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Vibe Coding Without Getting Hurt: Build Apps and Software with AI Without Writing Code: Think, Build, Test, Launch
English | October 8, 2026 | ASIN: B0HMDMRQKV | 84 pages | Epub | 406.86 KB
Today, you can build software by talking. But you have to know what to say.On January 31, 2026, a young guy discovered that the database of his app, built with AI in a few days and holding thousands of users, was open to anyone. Two unchecked checkboxes. This book exists because of those two checkboxes.Vibe coding is real: anyone can describe an app and watch it come to life in an afternoon, without ever having written a line of code. But the promise brings three traps with it: speed, houses of cards, open doors. To dodge them you need the one thing AI will not hand you: a method.In these pages you learn to think like someone who builds software, without becoming a programmer: the floor plan before the code, a work cycle that never loses the thread, the two-minute test that tells you whether your data is really protected, and a launch you will be proud of. With two true step-by-step stories, Sara and Elena, tested on real tools (Lovable, Claude and the like).If you have an idea and zero code, this book was written for you: so you can build it without getting hurt.With a foreword by Luigi Laura.Thirty years of software, three companies founded and sold: this is what I wish I could have read the day I started building with AI.Marco Reds

Vercel AI SDK 7 for Production Agents: A Hands-On TypeScript Guide to Building AI Applications from Scratch with Streaming, Tools, MCP, RAG, Durable Workflows, and Observability
English | September 24, 2026 | ASIN: B0HKY1M5DG | 228 pages | PDF | 3.39 MB
Building an AI demo is easy. Building an AI application you can actually trust in production is much harder.If streaming, tool calling, RAG, MCP, agent workflows, security, and observability sound overwhelming, Vercel AI SDK 7 for Production Agents gives you a practical path forward.Written for developers who are new to serious AI application development, this hands-on guide takes you step by step from your first reliable model call to complete production-ready agent systems. You do not need previous experience with AI agents, Vercel AI SDK, MCP, RAG, or durable workflows. Basic jаvascript or TypeScript knowledge is enough to get started.Instead of burying you in theory or giant code listings, the book builds one working layer at a time. You will learn why each component exists, how it fits into a real application, where it can fail, and how to make it more reliable before moving on.Key FeaturesHands-on TypeScript examples built progressively from scratchPractical coverage of streaming, structured output, tools, MCP, and RAGProduction-focused guidance on security, retries, timeouts, approvals, and cost controlDurable workflows, persistence, and human-in-the-loop agent patternsTesting, evaluation, tracing, debugging, and observability techniquesDeployment, scaling, CI/CD, and real-world production operationsPractical exercises that help you learn by building, testing, and fixing problemsMistakes are treated as part of the learning process. A failed stream, invalid schema, poor retrieval result, or unexpected tool call becomes an opportunity to understand the system better and improve it one step at a time.What You Will LearnYou will learn how to build reliable AI applications with Vercel AI SDK 7 and TypeScript, stream model responses, generate structured data, design safe tools, create multi-step agents, connect MCP servers, build production RAG systems, persist agent state, implement durable workflows, evaluate AI behavior, secure model and tool boundaries, control latency and cost, trace failures, and deploy agents for real users.Who This Book Is ForThis book is ideal for jаvascript and TypeScript developers, self-learners, backend developers, full-stack developers, and software engineers who want to move from basic LLM experiments to dependable production AI systems.No previous experience with AI agents, MCP, RAG, or Vercel AI SDK is required.Table of ContentsBuilding with Vercel AI SDK 7Streaming and Structured GenerationTools and Production Agent LoopsConnecting Agents with Model Context ProtocolBuilding Production RAG SystemsPersistence and Durable WorkflowsTesting and Evaluating AI SystemsReliability, Security, and Cost ControlObservability and Production DebuggingDeploying and Operating Production AgentsMove beyond AI demos and start building agents that are controlled, testable, observable, recoverable, and ready for production. Start building with confidence today.

Vegetable Gardening for Beginners: Grow 25 Easy Vegetables and Herbs With a Simple Planting Calendar That Fits Your Growing Season
English | October 8, 2026 | ASIN: B0HM9KGCRT | 261 pages | Epub | 8.21 MB
Your first vegetable garden, timed for your season, not someone else's.Most beginner guides tell you when to plant as if every garden had the same weather. Then fall tasks land in midsummer, tomatoes get pulled while they're still producing, and new gardeners give up.Vegetable Gardening for Beginners fixes that with a simple two-date system. Spring tasks count from your last spring frost, and fall tasks count back from your first fall frost. Look up your two dates once, and every planting time in the book fits your garden, whether your growing season is short, medium, or long.Inside, you'll find:25 full-color plant profiles: when to plant, how to space and care for each crop, how to tell it's ready, and the problems to watch forReady-made planting charts for short, medium, and long seasons, so you don't have to do the mathSeed starting made easy: the countback method, a day-by-day hardening-off schedule, and transplanting tipsA picture guide to the 10 most common garden pests, with low-toxicity ways to stop themA symptom troubleshooter for yellow leaves, flowers but no fruit, cracked tomatoes, and moreSeason-by-season to-do lists, including a warm-climate guide for zones 8-10Simple worksheets to plan your dates, map your beds, and record what workedEvery gardening term is explained in plain language, and the planting numbers are checked against university extension guidance. Whether you're starting with one raised bed or a few pots on the patio, this book helps you grow real food in your very first season.

