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Building a Safe Room: Step-by-Step Instructions for Design, Construction, and Equipping Your Residential Safe Haven
English | 14 Jan. 2026 | ASIN: B0GGXN1V9P | 71 pages | EPUB (True) | 3.57 MB
When emergencies strike, preparation can mean the difference between safety and uncertainty. Building a Safe Room is your comprehensive, no-nonsense guide to designing and constructing a secure refuge inside your home. Whether you're preparing for natural disasters, home invasions, civil unrest, or unexpected crises, this book walks you through every stage of creating a reliable, protective space for you and your family. Inside, you'll learn: * How to choose the right location in your home * Structural requirements for maximum protection * Materials and tools needed for reinforcement * Step-by-step construction methods * Ventilation, power, and communication solutions * Essential supplies to stock for short- and long-term sheltering * Budget-friendly options and advanced security upgrades * Common mistakes to avoid Written in clear, easy-to-follow language, this guide empowers homeowners, DIYers, and preparedness-minded individuals to take control of their safety. You don't need to be a contractor or engineer-just someone who values protection and peace of mind. Whether you're building from scratch or reinforcing an existing space, Building a Safe Room gives you the practical knowledge to create a dependable sanctuary when it matters most.

Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI
English | 20 Jun. 2025 | ISBN: 1836206232 | 277 pages | EPUB (True) | 5.78 MB
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities Key Features Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j Apply best practices for graph exploration, modeling, reasoning, and performance optimization Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud Purchase of the print or Kindle book includes a free PDF eBook Book Description Embark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j. As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI's most persistent challenges-mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses. Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you'll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud. By the end of this book, you'll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications. What you will learn Design, populate, and integrate a Neo4j knowledge graph with RAG Model data for knowledge graphs Integrate AI-powered search to enhance knowledge exploration Maintain and monitor your AI search application with Haystack Use LangChain4j and Spring AI for recommendations and personalization Seamlessly deploy your applications to Google Cloud Platform Who this book is for This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy. Table of Contents Introducing LLMs, RAGs, and Neo4j Knowledge Graphs Demystifying RAG Building a Foundational Understanding of Knowledge Graph for Intelligent Applications Building Your Neo4j Graph with Movies Dataset Implementing Powerful Search Functionalities with Neo4j and Haystack Exploring Advanced Knowledge Graph Capabilities Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems Constructing a Recommendation Graph with H&M Personalization Dataset Integrating LangChain4j and SpringAI with Neo4j Creating an Intelligent Recommendation System Choosing the Right Cloud Platform for GenAI Applications Deploying your Application on Cloud Epilogue

Building Learning Communities with Character: How to Integrate Academic, Social, and Emotional Learning By Bernard Novick, Jeffrey S. Kress, Maurice J. Elias
2002 | 143 Pages | ISBN: 087120665X | PDF | 2 MB
Today's students need to learn more than just reading, writing, and arithmetic. They need to learn life skills to successfully manage tasks, form relationships, solve everyday problems, and adapt to the demands of simply growing up. To satisfy those needs, many educators seek effective and lasting programs for their students' academic, emotional, and social growth. This book presents a nine-step, problem-solving approach to help educators not only create such a program, but also shape the school climate to sustain and nurture it. Combining three decades of work with individual schools and districts, authors Bernard Novick, Jeffrey S. Kress, and Maurice J. Elias provide experienced insight to overcoming obstacles to social-emotional learning and character education programs. Their organized approach deals with the implementation process stage by stage, including: * Assessing your school's readiness for change * Setting goals for your program * Anticipating details and roadblocks * Obtaining feedback to modify your implemented program * Creating high standards for accomplishment in academics and character Any educator who is confused by the avalanche of different terminologies and methodologies surrounding social-emotional learning will benefit from this book. This practical and concise guide will help educators create an effective and goal-oriented environment in every school, one where students, staff, and parents are continually sustained in a caring community of learners.

Building Generative AI on AWS Bedrock: Develop LLM Applications with Python, RAG, and Agents
English | 14 Dec. 2025 | ASIN: B0G6X5S7B4 | 414 pages | Epub | 296.06 KB
Stop building "toy" chatbots. Start architecting enterprise-grade Generative AI on AWS. The era of experimental AI is over. Businesses today demand secure, scalable, and intelligent applications that solve real problems. Building Generative AI on AWS Bedrock is your definitive, hands-on guide to mastering the managed service that is redefining how developers build with Large Language Models (LLMs). Written for Python developers and cloud architects, this book bypasses the fluff and dives straight into the code. You won't just learn "what" Generative AI is; you will learn exactly how to implement it using the AWS SDK (Boto3) and the Amazon Bedrock API. From your first API call to deploying autonomous Agents that can execute complex business tasks, this book takes you through the entire engineering lifecycle. What You Will Build and Master: The Bedrock Foundation: Navigate the control plane vs. data plane, manage quotas, and programmatically switch between top-tier models like Anthropic Claude 3 , Meta Llama 3 , and Amazon Titan without rewriting your code. Production-Grade RAG: Move beyond simple tutorials. Build robust Retrieval-Augmented Generation pipelines using Knowledge Bases for Amazon Bedrock. Learn to ingest data from S3, configure vector stores (OpenSearch Serverless), and implement advanced retrieval patterns like Hybrid Search and Cohere Rerank to eliminate hallucinations. Autonomous Agents: Transform passive chatbots into active workers.1 Design Bedrock Agents that can reason, plan, and execute tasks by calling your own Lambda functions. Master the art of writing OpenAPI schemas that teach the AI how to use your internal APIs. Safety & Governance: Deploy with confidence using Guardrails for Amazon Bedrock . Implement PII redaction, content filtering, and denied topic policies to ensure your application adheres to strict enterprise compliance standards. Observability & Operations: Stop flying blind. Set up CloudWatch dashboards to track token usage, latency, and costs.2 Learn to audit every interaction with CloudTrail and optimize your spend using Provisioned Throughput and caching strategies. Why This Book? 100% Python & Boto3: No pseudo-code. Every chapter is packed with executable scripts you can drop directly into your IDE. Architectural Focus: Learn the patterns that matter-state management with DynamoDB, event-driven ingestion, and the "Human-in-the-Loop" workflow. Future-Proof Design: Discover how to build "Model Gateways" that allow you to swap underlying models instantly as new versions are released. Whether you are a backend engineer tasked with adding AI to a legacy app, or a cloud architect designing a new intelligent platform, this book provides the blueprints you need. Master the stack. Control the cost. Deploy the future. Scroll up and grab your copy today to start building on Amazon Bedrock.

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Dl4All is download free for all software and educational resource. With a little searching, you can download what you need for free without spending any money.

Dl4All - Download free For All: software and educational resource.

Building a Safe Room Step-by-Step Instructions for Design, Construction, and Equipping Your Residential Safe Haven

Building Your English Vocabulary

Building Small Language Models from Scratch

Building Reliable AI Systems (MEAP V09)

Building Neo4j-Powered Applications with LLMs Create LLM-driven search and recommendations applications with Haystack,

Building Learning Communities with Character How to Integrate Academic, Social, and Emotional Learning

Building Income with ChatGPT Practical AI Applications for Modern Entrepreneurs

Building Generative AI on AWS Bedrock Develop LLM Applications with Python, RAG, and Agents

Building Games with Swift

Building Event-Driven Microservices Leveraging Organizational Data at Scale