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Loop Engineering for Developers: How to Make AI Run Itself, and Stop Itself (AI for Web Developers Series Book 7)
English | October 5, 2026 | ASIN: B0HLZRS916 | 149 pages | Epub | 3.81 MB
A loop with a weak verifier doesn't fail. It succeeds — confidently, hundreds of times, at producing junk.In June 2026, the creator of Claude Code said he no longer prompts the AI. A loop does. His job is to write the loop. Within a week the practice had a name: loop engineering — designing systems where AI agents find work, execute it, verify it, and stop on their own.Writing a while loop is easy. The hard part is making a machine decide "this is done." This book focuses on exactly that: the verifier.What you will learn:The five parts of a loop — trigger, goal, execution, verification, stop — plus external memoryHow to turn vague requirements into numbers, exit codes, and checklistsFive named end states, and why errors and exhaustion are never "success"The five-level verification ladder, and how to push checks down to deterministic Level 1Separating the generator from the verifier, and stopping agents from editing tests to passWhy looping can make results worse — and how evidence freshness, ratchets, and caps fix itClaude Code in practice: /goal, /loop, /schedule, and a three-layer loop built with Hooks, Skills, and SubagentsA hardened Ralph loop with circuit breakers, dual exit checks, and checkpointsA real failure autopsy, a full cross-platform Notepad project, and verification gates for agent teamsCost per accepted change, failure modes, and what should never be put in a loopGrounded in first-hand sources and the 2026 research — including the paper that showed accuracy dropping from 0.820 to 0.673 on a second repair pass — this book takes the criticism seriously instead of selling loops as magic.Every script in the book follows one exit-code convention and was tested before it went into print.Build the loop. Stay the engineer.

Let Me Lead
English | September 1, 2026 | ASIN: B0G69GRYPD | 232 pages | EPUB (True) | 558.16 KB
#1 Amazon Best SellerYour roadmap to success as an early-career professional.Being a professional at the start of your career often feels limiting and discouraging. With a job market that is growing more crowded and competitive, hirers are increasingly (and unnecessarily) casting time and experience as the "golden ticket" to career advancement, rather than investing in passion and potential. When organizations fall into the pattern of prioritizing experience, they amplify the bias our society holds against the youth and miss out on the innovation driving talents they provide. Organizational growth, along with that of the young workforce, is stunted as a result.In his book, Let Me Lead, Dr. Seger Morris shines a light on this form of ageism that runs rampant across all professions and shows how to overcome it. Using a diverse compilation of real-life examples that transcend industry differences, Dr. Morris teaches you:*How to avoid the experience pitfall and better foster innovation.*How to conquer your victimhood in ageism for increased success.*How to create synergy between your personal and professional life for a greater impact.Whether you're an early-career professional who's tired of begging for a seat at the table or an executive looking to broaden your perspective as a leader and mentor, this is the practical guide for you.

Learned, Not Programmed: A Beginner's Guide to How Artificial Intelligence, Machine Learning, Neural Networks and Generative AI Actually Work
English | September 24, 2026 | ASIN: B0HKWXRYQJ | 271 pages | Epub | 4.22 MB
How does a chatbot actually produce an answer? Why can an image generator create a picture no one has painted, yet struggle to count the objects in it? And why do AI systems sometimes state false things with complete confidence?Learned, Not Programmed explains what is really happening inside modern artificial intelligence, one step at a time. It requires no programming and no math beyond everyday arithmetic. The central idea is simple: today's AI systems are not given their behavior line by line. People design them, choose their data and set their goals, but the detailed behavior is learned from examples. Once you see that, much of what AI does, and much of what it gets wrong, starts to make sense.Inside, you will learn:How machine learning turns examples into behavior, and how a system measures and reduces its own mistakesWhat a neural network is, and how it learns by adjusting its settingsHow tokens and embeddings turn words into numbersHow transformers use attention to weigh contextHow a text predictor becomes a helpful assistantWhat happens, step by step, from the moment you press Enter to the answer on your screenHow generative AI creates images, voices and videoWhy AI makes mistakes: invented facts, bias, brittleness and uneven abilityHow to use AI with sound judgment, and how to protect yourself from deepfakes and scamsThe book is organized in ten short parts: what AI actually is, how machines learn, neural networks, turning language into numbers, and how a predictor becomes an assistant. It then follows a single question from start to finish before moving on to images and sound, why AI makes mistakes, using AI responsibly, and the debates about AI's future.It includes 27 original diagrams, a glossary of about 90 terms, and notes that document the sources behind its key claims. Where experts disagree or the evidence is still uncertain, it says so.This book is for you if:You use AI tools and want to understand what is happening behind the screenYou have heard terms like "neural network," "token" or "transformer" and want them explained in plain languageYou want to judge claims about AI, both the hype and the fear, for yourselfYou are a student, parent, teacher or professional who needs a solid foundation without a technical backgroundWritten for curious readers who want understanding, not just tips.

