Built to Burn: Why Work Burns People Out and How the Best Leaders Build Cultures That Don't by Karl Treacher
English | November 17th, 2026 | ISBN: 1394456514 | 336 pages | True EPUB | 0.75 MB
Burnout is a leadership problem, not a people problem - and the solution is in your hands

Building Websites All-in-One For Dummies, 4th Edition by Paul McFedries
English | November 9th, 2026 | ISBN: 1394431538 | 768 pages | True EPUB | 3.45 MB
Create your own professional website without breaking the bank

Building User-Friendly DSLs
English | 2024 | ISBN: 1617296473 | 506 pages | True PDF | 20.07 MB
Craft domain-specific languages that empower experts to create software themselves.Domain-specific languages put business experts at the heart of software development. These purpose-built tools let your clients write down their business knowledge and have it automatically translated into working software-no dev time required. They seamlessly bridge the knowledge gap between programmers and subject experts, enabling better communication and freeing you from time-consuming code adjustments.InsideBuilding User-Friendly DSLsyou'll learn how to:Build a complete Domain IDE for a car rental companyImplement a projectional editor for your DSLImplement content assist, type systems, expressions, and versioning language aspectsEvaluate business rulesWork with Abstract Syntax TreesReduce notated DSL content in concrete syntax into abstract syntaxBuilding User-Friendly DSLstakes you on a carefully-planned journey through everything you need to create your own DSLs. It focuses on building DSLs that are easy for busy business experts to learn and master. By working through a detailed example of a car rental company, you'll see how to create a custom DSL with a modern and intuitive UI that can replace tedious coding activities.About the technologyHere's the central problem of software development: business users know what they need their apps to do, but they don't know how to write the code themselves. As a developer, this means you spend a lot of time learning the same domain-specific details your user already knows. Now there's a way to bridge this gap! You can create a Domain-Specific Language (DSL) that empowers non-technical business users to create and customize their own applications without writing any code.About the bookBuilding User-Friendly DSLsteaches you how to create a complete domain-specific language that looks and works like a web application. These easy-to-use DSLs put the power to create custom software into the hands of business domain experts. As you go, you'll cover all the essentials, from establishing structure and syntax of your DSL to implementing a user-friendly interface.What's insideImplement a projectional editor for your DSLWork with Abstract Syntax TreesEvaluate business rulesAbout the readerFor developers with jаvascript and web development experience.About the authorMeinte Boersmais a senior developer and an evangelist of model-driven software development and DSLs.Table of Contents1 What is a domain-specific language?2 Representing DSL content as structured data3 Working with ASTs in code4 Projecting the AST5 Editing values in the projection6 Editing objects in the projection7 Implementing persistence and transportation of ASTs8 Generating code from the AST9 Preventing things from blowing up10 Managing change11 Implementing expressions: Binary operations12 Implementing expressions: Order of operations13 Implementing a type system14 Implementing business rules15 Some topics we didn't cover

Building RAG Systems That Actually Work: A Practical Guide to Giving AI Accurate, Current, and Company-Specific Knowledge With Retrieval-Augmented Generation
English | September 18, 2026 | ASIN: B0HGWHVJMV | 149 pages | EPUB (True) | 1.82 MB
Every organization today sits on a mountain of information that its AI systems cannot reach. Companies have spent years accumulating internal documentation, product manuals, and institutional knowledge, yet the language models they deploy remain limited to public data. This gap between what a model knows from training and what it needs to know to be useful is the biggest barrier to enterprise AI adoption, and retrieval augmented generation, or RAG, is the dominant architecture for closing it. Feed the right documents to the model at the right time, and it stops guessing and answers from your actual knowledge base.This book is written for the engineer or technical leader who has moved past the tutorials and needs to build a RAG system that actually works in production. The internet is full of demos that look great on clean data and fail on real documents, and the difference between a prototype and a production system is engineering depth across a dozen interconnected decisions. Chunking strategies, embeddings, retrieval quality, reranking, and evaluation all have to work together, and this book walks through each one.Inside, you'll discover:* The anatomy of a RAG pipeline and where quality is silently lost* Document intelligence and chunking strategies that survive messy data* Embeddings, vector databases, and hybrid search that return the right context* Reranking and context selection that maximize what reaches the model* Evaluation frameworks that measure what matters instead of guessing* Advanced patterns, including graph based and agentic RAG* Production concerns, from scaling and cost to monitoring and maintenanceThe framework is deliberately tool agnostic, because the ecosystem changes too quickly to learn a single stack. What endures is the ability to reason about retrieval quality, diagnose failures, and design systems that improve with feedback. That skill is what this book builds.

