Books

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.

LLMs and agentic AI are currently generating a great deal of hype. When applied correctly, they can deliver tremendous benefits. This book outlines the challenges involved in implementing these technologies within large enterprises—particularly in regulated environments. It serves as an accessible introduction for IT-focused executives and enterprise architects, while also proving useful for IT professionals in general who wish to explore the subject and avoid common project pitfalls.

Hyperscale Data Center Management reveals the architecture, operations, and strategy behind the world’s largest computing facilities. Discover how power, cooling, and network fabrics are engineered to serve billions; how automation and reliability principles keep services running through constant hardware failure; and how sustainability and economics shape every decision. From foundational definitions to the new demands of AI and GPU workloads, this is the complete guide to managing infrastructure that never sleeps. Essential reading for architects, operators, and leaders building for the future.

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. Eight 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.

Every neural network is a collection of matrix operations.Every convolution is a structured matrix transformation.Every deep learning breakthrough ultimately depends on efficient tensor computations.But how do these mathematical operations actually power intelligent systems?In Matrix Computations for Deep Learning, Anshuman Mishra reveals the mathematical engine behind modern AI. From Singular Value Decomposition and tensor algebra to convolutional neural networks, GPU acceleration, and large-scale machine learning systems, this book provides a complete roadmap for understanding the computational foundations of deep learning.Discover how matrices become intelligence—and how mathematics becomes machine learning.

Every secure communication begins with mathematics.Every modern encryption system relies on number theory.Every intelligent cyber defense increasingly depends on Artificial Intelligence.But what happens when these three worlds converge?In Cryptography, Number Theory, and AI Security, Anshuman Mishra takes readers on a journey from the mathematical foundations of encryption to the cutting-edge frontier of AI-powered cybersecurity.Explore prime numbers, RSA, elliptic curves, blockchain security, machine learning for threat detection, homomorphic encryption, federated learning, adversarial AI, and post-quantum cryptography.Discover how mathematics and machine intelligence work together to secure the digital world.

Prediction is only the beginning.Modern AI systems must explain their predictions, quantify uncertainty, scale to massive datasets, and operate reliably in real-world environments.How do Support Vector Regression models capture nonlinear patterns?How do Bayesian methods estimate uncertainty?How do Gaussian Processes make predictions with confidence intervals?How can complex AI models remain interpretable and trustworthy?In this advanced second volume, Anshuman Mishra explores the cutting-edge regression techniques that power intelligent prediction systems across machine learning, data science, healthcare, finance, robotics, and scientific research.Discover how modern Artificial Intelligence transforms data into reliable, explainable, and scalable predictions.