Learn English Fast and Easy: 16 Mini Books in 1: Grammar, Vocabulary, Pronunciation and Conversation. American English for Beginners in 45 Days (English All-in-One Book 35)
English | September 15, 2026 | ASIN: B0HJT92YCX | 232 pages | Epub | 682.93 KB
Have you ever started learning English and then stopped?It was not about ability. It was because English books are dense, long and dull, and by the third week you stop opening them.This English course is built so that does not happen again.WHAT IS INSIDESixteen short mini books covering English grammar, English vocabulary and conversation. Each one has a single topic and nothing else. You do not read them straight through or study for hours: each one takes two or three days.1. Your first 200 words, grouped by topic2. The verb to be, the first verb you need3. The small words: a, the, in, on, at4. Numbers, time and dates for prices and schedules5. How an English sentence is built6. The simple present and your daily routine7. The present continuous: what is happening now8. Modal verbs: can, could, should9. The past and the 50 irregular verbs that really come up10. The other 300 words that complete the 50011. The present perfect and when to use it12. The future: going to and will13. Everyday phrases for real conversation14. English for travel: airport, hotel and transport15. English at work: the interview and the email16. Short stories to read straight throughWHO THIS ENGLISH BOOK IS FORFor adult beginners who want to learn English from scratch. It works as a self study English workbook: no teacher and no class needed. For anyone who studied at school and remembers nothing. For anyone about to travel, move abroad or use the language at work. For anyone who tried other methods and gave up.WHAT MAKES IT DIFFERENTSimple respelling. Every word comes written the way it sounds, with no phonetic symbols to learn first.Plain English. The rules are explained in short, clear sentences, with no grammar jargon.Exercises with every answer. More than 190 solved exercises, at the end of each mini book, so you can check your own work.The mistakes learners make. Each mini book includes the errors that come from translating word for word, with the correction and the reason behind it.American English. What you hear in the United States, in films and in series. And it works anywhere: the English spoken between foreigners is the same in Tokyo as it is in Madrid.WHAT THIS BOOK DOES NOT PROMISEYou will not speak fluent English in a month. That does not happen with any book, and whoever promises it is lying to you.What we do promise is concrete: in 45 days, one hour a day, you will understand how English is put together and you will build your own sentences instead of repeating memorized ones. The day by day plan is inside.How far you go after that is up to you.Scroll up and start today.

LSTM Networks from the Ground Up: A Hand-Worked Mathematical and PyTorch Guide to Gates, Gradients, and Backpropagation Through Time
English | October 9, 2026 | ASIN: B0HMGWH6ZC | 193 pages | Epub | 277.24 KB
Unravel the Mathematics and PyTorch Implementation of LSTMs—Step by Step, Gate by Gate.Most deep learning books treat Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) cells as black boxes. They show you how to call torch.nn.LSTM() in three lines of code, leaving the core mathematics, gradient flows, and backpropagation mechanics completely hidden.LSTM Networks from the Ground Up bridges the gap between high-level theory and low-level code. Designed for data scientists, machine learning engineers, and students who want a deep, unabstracted understanding, this text walks you through the complete architecture using worked mathematical derivations and raw PyTorch code.Inside This Book, You Will Learn:The Mathematical Foundations: Derive the exact equations governing the Forget Gate, Input Gate, Candidate State, Cell State, and Output Gate.Backpropagation Through Time (BPTT) by Hand: Step through partial derivatives and gradient flow equations line-by-line to understand how LSTMs solve the vanishing gradient problem.Custom PyTorch Implementations: Build functional LSTM cells and multi-layer networks from scratch using basic tensor operations before touching pre-built modules.Practical Diagnostics & Training: Master state management, sequence padding, gradient clipping, and time-series feature engineering.Who Is This Book For? Whether you are building custom time-series forecasting pipelines, working with financial data, or preparing for deep learning interviews, this hands-on guide gives you the foundational confidence to debug, design, and optimize recurrent architectures with complete mathematical clarity.Grab your copy today and master LSTMs from first principles!