Building LLM Applications with DSPy: Replacing manual prompts with systematic optimization
English | 2026 | ISBN: 1633435016 | 296 pages | True PDF | 5.41 MB
Get the eBook free when you register your print book at Manning."An essential, forward-looking guide."-Dhyey Mavani, Amherst CollegeDSPy (Declarative Self-improving Python), an innovative framework for prompt programming, replaces fragile and unpredictable manual prompts with clean, modular Python code that declares what a model should do, defines the inputs and response formats, and establishes clear evaluation metrics. InBuilding LLM Applications with DSPy, authorsSerj SmorodinskyandBrett Kennedypresent a systematic approach for treating prompts as programmatic artifacts rather than loose text strings, so you can build, scale, maintain, and improve complex AI applications with the structural integrity of traditional software.Practical from page one, this book mirrors a professional AI engineering workflow, taking you from simple classifiers to complex summarizers, advanced RAG, and agentic systems. ReviewerCyrus Nouroozi, a DSPy contributor, notes that the book "frames the whole subject around a single coherent thesis: prompt programming is a data-driven discipline analogous to machine learning." To support its strong focus on prompt programming as an engineering process, the book introduces the baseline -> evaluate -> optimize loop that eliminates the guesswork of traditional prompt design.Production applications require verifiable, consistent metrics, which are difficult to establish with conventional prompts. This book guides you through constructing custom evaluation metrics, setting up multi-threaded test runners, and calibrating LLM-as-a-judge protocols to gather hard data. Instead of deploying prompts on a whim, you compile and optimize them against structured validation sets. The resulting programs are mathematically proven to be more accurate, more cost-effective, and remarkably resilient to underlying model drift.The final chapters show you how to build DSPy into fully agentic pipelines, integrating conversation memories, and connecting agents to external environments via MCP. This focused 9-chapter book also introduces cutting-edge DSPy v3 features like SIMBA and GEPA optimizers to squeeze maximum performance out of smaller, cheaper open-weight language models. It is an indispensable resource for any modern programmer wanting to build robust, self-improving, and production-ready generative AI systems.What's inside* Build modular LLM applications using declarative Python signatures* Automate prompt optimization using SIMBA and GEPA* Construct rigorous, repeatable metric functions to verify outputs* Deploy reliable, multi-hop RAG systems and agentsAbout the readerThis book is designed for software developers, data scientists, and AI engineers with basic Python skills.About the authorSerj Smorodinskyis a DSPy contributor, data scientist, and AI engineer with over ten years of experience leading teams to build conversational AI, conversational chatbots, and agentic workflows for enterprise clients.Brett Kennedyis a data scientist with over thirty years of software development experience. He is a regular contributor to open-source projects and the author ofOutlier Detection in Python.Table of Contents1 Introduction to prompt programming and DSPy2 Basic prompting and DSPy3 Classifying user intent4 Evaluating DSPy programs5 Optimizing prompt examples6 Optimizing prompt instructions7 Custom modules8 Summarization and more effective metric functions9 Creating an agentic RAG-based chatbot

Building AI-Powered Distributed Architectures with Aspire
English | 2026 | ISBN: 1808492773 | 354 pages | True PDF | 49.14 MB
Build cloud-native distributed .NET applications with Aspire, ASP.NET Core microservices, generative AI, semantic search, and Azure deployment.Key FeaturesBuild an end-to-end distributed e-commerce system with AI capabilitiesIntegrate PostgreSQL, Redis, RabbitMQ, Keycloak, and Ollama with AspireDeploy secure, AI-enabled microservices to Azure Container AppsBook DescriptionBuild practical AI-powered distributed applications by seeing how microservices, messaging, security, data services, and generative AI work together in one project. This book guides you through building a cloud-native e-commerce application with independently deployed services, reliable communication, specialized data stores, and intelligent features.You'll use Aspire to orchestrate Catalog and Basket microservices, configure service discovery, and integrate PostgreSQL and Redis. You'll implement synchronous communication and asynchronous messaging with RabbitMQ and MassTransit, secure Basket APIs with Keycloak and JWT bearer tokens, and connect a Blazor frontend to the distributed backend.You'll then add generative AI capabilities using Microsoft.Extensions.AI, Microsoft Agent Framework, and Ollama. You'll build customer-support Q&A and semantic product search using embeddings and vector storage before deploying the application to Azure Container Apps.Written by software and solutions architect Mehmet Ozkaya, who has more than 15 years of industry experience and has taught more than 200,000 students, this book combines hands-on development with architectural guidance. By the end, you'll be able to design, orchestrate, secure, enhance, and deploy AI-enabled distributed .NET applications.What you will learnDesign cloud-native distributed systems with AspireBuild Catalog and Basket microservices with ASP.NET CoreOrchestrate PostgreSQL and Redis as backing servicesImplement event-driven messaging with RabbitMQ and MassTransitSecure microservice endpoints with Keycloak and JWT bearer tokensBuild enterprise AI features with Microsoft.Extensions.AI, Ollama, and Microsoft Agent FrameworkImplement semantic search with embeddings and vector storageDeploy distributed applications to Azure Container AppsWho this book is for.NET developers, software engineers, and solution architects who want to build cloud-native distributed applications and add generative AI capabilities will benefit from this book. It is also suited to developers exploring microservices, event-driven architecture, Aspire, and AI integration. Basic knowledge of C# and programming concepts is required.Table of ContentsIntroducing the AI-Powered eShop Architecture with AspireCloud-Native Distributed Architectures and Backing ServicesDeveloping Distributed Applications with AspireBuilding Your First Aspire ApplicationBuilding an eShop Distributed Microservices Architecture with AspireDeveloping the Catalog Microservice with PostgreSQL and AspireDeveloping the Basket Microservice with Redis and AspireSynchronous Communication Between Catalog and Basket Using Aspire Service DiscoveryAsynchronous Communication with RabbitMQ and MassTransit in AspireSecuring the Basket Microservice with Keycloak AuthenticationDeveloping the Client Blazor Web ApplicationDeploying the eShop Aspire Application to Azure Container AppsBuilding Generative AI Features with Microsoft.Extensions.AI, Microsoft Agent Framework, and OllamaBuilding Semantic Product Search with Vector Embeddings

