Books

JSON succeeded not because it was better--XML had schemas, namespaces, and 10 years of tooling, but because it understood modularity in ways XML never could. This book deconstructs how a deliberately incomplete format defeated a comprehensive standard, then shows you the production patterns (JSON Schema, JSONB, MessagePack, versioning strategies) that make JSON systems scale from prototype to billions of requests.

This isn't theory from an academic—it's hard-won wisdom from someone who has lived every challenge in this book. ? WHO THIS BOOK IS FOR: - Senior engineers aspiring to CTO or VP Engineering roles- New CTOs (under 2 years) seeking a comprehensive playbook- Experienced CTOs wanting fresh frameworks and validation- Technical founders wearing the CTO hat- Engineering managers planning their executive path

Pare de copiar padrões de design sem entender os problemas que os criaram. A arquitetura da Netflix não foi desenhada em um quadro branco; ela foi forjada por falhas críticas. Neste livro, mergulhamos nos bastidores técnicos da maior plataforma de streaming do mundo para entender os trade-offs reais por trás dos microsserviços, da resiliência e da escala global. Fugindo do "hype" e focando na engenharia, BehindTheStack disseca como o monólito foi quebrado, como a persistência de dados evoluiu e como a cultura de liberdade e responsabilidade moldou o código. Escrito para Arquitetos, Tech Leads e Engenheiros Sêniores que buscam profundidade além dos tutoriais rasos. Adquira a versão Early Access e acompanhe a escrita deste manual de sobrevivência em escala.

Stop reading 400-page theory books. This is a 30-page tactical field guide you can read during your lunch break and apply before you clock out. Learn the unwritten rules of clean code—without the fluff.

Master AI Agents from Architecture to ProductionBuild autonomous agent systems that actually work in production. This comprehensive guide takes you from understanding ReAct patterns to orchestrating multi-agent systems at scale. What You'll Master:✅ Agent architectures: When to use agents vs RAG vs fine-tuning✅ Reasoning patterns: ReAct, Chain-of-Thought, Plan-and-Execute✅ Multi-agent orchestration with proper coordination protocols✅ Production deployment with error handling, monitoring, cost optimization✅ Tool calling, memory systems, and context management Who This Is For: Software engineers building LLM applications, backend engineers adding agentic capabilities, senior engineers preparing for AI agent interviews at top companies. What Makes This Different: 100+ production-focused scenarios with real architectural trade-offs. Real-world examples from companies shipping agent systems . Stop building chatbots. Start building agents that take action.

Master Generative AI from Theory to ProductionYou don't learn Gen AI from tutorials — you learn from solving real problems. How does ChatGPT handle context and avoid hallucinations? How does Perplexity build RAG at scale? How does GitHub Copilot generate accurate code? System Design Mastery - Generative AI teaches through real-world scenarios and production patterns. 116 scenario-driven case studies covering:✅ RAG with vector databases and hybrid search✅ Prompt engineering with Chain-of-Thought reasoning✅ Document processing with multi-format parsing✅ Multi-modal AI with vision and audio✅ Production deployment with monitoring and cost optimization Every scenario includes: production problem, architectural approaches, Gen AI patterns, decision frameworks, tool implementations, and interview-ready explanations. Learn through case studies from OpenAI, Anthropic, Google, Meta, and top AI-companies. Your journey from developer to Gen AI architect begins here — with scenarios you'll face and tools you can deploy.

Master Data and ML Systems at ScaleYou don't master data platforms from textbooks — you master from solving real problems at petabyte scale.System Design Mastery - Data Analytics and Machine Learning teaches through production scenarios and case studies. 100 scenario-driven case studies covering:✅ Lakehouse architecture with Delta Lake, Iceberg, Hudi✅ Real-time pipelines with Kafka, Spark, Flink✅ Feature stores with Feast for training-serving consistency✅ MLOps platforms with MLflow, SageMaker, Airflow✅ Data quality frameworks with Great Expectations✅ Multi-region data sync and model serving at scale Every scenario includes: production challenge, architectural trade-offs, data/ML patterns, decision frameworks, and interview-ready explanations. Learn through real-world case studies from Netflix, Uber, Airbnb, Spotify's petabyte-scale data and ML architectures. Your journey from data engineer to architect begins here — with scenarios you'll face and systems you can build.

You don't master distributed systems from diagrams — you master from solving complex problems at scale. How does Netflix handle distributed transactions across regions? How does Uber orchestrate sagas for ride workflows? System Design Mastery – Advanced Track teaches through production scenarios and case studies. 124 advanced scenario-driven case studies covering: ✅ Event sourcing, CQRS, saga orchestration✅ Service mesh with Istio configuration✅ Distributed tracing with OpenTelemetry✅ Workflow orchestration with Temporal✅ Change data capture with Debezium✅ Multi-region architectures and conflict resolution Every scenario includes: production challenge, architectural trade-offs, advanced patterns, decision frameworks, and interview explanations. Learn through real-world case studies from Google, Netflix, Uber, Meta, and Stripe's planet-scale architectures. Your journey from senior engineer to architect begins here — with scenarios you'll face and patterns you can implement.

Master System Design from Theory to ProductionYou don't learn system design from textbooks — you learn from solving real problems. How does Netflix handle video streaming at scale? How does Uber route millions of rides? How does Slack deliver messages instantly? System Design Mastery – Foundation Track teaches through real-world scenarios and case studies. 124 scenario-driven case studies covering:✅ Database design, sharding, replication✅ Caching with Redis configuration✅ Microservices with Spring Boot & Kubernetes✅ Message queues with Kafka✅ Load balancing with NGINX✅ Monitoring with Prometheus Every scenario includes: production problem, architectural approaches, design patterns, decision frameworks, tool configurations, and interview-ready explanations. Learn through real-world case studies — from Google, Amazon, Meta, Netflix, and top tech companies. Your journey from developer to designers begins here — with scenarios you'll face and tools you can deploy.

O Jetpack Compose é o futuro da UI do Android. Domine como ele funciona internamente e torne-se um desenvolvedor mais eficiente com ele. Você também achará útil mesmo se não for um desenvolvedor Android. Este livro fornece todos os detalhes para entender como o Compose compiler e o runtime funcionam, e como criar uma biblioteca cliente usando-os.

Jetpack Compose是Android界面开发的未来。掌握其内部工作原理，让您成为更高效的开发者。即使您不是Android开发者，这本书对您来说也很有价值。本书详细介绍了Compose编译器和运行时的工作原理，以及如何使用它们创建客户端库。

Jetpack Compose 是 Android 使用者介面的未來。透過深入了解其內部運作原理，你將成為更有效率的開發者。即使你不是 Android 開發者，這些知識對你來說也很有價值。本書提供所有細節，幫助你理解 Compose 編譯器和執行時期是如何運作的，以及如何使用它們來建立客戶端程式庫。

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

What happens when software begins to think for itself? The Rise of AI-Native Software takes you on a journey through the evolution of coding, from human-written logic to AI-generated intelligence. Ayodeji Stephen Saliu reveals how artificial intelligence is transforming development workflows, redefining productivity, and birthing a new generation of “AI-native” applications that learn, adapt, and co-create with humans.Through vivid examples and frameworks, the book uncovers how developers can move beyond automation toward true cognitive collaboration. This is not a story of machines replacing engineers, but of a partnership that amplifies human potential, unleashing creativity, precision, and innovation at scale.