue to shape the future of Machine Learning, Deep Learning, Computer Vision, Speech Recognition, Biomedical AI, and Edge Intelligence.From Fourier Transforms and Fast Fourier Algorithms to Wavelet Scattering Networks and Hybrid CNN Architectures, this book demonstrates how mathematical signal analysis becomes intelligent feature extraction.Discover the mathematics behind perception, representation, and intelligent decision-making.

Can a neural network be viewed as a differential equation?Why does gradient descent behave like a dynamical system?How do biological neurons inspire modern AI architectures?What mathematical principles govern stability, learning, adaptation, and intelligence?In Differential Equations in AI and Neural Dynamics, Anshuman Mishra explores the mathematical framework that underlies modern Artificial Intelligence.From Ordinary Differential Equations and Neural Population Models to Neural ODEs, Stochastic Learning, Reinforcement Learning, and Brain-Inspired Computation, this book reveals how continuous-time mathematics drives intelligent behavior.Discover how equations of change become equations of intelligence.

Stop maintaining CLAUDE.md and AGENTS.md as two files that drift apart. This handbook documents which file each of nine tools reads — checked on 2026-06-02 against each tool's official docs — and ranks six ways to keep one instruction file in sync, from an @AGENTS.md import to a symlink to a CI drift check. It is also precise about where official support still is not: issue #6235 was closed as completed, but the official CHANGELOG does not mention AGENTS.md anywhere, and on v2.1.246 AGENTS.md is still not loaded into the model's context the way CLAUDE.md is.

Between March and April 2026, Claude Code subscribers lived through six weeks of stacked regressions: cache TTL shortened from sixty minutes to five, a tokenizer that charged 1.35 to 1.46 times more for the same prompts, Pro access that briefly disappeared, and a third-party tool ban. This playbook turns that window into a decision instead of a guess: five measurable triggers, three paths — stay and fortify, switch, or build your own stack — and a decision tree that returns one specific recommendation, with a 30-day cost projection for each path. Edition 2 is 251 pages: ten chapters, six path supplements and four appendices, reconstructed from public GitHub issues, Hacker News threads, and independent measurement posts.

Once the five essential hooks are in place, the incidents that keep happening are the guards themselves failing, silently. Nine chapters collect the hooks (Git, credentials, token spikes, autonomous operation, multi-agent work), a chapter on hooks that do not fire, eight dated incidents from the author's own unattended operation, and a 50-point audit you can run against your own setup.

What do speech recognition systems, computer vision models, autonomous robots, and biomedical AI applications have in common?They all rely on the mathematics of signal transformation.How does a neural network extract meaningful patterns from raw audio?Why are Fourier features becoming increasingly important in machine learning?How can Laplace and Z-Transforms help analyze dynamic systems, sequential data, and intelligent control architectures?In Integral Transforms for Artificial Intelligence, Anshuman Mishra reveals how Fourier, Laplace, and Z-Transform techniques power modern AI systems across machine learning, deep learning, computer vision, speech processing, robotics, and signal analysis.Discover how mathematical transformations convert raw signals into intelligent insights—and how they continue to shape the future of Artificial Intelligence.