Vector Databases in Depth: Index structures, quantization, hybrid retrieval, and multi-tenant operations at scale.
English | September 20, 2026 | ASIN: B0HKTT2T1M | 214 pages | EPUB (True) | 350.78 KB
Vector Databases in Depth is a practical engineering guide to the data layer that makes semantic search, recommendation, and retrieval-augmented generation practical. It starts from a single observation: vector search is a systems problem, not a library call, and the difference between a prototype and a production system is measured in the same units as any other distributed system - recall, latency, cost, isolation, and operational discipline.The book walks through the full stack - why vector search matters and where it fits, embeddings as a data type and the decisions that shape them, similarity metrics and the normalisation practices that make them interchangeable, approximate nearest-neighbour search and the recall-speed trade-off, HNSW in detail with its parameters and tuning workflows, IVF-PQ and disk-based indexes for corpora that exceed memory, quantization from int8 to binary and the rescoring patterns that recover lost recall, metadata filtering that participates in the index traversal, hybrid retrieval that fuses dense and sparse results, re-ranking and multi-stage pipelines, horizontal scaling with sharding and replication, multi-tenancy with enforced filter isolation, operations, monitoring and cost engineering, and the trends reshaping the field.It covers the failure modes that quietly wreck vector search: an embedding upgrade that invalidates every cached vector, a filter applied after retrieval that excludes the top-k, a shard that becomes hot because the tenant hashing function is not uniform, a quantization step that reduces recall by twenty points without any metric catching it, a re-ranker that adds three seconds of latency, an index that fits at build time and does not fit at query time, a multi-tenant query that leaks vectors across tenants. Each is presented with the failure, the countermeasure, and the operational tradeoff.

Understanding Microsoft Excel for Beginners and Seniors: A Simple Guide to Spreadsheets, Tables, Formulas, and Everyday Calculations (Smart App guide series Book 1)
English | October 7, 2026 | ASIN: B0HM7KQDYJ | 199 pages | Epub | 1.49 MB
Understanding Microsoft Excel for Beginners and Seniors is a practical, easy-to-follow guide designed for beginners, seniors, older adults, retirees, family helpers, and anyone who wants to become more comfortable using Microsoft Excel.Excel can seem intimidating when you first encounter its rows, columns, cells, menus, and many buttons. This book takes a simple, step-by-step approach to help you understand the basics without overwhelming you with complicated technical language.You will begin by learning what Excel is and how it can be used for everyday tasks. The book explains important concepts such as workbooks, worksheets, rows, columns, cells, and cell references. You will also learn how to create a new workbook, enter information, save files, open existing spreadsheets, and move around the Excel window with greater confidence.The guide then shows you how to create and organize useful spreadsheets. You will learn how to enter and edit information, select cells and ranges, adjust column widths, copy and paste information, use AutoFill, format numbers and dates, add borders and shading, and create simple tables. You will also discover how sorting and filtering can make lists easier to manage.A major part of the book focuses on formulas and everyday calculations. You will learn how to use basic arithmetic, cell references, SUM, AVERAGE, MIN, and MAX, as well as simple percentages. Clear examples demonstrate how these tools can be used for household budgets, expenses, and other practical calculations. The book also explains common formula errors and how to correct them.You will then explore practical ways to use Excel in everyday life, including creating budgets, tracking expenses, managing bills, building shopping lists, organizing personal information, recording appointments, and creating simple charts.Finally, the book helps you develop confidence through useful shortcuts, organization techniques, error-checking habits, file-sharing guidance, and simple projects for practice.Whether you are opening Excel for the first time or returning to it after many years, this guide provides a straightforward starting point for learning how to use spreadsheets with greater confidence and independence.

