Books

Explore the terrifying and beautiful physics of our cosmic isolation. The Solitude of Stars blends hard science with lyrical poetry to answer why the night sky remains so incredibly silent. Discover why the merciless laws of the universe make our lonely blue marble a precious, statistical anomaly.

Assess your AI visibility, identify the gaps and prepare your website for AI search — step by step, without SEO expertise. Built for small business owners who want to do the work independently.

«Décadas antes de la invención de Internet, los laboratorios del CERN ocultaron un hallazgo capaz de alterar la carrera atómica. Basado en hechos e instalaciones reales de la Guerra Fría en Suiza, Francia y América Latina.»

A premium, beginner‑friendly cinematic guide to tiny homes and container homes, featuring 40+ diagrams, layout visuals, safety panels, insulation charts, airflow strategies, space‑saving furniture icons, and small‑space design principles. Includes zoning basics, structural notes, budgeting guidance, and a printable starter checklist — all presented with clean EVL cinematic formatting for modern, practical planning.

A practical, easy-to-follow guide that teaches you how to speak with clarity, confidence, and impact. Learn structure, openings, delivery, nerves, Q&A, visuals, and more.

Transform your AI potential into practical action. This handbook provides the professional toolkit you need to build, test, and deploy intelligent agents with confidence.

AI is basically a very well-optimized math system pretending to sound like a person. That's it. That's the whole secret.This book explains exactly how, no math, no code, no jargon. Just a straight conversation about what's actually happening inside the machine everyone won't stop talking about.

Master Swift from the ground up and learn how to write clean, modern code with confidence. From core language fundamentals to concurrency, generics, protocols and production-ready architecture, this practical guide covers the Swift features that matter in 2026 with clear explanations and useful examples.

📘 Mastering Advanced ADB Command Line - Professional HandbookThe most comprehensive, technically accurate, and production‑ready ADB manual available online. A full deep‑dive into Android Debug Bridge internals, automation, performance engineering, security, and forensic workflows — written for professionals who demand more than basic tutorials. This is not a beginner’s guide. This is a complete engineering reference.

Advanced IBM Quantum Computing and Qiskit Architecture: A Strategic Briefing Executive SummaryThe current landscape of quantum computing has shifted from a circuit-centric focus to a workload-centric ecosystem. The modern Qiskit architecture is designed to bridge the gap between idealized mathematical abstractions and the noisy, physical reality of IBM’s superconducting processors (such as the Eagle, Osprey, and Condor).

Fragment 1 — Chapter 1, "Introduction": why AI governance is a governance question, not an engineering oneWhen an organization first faces a decision to deploy an AI system — whether a credit-scoring model, an automated resume-screening tool, or a customer-support chatbot — the most common governance mistake is to treat that decision like an ordinary IT project: define the requirements, select a vendor, implement, and hand the system over to operations. Artificial intelligence technologies do rely on software and computing infrastructure, and in that sense they resemble any other IT initiative. But they differ in how they generate risk, and that difference calls for a distinct governance approach rather than a simple extension of familiar project management.Risk-oriented AI governance starts from a different premise: before discussing rollout timelines, budgets, or functional requirements, an organization has to answer the question "what adverse consequences could this system cause, for whom, with what likelihood, and how severe would they be" — and, alongside it, "how much of that harm are we willing to tolerate for the expected benefit." Fragment 2 — Chapter 3, "Trustworthy AI: Seven Characteristics of Trust": why explainability and interpretability are not synonymsConsider an AI system that automatically sorts incoming support tickets by priority. Explainability answers the "how" question: which features of the incoming text (keywords, tone, customer history) contributed to the computed priority score and with what weight — a technical account of the computation mechanism. Interpretability answers the "why" question for this particular ticket in the context of the system's business purpose — that is, whether a high priority score means "this customer is losing money right now" or "this customer is statistically likely to cancel," and whether that meaning matches how a support agent should act on it. The two characteristics support each other but serve different governance needs: explainability matters more to the engineer debugging the model, interpretability matters more to the agent or manager who must act on the system's output without understanding its internal mechanics. Fragment 3 — Chapter 18, "The Running Case": where ISO and NIST illuminate each other's blind spotsPrecisely because NIST explicitly requires, in its own standalone subcategory, a designated authority to deactivate a system, and no direct equivalent exists in Annex A of ISO, an organization that implements only ISO/IEC 42001 without checking it in parallel against the AI RMF risks missing this requirement altogether — it dissolves between the adjacent, but not identical, controls A.6.2.5 (release criteria) and A.3.2 (roles and responsibilities). This is a compelling, concrete example of the argument the book already made back in Chapter 1: the two frameworks do not compete but mutually illuminate each other's blind spots. Fragment 4 — Chapter 18, the book's closing paragraphAI risk management, as this book has shown it, never concludes with a signed document. It concludes — and immediately begins again — with the next MAP cycle, the next internal audit, the next policy review, the next model version, which has to be brought into operation just as thoroughly documented, traceable, and responsibly managed as the one before it.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.