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AI Cinematic Filmmaking: Pre-Production: The Complete Workflow for AI Filmmakers: From Story to Animatic (THE AI CINEMATIC SERIES Book 5)
English | May 11, 2026 | ASIN: B0H1DYD485 | 252 pages | Epub | 2.40 MB
AI Cinematic Filmmaking: Pre-Production is a practical workflow guide for filmmakers, creators, writers, and AI artists who want to turn ideas into structured cinematic projects. Instead of focusing on hype or endless prompt tricks, the book breaks down the real planning process behind AI filmmaking.Inside, you'll learn how to:analyze stories for AI adaptationbuild a Story Biblebreak scripts into cinematic scenescreate shot lists and visual language systemsdesign consistent characters and locationsorganize production assetsdevelop storyboards and animaticsprepare projects for smoother AI video generationThe workflow is designed for real production use, whether you are creating:short filmscinematic videostrailersweb seriesexperimental projectsindie feature filmsThis book teaches that methodology, end to end, using Ambrose Bierce's "An Occurrence at Owl Creek Bridge" as a worked example throughout. Every prompt is shown. Every output is explained. Every creative decision is made transparent.Plus two production tools: the Prompt Library with a 9-Day Schedule, and the Suffix Library Generator that compresses your bibles into paste-ready fragments for image and video generation using chatgpt, Claude, Gemini , GPTimage, NanoBanana, Veo, Kling, Higgsfiled cinema studio, Seedance, etc. Solo filmmakers, writer-directors, AI artists ready to make full films, traditional filmmakers exploring AI workflows, and anyone with a story worth telling. Book 1 of 3. Book 2 covers Production (video generation, voice, rough cut). Book 3 covers Post-Production (final audio, score, color, delivery).

AI Builds Itself: Recursive Self-Improvement in 2026 (Toward Artificial SuperIntelligence Book 1)
English | March 31, 2026 | ASIN: B0FRBBGPG6 | 199 pages | Epub | 651.66 KB
The machines are already improving themselves. You just have not been told how fast.In the spring of 2026, Claude began writing between seventy and ninety percent of the code used to train its own next version. AlphaEvolve spent over a year optimizing the training process for the very models that powered it, recovering enough wasted compute across Google's global fleet to power a small country's worth of servers. OpenAI's Codex debugged the training pipeline that produced it. And a 630-line Python script showed that anyone with a single GPU could run autonomous AI research experiments overnight."AI Builds Itself" is the first comprehensive survey of recursive self-improvement as it exists right now. Written by H. Peter Alesso, an author with deep expertise in AI systems, web intelligence, and emerging technology, this book maps the full landscape: the organizations driving the science, the technical architectures that make self-improvement possible, the billion-dollar startups founded by the architects of AlphaGo and GPT-4, and the safety researchers warning that the feedback loops powering progress are the same loops that could outrun human oversight.This book is for technology professionals, AI researchers, policymakers, investors, and informed general readers who want to understand the most consequential technological development of the decade, not through speculation or hype, but through documented evidence, cited research, and careful analysis of what the leading minds in the field are actually building.You will learn how AlphaEvolve discovers algorithms that outperform fifty-six-year-old mathematical records. How the Darwin Godel Machine rewrites its own source code and evaluates whether the rewritten version is better at rewriting itself. How Karpathy's AutoResearch framework turns plain-language instructions into an overnight autonomous research laboratory. How Constitutional AI attempts to keep self-improving systems aligned with human values. And why the AI 2027 forecast, graded at sixty-five percent of its predicted pace, still projects transformative change within the next eighteen months.One appendix with a working implementation you can run yourself. A comprehensive glossary and bibliography. No hype. No science fiction. Just the clearest account available of what is happening inside the world's most advanced AI laboratories and what it means for everyone outside them.

AI Automation with n8n: Build Smart Workflows Using AI Tools: A Practical Guide to Automating Tasks, Integrating AI Tools, and Creating Intelligent Workflows Without Complex Coding
English | March 7, 2026 | ASIN: B0GRPDF17B | 199 pages | Epub | 7.00 MB
Automation is becoming one of the most valuable digital skills. Tools like n8n allow anyone to connect apps, automate tasks, and build powerful workflows without writing complex code.AI Automation with n8n explains how to combine workflow automation with modern AI tools to create systems that save time and handle repetitive tasks automatically.This book takes a practical approach, helping readers understand not only how automation works, but how to apply it in real projects.In this book you will learn:* How workflow automation platforms work* How to install and configure n8n step-by-step* How nodes, triggers, and workflow logic operate* How to integrate APIs and external services* How to use AI models inside automation workflows* How to automate content creation and communication* How to process and analyze data automatically* How to build AI-powered assistants and automation toolsReal examples throughout the book show how to automate tasks such as:* generating content using AI* summarizing emails and messages* categorizing support requests* scheduling social media posts* extracting data from documentsBy the end of this guide, you will have the knowledge required to design your own intelligent workflows using n8n and modern AI tools.

