Books

Building a chat feature over your product docs? You need a vector database.Adding recommendations to your app? Vector database.Searching 100 million images by visual similarity? Definitely a vector database.Yet most engineers stumble into these projects unprepared. They don't understand the trade-offs between IVF, HNSW, and Product Quantization. They pick the wrong similarity metric. They scale the wrong way.This book is the missing manual. It covers:The mathematics of embeddings and why they workHow to choose the right algorithm for your latency and accuracy constraintsDeep dives into Pinecone, Milvus, Weaviate, Qdrant, and ChromaBuilding production RAG systems that actually workIndustry case studies from Spotify to JPMorgan Chase20+ ready-to-run recipes for common scenarios

You know how to code, but everyone else seems to "just get it" while you secretly Google and ChatGPT everything. The Software Realm DECODED is the patient mentor conversation you've been searching for, Peter asks the questions you're afraid to ask, and the Ultra Senior Developer explains what bootcamps skip and seniors assume you know. By the final chapter, the imposter syndrome disappears and systems finally make sense.

This series of test-driven small coding puzzles lets you code a database from scratch (no dependencies).We'll cover KV storage engines, LSM-Tree indexes, SQL, concurrent transactions, ACID, etc.

这个一个用代码手搓数据库的项目。你可以通过这个项目：学习数据库底层原理和计算机基础。提升技术深度。通过实操来锻炼编程技能。获得一个完整的个人项目。可以用在简历、面试中。项目全面实现了几个最重要的部分：KV 储存引擎。SQL 与关系型数据库。索引与数据结构。虽然范围很广，但是被拆分成了多个小步骤。每个步骤都很简单，最多几十行代码。你会发现，复杂的概念可以从简单的概念演变而来，可以说是从0开始发明数据库。 作者网站上精选了一些类似的资源：程序员如何学习底层技术?可以邮件订阅作者网站。

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 Gen 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? Grokking Gen 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.Grokking 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? Grokking System Design – 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? Grokking System Design – 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.

Most data engineering projects fail not because of technology—but because of design.Design-Driven Data Engineering reveals a powerful new approach: start with business design, shape clarity through information modeling, and only then build systems that scale. This book gives you the frameworks, blueprints, and real-world patterns to design architectures aligned with business value, analytics needs, and modern AI-era requirements. Whether you’re an engineer, architect, analyst, or technical lead, this guide shows you how to turn complexity into clarity—and build data systems that actually work.

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.

STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

Build AI agents that truly remember, reason, and act—entirely on user devices. Move beyond prompt engineering to create autonomous systems with graph-based memory using SQLite and LibSQL. Learn to implement hypergraphs, metagraphs, and vector search for privacy-first AI that scales to millions of entities. From personal knowledge graphs to production mobile apps, master the three pillars of agent autonomy: tools, memory, and reasoning. Real code, working examples, battle-tested in production. The future of AI is local, private, and in your hands.

Your API deserves more than just working, it deserves to be exceptional.