Books

Este livro espera trazer a inspiração da musa a todos aqueles que querem compreender o céu e muitas outras coisas por meio da astrologia.

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.

Your apps are failing. You just don’t know it yet.What if you could see every crash, slowdown, and bottleneck before your users do? Understanding Dynatrace pulls back the curtain on AI-powered observability—turning blind spots into actionable insights. Stop guessing. Start observing.

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.

Some meetings are accidents. Some accidents are fate. A flight, a city, and two people who weren't looking — but found each other anyway.

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.

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.

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.

Lo que no te cuentan sobre construir el software que mueve un e-commerce en LATAM: facturación electrónica ante el SII, concurrencia con plata de por medio, marketplaces con sus gotchas no documentadas y multi-moneda sin descuadres de un peso. No es teoría ni un "hola mundo": son las decisiones, los bugs que costaron dinero y los patrones que vienen de operar un ERP multi-tenant real en producción.

Every intelligent prediction begins with a simple question:Can we model the relationship between data and outcomes?Regression is the foundation upon which modern predictive analytics, machine learning systems, and countless AI applications are built.From linear regression and regularization techniques to neural network regression, Gaussian processes, explainable AI, and real-world predictive systems, this book provides a complete roadmap for understanding how machines learn to predict.In Linear and Nonlinear Regression in Artificial Intelligence, Anshuman Mishra combines mathematical rigor, practical implementation, and real-world applications to help readers master one of the most powerful tools in Artificial Intelligence.Discover the mathematics behind prediction—and the science behind intelligent decision-making.