AI: Anatomy of Modern Power : Data, Algorithms, and the New Global Struggle for Control (Anatomy of Modern War Book 5)
English | May 9, 2026 | ASIN: B0H15RZRGG | 223 pages | Epub | 9.64 MB
Artificial intelligence is no longer a future technology. It is already one of the most powerful forces reshaping the global balance of power.AI: Anatomy of Modern Power explores the true nature of AI beyond hype, myths, and simplifications. It examines how artificial intelligence is gradually transforming into a strategic infrastructure that affects the economy, politics, defense, information, and the very functioning of modern societies.The book begins with the foundations of artificial intelligence: data, algorithms, computational power, and the invisible infrastructures that make AI possible. It then analyzes how AI evolves from a technological tool into a mechanism of influence and geopolitical power.Through an examination of the global race for computational supremacy, competition over semiconductors, data as a strategic resource, and the concentration of digital power, the book reveals that the battle for AI is not merely about innovation. It is about control over the foundations of the new global order.At the same time, the book explores the social and political consequences of the algorithmic age: surveillance,information manipulation, algorithmic influence, labor automation, digital dependency, and the transformation of the concept of authority itself.The book does not treat artificial intelligence merely as a technological issue. It treats it as a new system of power.In a world where data becomes raw material, algorithms become mechanisms of influence, and computational power becomes geopolitical advantage, understanding AI is no longer optional. It is a necessity.AI: Anatomy of Modern Power is a strategic analysis of artificial intelligence as the defining arena of competition in the 21st century.

AI Agents in Production: A Practical Guide to Building Reliable LLM-Powered Systems with Claude, GPT, and MCP
English | May 17, 2026 | ASIN: B0H23MM17L | 79 pages | PDF | 37.06 MB
Stop Building Demos. Start Shipping Production-Ready AI Agents.AI agents are transforming software engineering, but deploying a reliable system that runs at scale is entirely different from running a local prototype. AI Agents in Production is your definitive, hands-on guide to taking large language model (LLM) applications from fragile experiments to robust, scalable enterprise systems. Written specifically for working software engineers, this book provides the practical blueprints you need to build reliable agentic systems using Claude, GPT, and the Model Context Protocol (MCP).What You Will LearnDesign Robust Architectures: Master ReAct, Plan-and-Execute, and advanced multi-agent system patterns.Integrate Real-World Tools: Use the Model Context Protocol (MCP) to seamlessly connect agents to external APIs with Python and Node.Implement Bulletproof Reliability: Master advanced error handling, retry logic, and graceful degradation for LLM variability.Instrument for Observability: Track traces, metrics, and agent behavior logs to monitor your systems in real-time.Defend Against Vulnerabilities: Secure your agents against prompt injection, unauthorized tool access, and model drift.Scale for the Enterprise: Optimize throughput and cost using message queues, parallelism, and efficient context memory management.Automate Evaluation: Build continuous regression testing and quality evaluation pipelines for non-deterministic AI.If you are tired of theoretical AI research papers and want actionable, production-focused engineering strategies, this book is for you. Every chapter includes practical implementation checklists you can apply to your codebase today. Scroll up and grab your copy to build AI agents that actually work in production!

AI Agents and Large Language Models for Economic Research with Python: Automating Causal Discovery, Model Building, Policy Simulation, and Real-Time Decision Support
English | May 31, 2026 | ASIN: B0H3J7ZS5H | 439 pages | Epub | 537.03 KB
Reactive PublishingAI Agents and Large Language Models for Economic Research with Python offers a practical, hands-on guide to integrating modern AI technologies into economic analysis and research workflows.This book explores how Large Language Models and autonomous AI agents can be leveraged alongside Python to enhance traditional economic research methods. It provides clear implementations for automating key tasks including causal discovery, econometric model building, policy simulation, and real-time decision support systems.What You'll Find Inside:Fundamentals of using LLMs for economic literature review, data interpretation, and hypothesis generationBuilding and deploying AI agents capable of executing multi-step economic research tasksPractical Python implementations for causal inference automationTechniques for constructing, validating, and simulating economic models at scaleMethods for creating real-time policy analysis and decision support toolsCode examples using current open-source libraries and frameworksBest practices for ensuring transparency, reproducibility, and robustness in AI-assisted economic researchWritten for economists, researchers, data scientists, and graduate students, this book bridges the gap between cutting-edge AI technologies and applied economic analysis. Rather than treating AI as a black box, it emphasizes understanding the underlying methods, limitations, and responsible implementation practices.Whether you are looking to accelerate your research pipeline, explore new analytical approaches, or incorporate AI agents into policy evaluation workflows, this guide provides the technical foundation and working code examples needed to begin.Clear. Technical. Reproducible.Ready to enhance your economic research capabilities with AI and Python.