LOCAL AI ON YOUR PC: How to Run Private AI, LLMs, Coding Agents and Generative AI on Consumer Hardware (Precision Engineering Book 11)
English | October 9, 2026 | ASIN: B0HMK1647L | 277 pages | Epub | 483.68 KB
What if your PC could become your private AI workstation-without sending your sensitive data to someone else's servers?Are you tired of cloud AI services handling your documents, source code, prompts, or personal information? Have you tried running an LLM locally only to discover that VRAM, RAM, quantization, model size, and performance are more complicated than the tutorials suggested?Do you want a local coding assistant or AI agent, but worry about giving an AI system access to your files, terminal, or development environment? Or are you frustrated by guides that show you how to launch a model but never explain why it fails, slows down, runs out of memory, or becomes difficult to maintain?Local AI on Your PC takes a different approach.Instead of focusing on copy-and-paste commands, this book teaches you how to design, build, secure, evaluate, troubleshoot, and maintain practical AI systems on consumer hardware.You'll learn how to move from experimentation to engineering:From guessing whether a model will fit to sizing hardware and memory requirements intelligentlyFrom downloading random models to choosing models, runtimes, formats, and quantization levels deliberatelyFrom basic chat to private RAG assistants over your documentsFrom AI-generated snippets to repository-aware coding assistantsFrom prompts to safer autonomous coding agents with controlled tool accessFrom mysterious failures to systematic benchmarking and troubleshootingFrom isolated tools to integrated workflows using APIs, applications, files, databases, Git, and development toolsInside, you'll discover how to work with local LLMs, coding agents, image generation, speech, audio, video, automation, and multimodal workflows while understanding the hardware and software constraints behind them.You'll learn how VRAM, RAM, context windows, KV cache, quantization, GPU acceleration, storage, latency, throughput, and thermal behavior affect real workloads. You'll also learn how to protect credentials and private data, isolate network services, restrict agent capabilities, validate AI-generated results, and build systems that remain dependable as models and runtimes change.Whether you're a beginner tired of incomplete tutorials, a developer building private AI tools, a privacy-conscious user, a PC enthusiast optimizing existing hardware, or an AI early adopter, this book gives you a practical engineering framework for making better decisions.Because running a model is only the beginning.The real goal is to build local AI you can understand, control, troubleshoot, and depend on.If you're ready to move beyond fragile experiments and build a capable private AI environment on hardware you control, this is the guide that will help you make local AI work-and keep working.

LLM Evaluation Engineering: Reliable Tests, Regression Gates, and Quality Metrics for Production AI
English | October 5, 2026 | ASIN: B0HLZD9JZF | 117 Pages | Epub | 304.34 KB
The demo looks good. Should you ship?A fluent answer can hide the wrong policy, an unsupported claim, or an unauthorized action. A higher average score can conceal a regression in the cases that matter most.LLM Evaluation Engineering shows AI engineers, QA and SDET practitioners, and technical product leaders how to replace intuition-driven releases with a repeatable quality process.You will learn how to:- Turn product requirements into observable behaviors and explicit acceptance criteria.- Build versioned datasets with useful coverage, protected holdouts, and clear limits on what the results prove.- Write deterministic graders, calibrate human rubrics, and audit model judges for consequential errors.- Compare candidates with paired evidence, confidence intervals, and predeclared regression margins.- Evaluate retrieval, claim support, agent state, authorization, retries, and execution budgets.- Build release gates and production feedback loops that keep critical failures visible.Apply the method as you read. Fourteen focused chapters include explained decision exercises. A fifty-failure-mode workbook and twelve operating templates help you design your own evaluation system. The complete standard-library Python lab is reproduced in the book: its candidate improves the overall score, yet the gate correctly blocks a critical regression.The approach is vendor-neutral. All cases and release results are original fictional fixtures, and the lab requires no API key or paid evaluation platform. Basic Python, JSON, and software-testing knowledge are helpful; no statistics degree is required.Build evidence you can inspect. Know what your scores prove. Make the next AI release a decision you can defend.

