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Category: "Large language models"

Books

  1. Under The Hood
    Build Every Layer of a Large Language Model from Scratch
    Ramchand Kumaresan

    Bestselling book on building LLMs. A practical, project-driven manual for engineers who want to understand how modern language models are built — and where they fail — by writing every layer themselves. From a scalar autograd engine to RLHF to fused specialists, in 36 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

  2. The Model Context Protocol (MCP) in Practice
    Building, Integrating, and Scaling Custom Tool Servers for AI Agents
    Yohan Rodriguez

    A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).

  3. Claude Code Masterclass
    Build Real-World Software with Claude Code, AI Workflows, and Hands-On Projects
    Luca Berton

    Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.

  4. Local AI Engineering with Ollama
    Run, understand, customize, fine-tune, and build agentic apps on your own hardware
    Aymen El Amri

    Pull a model onto a machine you own, shape it with a Modelfile, fine-tune your own adapter, and build a chat app that calls tools and talks to an MCP server, all running on your own hardware. By the end, you'll know exactly where owning your AI beats renting it, and where it doesn't.

  5. Claude Code: 기초부터 숙달까지
    2026년 에이전트형 소프트웨어 개발 완벽 가이드
    Steve Publications

    소프트웨어 개발 환경이 빠르게 변화하는 가운데, Claude Code는 이러한 변화의 중심에 있습니다. 이 책은 개발자가 AI 에이전트와 효과적으로 협업하여 코드를 작성하고, 워크플로를 자동화하며, 더 큰 규모의 프로젝트를 자신 있게 수행하는 방법을 안내합니다. 설정 및 프롬프트 설계부터 실무 엔지니어링 사례에 이르기까지 폭넓은 내용을 다루는 이 책은, 2026년의 현대적인 소프트웨어 개발 방식을 이해하고자 하는 모든 이에게 실용적인 로드맵을 제시합니다.

  6. Enterprise Intelligence Architecture
    A Reference Architecture for Governed, Memory-Centric Enterprise AI Systems
    Danny Francisco

    Large language models are not the architecture. Enterprise Intelligence Architecture shows how memory, governance, orchestration, validation, and model routing work together to build AI systems that are reliable, explainable, and production-ready. A practical blueprint for architects, AI engineers, and technical leaders.

  7. Kubernetes AI
    Run LLMs, GPUs, and ML Workloads in Production
    Luca Berton

    Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.

  8. AI-Native Software Testing
    Designing Self-Generating, Self-Healing, and Intelligent Test Systems with Playwright, AI Agents, and LLMs
    Srinivas Bommena

    Traditional test automation can execute scripts, but it struggles to explain failures, adapt safely to application changes, evaluate AI-powered features, or govern autonomous testing agents. AI-Native Software Testing shows practitioners how to design a modern testing architecture that combines Playwright, AI agents, LLMs, controlled self-healing, and governed CI/CD automation.

  9. DeepSeek V3
    Practical Guide for AI Learning Research & Professional Use
    Anshuman Mishra

    Master DeepSeek V3 — one of the most capable AI models of its time.This practical guide teaches students, researchers, and professionals how to use DeepSeek V3 for learning, research, coding, content creation, automation, and productivity — with strong focus on prompt engineering and responsible AI usage.

  10. Envoy Proxy: The Definitive Production Guide
    Architecture, Configuration, Operations, and Advanced Patterns for Cloud-Native Infrastructure and AI Workloads
    Steve Publications

    Envoy Proxy is the foundation of modern cloud-native networking, powering service meshes, API gateways and AI infrastructure. This practical guide shows you how to design, deploy and operate Envoy in production with expert guidance, real-world configurations and advanced patterns for scalable, resilient systems.

  11. LLMs Explained
    How Large Language Models Learn, Think, and Generate
    Steve Publications

    Large language models are changing the world, yet few people understand how they actually work. This book cuts through the hype, explaining the ideas behind modern AI with clarity, precision, and no unnecessary jargon.

  12. DSPy in Depth
    Programming Language Models from Zero to Production
    Steve Publications

    Learn to build production-ready LLM applications with DSPy through hands-on tutorials, complete runnable examples, and real-world projects. Master DSPy's core abstractions and create AI systems that improve with data instead of endless prompt tweaking.

  13. PydanticAI: Building Production-Grade AI Agents
    From Fundamentals to Advanced Agent Workflows with Python's Type-Safe Framework
    Steve Publications

    Learn how to build reliable AI agents with PydanticAI, from simple chatbots to production-ready multi-agent systems. With practical examples, clear explanations, and hands-on projects, this book helps you write AI applications that are structured, testable, and easy to maintain.

  14. Claude Code: From Basics to Mastery
    The Complete Guide to Agentic Software Development in 2026
    Steve Publications

    Software development is changing fast, and Claude Code is at the center of that shift. Learn how to work effectively with AI agents to write code, automate workflows, and build larger projects with confidence. From setup and prompt design to real-world engineering practices, this book provides a practical guide to modern software development in 2026.

  15. MCP is the protocol powering the next generation of AI agents, and this is the only book that teaches you all of it. From Python fundamentals to low-level SSE transport, go from zero to production-ready MCP developer.