Build Your Own Retro Computer: A Beginner Friendly Guide to Raspberry Pi Emulators Classic Games and the History of Computing
English | September 22, 2026 | ASIN: B0HKQ2B6DW | 109 pages | EPUB (True) | 724.26 KB
Build Your Own Retro Computer is a beginner-friendly guide to creating a Raspberry Pi system inspired by the computers and games of the past.Explore the history of personal computing, understand the hardware inside retro machines, and learn how to choose parts, install an operating system, configure emulators, and organize classic software responsibly. You'll also learn the basics of BASIC and Python through practical projects, including a retro console, information terminal, pixel clock, arcade control panel, and personal preservation workstation.With clear explanations, build checks, troubleshooting advice, and hands-on projects, this book helps you move from simply playing retro games to understanding how the machines behind them actually work.

Build Your Own JARVIS with Claude Code: Create a Personal AI Assistant That Thinks, Uses Tools, Automates Tasks, Controls Your Computer, and Works for You Step by Step
English | September 24, 2026 | ASIN: B0HKWNZNQM | 399 pages | Epub | 2.24 MB

Build Your Own AI Agent Framework: A Practical Comparison of Local Coding Agents
English | September 18, 2026 | ASIN: B0HGQ2MX31 | 159 pages | EPUB (True) | 1.67 MB
If you have spent any time with a modern coding agent, you know the strange feeling that comes with watching one work. You type a description of a feature, and the agent reads your repository, plans an approach, edits files, runs the tests, and reports back like a careful colleague. Then something changes. You notice the agent makes decisions you would not make, follows conventions that are not yours, and forgets constraints you explained 2 weeks ago. You also notice the whole setup belongs to someone else, the vendor, the model provider, and the subscription tier, and the price of that ownership is paid in ways you did not expect.This book exists because the industry has the direction of travel backwards. The companies that make coding agents want you to think of their product as the answer, a polished tool you subscribe to and trust to handle everything. What serious developers need is a framework they build themselves, choose the materials for, and own outright. The tools that exist today have reached the quality where this is finally possible, and this book gives you the practical comparison of local coding agents and the architecture for assembling them into a system that reflects your judgment.Inside, you'll discover:* How a coding agent actually thinks and works* The honest local versus cloud decision* Running models on your own hardware* Terminal agents, open source agents, and editors* Connecting tools with MCP* Memory, rules, prompts, and custom tools* Orchestration, verification, and securityThe tools are not the scarce resource anymore, and the judgment to use them well is. That judgment, that system, and that discipline are what this book is actually about, and the agents are simply the machines you practice them on. Go build your framework, because the future belongs to the developer who builds rather than rents.

Build Your First AI Agent with Claude Code: A Step-by-Step Developer Guide to Creating Autonomous AI Agents That Work While You Sleep - No PhD Required
English | September 18, 2026 | ISBN: 6610001329976 | 176 Pages | EPUB (True) | 1.65 MB
You picked up this book because you have watched the developer world change around you. You have heard the word agent repeated in every conversation and every job posting, but you are not sure what it means or how to build one. Maybe you already use AI coding assistants daily and you are ready to stop typing every command and start building systems that work on their own. You may worry this requires a machine learning degree or months of study. It does not. What you need is a clear, practical guide that builds you up to real, working agents you can deploy this week. This AI agent book for beginners gives you a structured way to learn AI step by step.