Kubernetes Operators in Depth: Controller patterns, CRD design, versioning, and operator lifecycle.
English | September 20, 2026 | ASIN: B0HKT13QMH | 319 pages | EPUB (True) | 247.47 KB
Kubernetes Operators in Depth is a practical engineering guide to extending Kubernetes with the same control-loop discipline the platform uses internally. It starts from a single observation: the operator pattern is easy to start and hard to run in production, and the difference is measured in the specific patterns — finalizers, conditions, conversion webhooks, leader election — that most tutorials skip.The book walks through the full discipline — why operators exist and what they replace, the reconciliation loop and its consequences, custom resource definitions and their schema decisions, building a first operator with Kubebuilder and controller-runtime, the controller patterns and best practices that separate production operators from demos, status and conditions and observability, ownership and garbage collection and finalizers, admission webhooks and validation, operator versioning and CRD evolution, conversion webhooks and multi-version CRDs, testing with envtest and kind, packaging and distribution through OLM and OperatorHub, day-two operations and upgrades, and the trends reshaping the field.It covers the failure modes that quietly wreck operators in production: a reconciliation loop that runs hot because it updates the resource it just read, a finalizer that is never removed and blocks every deletion, a webhook that fails closed during an outage and blocks deployments cluster-wide, a CRD version change that breaks every existing resource, a conversion webhook that loses data because the older version has no place to store it, an owner reference that is wrong in scope and causes a cascade that deletes resources it should not. Each is presented with the failure, the countermeasure, and the operational tradeoff.

Kalshi & Polymarket Trading with AI: The Beginner's Prediction Markets Playbook - Real Data, Real Math, and AI Workflows, No Code Required
English | September 24, 2026 | ASIN: B0HKYKCZ29 | 166 pages | Epub | 658.76 KB
Prediction Markets with AI, No Code RequiredYou don't need to know what will happen. You need to know when the price is wrong.Prediction markets went from under $5 billion a month in trading to more than $45 billion in about a year. Millions of people can now trade on Fed decisions, inflation reports, elections, and tomorrow's temperature on Kalshi, Polymarket US, and brokerage apps like Robinhood - and most of them are doing it on hunches. Kalshi & Polymarket Trading with AI is the careful beginner's alternative: a 160+ page, research-based playbook that shows you how these markets really work and how to use free public data and AI tools to estimate probabilities better than the crowd.Written by the author of Machine Learning for Sports Betting and Algorithmic Sports Betting with AI, this book is built on real numbers, not hype: actual fee formulas, actual settlement rules, a study of more than 300,000 Kalshi contracts, and complete worked examples you can check yourself.Inside, you'll learn:Exactly how contracts, order books, spreads, and fees work - and how to calculate your break-even before every tradeKalshi, Polymarket US, and the brokerage apps compared, plus the 2026 court fights over sports contractsHow to read settlement rules so you never lose on a technicalityWhat the research says about who wins and loses - including why cheap "lottery ticket" contracts have lost over 60% on averageForecasting skills that work: base rates, decomposition, updating, and Brier scoresWhy AI models are overconfident forecasters - and how to use them as a Researcher, Analyst, and Skeptic insteadStep-by-step playbooks for weather (free NWS forecasts), Fed and CPI markets (CME FedWatch and the Cleveland Fed nowcast), elections, sports, and niche marketsKelly-based position sizing with a simple lookup table and exposure limitsA trading journal and weekly routine that measure real skill, not luckTaxes, account security, scams, and the warning signs of problem tradingOne complete week of research and trades, every number shown - including a lossThe book includes 23 copy-paste AI prompts, a glossary, a resource directory of free data sources, and a prompt index.This is not a promise of profit. It's a disciplined, honest process for a genuinely new kind of market.Educational content only. Not financial, legal, or tax advice. Trading event contracts involves risk, including the risk of losing your entire stake. If trading is affecting your life in ways that concern you, the National Council on Problem Gambling is available at 1-800-GAMBLER or ncpgambling.org.If this book helps you trade more carefully, an honest review is the single biggest thing you can do to help an independent author and the next reader deciding whether it's right for them.

Julius Caesar: The Man Who Made Rome Eternal: Anthony Everitt
English | October 29, 2025 | ISBN: 9798232047863 | 117 Pages | EPUB (True) | 275.08 KB
Julius Caesar: The Man Who Made Rome Eternal is a sweeping and intimate portrait of the man who shaped the destiny of civilization. From his rise in the crumbling Republic to his death beneath Pompey's statue, Caesar's story unfolds as both triumph and tragedy - the creation of order from chaos, and the birth of empire from ambition.