Ultimate Machine Learning with LightGBM Using TensorFlow: Master LightGBM, XGBoost, TensorFlow, Deep Learning, Feature Engineering, Explainable AI, and MLOps with Python
English | September 22, 2026 | ISBN: 816964500X | 435 pages | EPUB (True) | 10.31 MB
Boost the Model. Deepen the Intelligence. Ship to Production.Key Features● Get a free one-month digital subscription towww.avaskillshelf.com.●Hands-on LightGBM and TensorFlow mastery covering gradient boosting, neural networks, and hybrid model architectures.● Production-ready MLOps pipelines with Docker, Kubernetes, CI/CD automation, and cloud deployment for enterprise AI.● Explainable AI implementation using SHAP, LIME, and TensorBoard alongside ChatGPT and GitHub Copilot for accelerated development.Book DescriptionBuild Models That Win on Accuracy-and Systems That Win in Production.Machine Learning (ML) practitioners who can combine gradient boosting with deep learning, and deploy the results to production are the engineers solving the hardest problems in enterprise AI. Ultimate Machine Learning with LightGBM Using TensorFlow provides a practical, end-to-end guide for building intelligent, scalable, and production-ready ML systems using two of the most powerful frameworks in modern AI.You begin with ML fundamentals and ensemble methods, then advance through XGBoost, LightGBM, and TensorFlow neural networks before combining them into hybrid architectures that deliver performance. Each chapter integrates hands-on Python examples, feature engineering, hyperparameter tuning, explainable AI using SHAP, LIME, and TensorBoard, with AI-assisted development throughout using ChatGPT and GitHub Copilot.The final section addresses production deployment, covering MLOps practices, Docker, Kubernetes, CI/CD pipelines, and cloud deployment. Thus, by the end of the book, you will design, optimize, deploy, and manage enterprise-grade ML applications with technical depth and practical confidence.What you will learn● Build high-performance ML models using LightGBM and TensorFlow for real-world business applications.● Design hybrid AI architectures combining gradient boosting and deep learning for complex problem solving.● Optimise model accuracy through feature engineering, hyperparameter tuning, and explainable AI methods.● Develop production-ready ML pipelines using MLOps, Docker, Kubernetes, and automated CI/CD practices.● Implement end-to-end ML workflows from data preparation through cloud deployment with production confidence.● Leverage ChatGPT and GitHub Copilot to accelerate machine learning development and debugging workflows.Table of Contents1. Introduction to ML Landscape2. Ensemble Methods and Tree-Based Models3. XGBoost and Gradient Boosting4. Introduction to LightGBM5. Data Preparation and Feature Engineering for LightGBM6. Hyperparameter Tuning for Optimal Performance7. Practical Applications and Interpretability8. Introduction to TensorFlow9. Crafting and Training a Neural Network10. Efficient Data Pipelines with tf.data11. Building Hybrid and Stacked Models12. Seamless Integration with tf.data13. Advanced Topics and Real-World Case Studies14. A Final Project15. The Future of ML and Further Learning Index

Ubuntu 26.04 LTS Without Guesswork: A Hands-On Guide to Installing, Understanding, Securing, and Troubleshooting Resolute Raccoon-from GNOME 50 and Linux Commands to Networking and System..
English | October 8, 2026 | ASIN: B0HMF7VWL5 | 311 Pages | Epub | 1.24 MB
Master Ubuntu 26.04 LTS with confidence-without relying on unexplained commands or outdated tutorials.Ubuntu 26.04 LTS Without Guesswork is a practical, beginner-friendly guide to installing, using, securing, and troubleshooting Ubuntu 26.04.1 LTS. Whether you are moving from Windows, learning Linux for the first time, or developing stronger system-administration skills, this book takes you step by step from the fundamentals to real-world Ubuntu management.You'll learn how to install Ubuntu safely, work confidently with GNOME 50 and Wayland, manage software with APT and Snap, use the Linux terminal, understand permissions and sudo-rs, manage systemd services, configure networking and SSH, improve system security, recover from common problems, and explore Docker, Ubuntu Server, WSL, and modern development workflows.Rather than simply telling you what to type, this guide explains why things work, how to verify them, and what to do when they don't.A practical companion for anyone ready to use Ubuntu with greater confidence, independence, and control.

USE WHAT YOU HAVE: John Harrison
English | July 25, 2025 | ISBN: None | 61 Pages | EPUB (True) | 1.92 MB
You don't need to wait for more-you already have enough to begin.

This is America: A Short History of the United States (This World of Ours)
English | November 12, 2014 | ASIN: B00PKE094S | 242 pages | EPUB (True) | 4.85 MB
This Is America: A Short History of the United States, the latest in Berkshire's acclaimed "This World of Ours" series, is a unique and very short introduction to the United States of America, a nation that in recent history has been the most affluent, influential, and powerful on Earth, known for innovation as well as for its belief in its own "exceptionalism."

Think Better with AI: A Practical Guide to Smarter Research, Critical Thinking, and Better Decisions with Artificial Intelligence
English | October 11, 2026 | ASIN: B0GXVC776D | 74 pages | Epub | 244.04 KB
AI can help you work faster-but only when you build a better way to use it.Think Better with AI is a practical guide to mastering research, critical thinking, and decision-making with artificial intelligence. Rather than chasing disconnected prompts or relying blindly on fluent answers, this book teaches you a repeatable thinking system to frame questions, investigate claims, evaluate sources, and challenge assumptions.Inside, you will learn how to: * Move beyond confidence: Evaluate whether AI-generated claims are actually supported by credible evidence. * Build research plans: Turn broad, vague questions into focused inquiries with clear verification requirements. * Make better decisions: Compare options using explicit criteria and structured evaluation matrices. * Challenge your thinking: Use AI to uncover hidden assumptions and generate counterarguments without falling into confirmation bias.Complete with reusable templates, practical exercises, and clear verification checklists, this book ensures that AI supports your process while you retain independent judgment and full responsibility for your conclusions.