AI Agents Revolution Build Autonomous Agents That Work 24x7
English | May 13, 2026 | ISBN: 9798235290853 | 201 Pages | EPUB (True) | 371.33 KB
The world is rapidly moving toward an era where machines are no longer limited to performing predefined tasks. Instead, they are becoming intelligent systems capable of thinking, planning, acting, and improving independently. This book, AI Agents Revolution Build Autonomous Agents That Work 24 7, is designed to help readers understand and master this powerful transformation.

AI Agents : From foundation to Multi - Agent Systems
English | March 23, 2026 | ASIN: B0GTMSJKY9 | 458 pages | Epub | 303.40 KB
This book focuses on next-generation AI systems, including AI agents, Model Context Protocol (MCP), and intelligent orchestration frameworks. AI agents have emerged not as incremental improvements to chatbots, but as a new computing paradigm. In just a few short years, they have evolved from research experiments to boardroom priorities. What makes this shift remarkable is not just the speed of adoption, but the depth of impact. Agents are not confined to customer service or IT automation; they are supporting clinical workflows, evaluating financial exposure, assisting legal research, managing infrastructure, and orchestrating enterprise operations. Let us learn more about it.

AI Agent Design Patterns: A Practical Guide to Reliable LLM Workflows, RAG Systems, Tool Use, Memory, Planning, and Intelligent Automation
English | May 19, 2026 | ASIN: B0H2BW3NXM | 292 pages | Epub | 359.85 KB
AI agents are changing how software works.We are moving from systems that simply respond to commands to systems that can reason, plan, use tools, retrieve knowledge, collaborate, critique their own work, and complete complex tasks with minimal supervision.But most people trying to build AI agents face the same problems:Their agents hallucinate.Their workflows break under complexity.Their prompts become messy and unmanageable.Their multi agent systems turn chaotic.Their tools fail unpredictably.Their memory systems become unreliable.And their "smart assistant" works well in demos but collapses in real-world use.This book was written to solve those problems.Agentic Design Patterns is a practical guide to designing reliable, scalable, and intelligent AI agent systems using modern agent architecture principles and proven design patterns.Instead of vague theory and hype, this book focuses on how real agent systems are structured, coordinated, controlled, and improved.Inside this book, you will learn:* What AI agents actually are and how they differ from chatbots and automation systems* How large language models reason, fail, hallucinate, and recover* The core building blocks behind modern agentic systems* How to design agents that can plan, retrieve information, use tools, reflect, critique, and collaborate* The difference between simple agents, router agents, planner agents, ReAct systems, and multi agent workflows* How specialist agents work together in coordinated systems* Why memory, context management, and retrieval systems matter* How to design safer and more reliable agent architectures* Common AI agent failure modes and how to avoid them* Practical implementation checklists for building production quality systems* Real architectural thinking behind modern AI agentsThis book is designed for:* AI engineers and developers* Product managers and technical founders* Automation builders* Software architects* Researchers and AI enthusiasts* Anyone who wants to understand how modern AI agent systems are actually designedWhether you are building AI assistants, research agents, coding systems, workflow automations, customer support agents, or multi agent orchestration systems, this book gives you the mental models and practical frameworks needed to build with confidence.The future of software is agentic.This book helps you understand how to design it properly.

AI AGENT MASTERY with Claude AI: A Practical Guide to Building Smart Systems That Plan, Reason, and Execute
English | May 12, 2026 | ASIN: B0GX2YDS3K | 167 pages | Epub | 1.07 MB
Build Smart Systems That Plan, Reason, and ExecuteThere is a fundamental difference between an application that answers questions and one that solves complex problems. Problem-solving requires understanding a broader goal, breaking it down into actionable sub-tasks, managing dependencies, and recovering gracefully when individual steps fail. AI Agent Mastery with Claude AI provides the definitive roadmap for engineering the architecture of deep reasoning.Written for intermediate to advanced Python developers, this guide demonstrates how to construct systems that formulate structured plans rather than simply generating prose. Every concept is backed by complete, tested code with no pseudocode or placeholder outputs, allowing you to implement production-grade logic immediately.Core Concepts Covered:Deep Reasoning Prompts: Design system prompts that force structured decomposition and Socratic questioning.Planning Systems: Build task tree generators, resolve dependencies, and manage topological execution ordering.Dynamic Re-Planning: Implement self-correction loops, checkpoint recovery, and failure detection mechanisms.Execution Engines: Develop code execution sandboxes, REST API orchestrators, and automated data pipelines.Mastery-Level Patterns: Deploy the Critic-Revise pattern and integrate Knowledge Graphs for structured analysis.Elevate your engineering skills and build autonomous applications capable of genuine, self-directed problem-solving.(Note: This is an independent publication and is not affiliated with or endorsed by Anthropic PBC).