Books

Build real systems software with Odin. This practical guide takes you from the language basics to memory, concurrency, networking and performance. Learn how Odin works under the hood while building parsers, allocators, simulations and more, with complete examples and clear comparisons to C, C++, Rust and Go.

A Comprehensive Engineering Guide to Autonomous Agent Architectures, Multi-Agent Systems, Memory, Tool Execution, Reliability, Observability, and Production DeploymentAutonomous AI agents represent a fundamental shift from conventional conversational applications toward software systems capable of executing multi-step tasks, interacting with external tools, maintaining persistent state, coordinating specialized components, and adapting their behavior to changing conditions. Building such systems requires considerably more than connecting a language model to a prompt. It demands a disciplined approach to execution control, memory architecture, workflow orchestration, security, evaluation, and production infrastructure.Mastering AI Agents: Hands-On Production Code is a practical, implementation-focused engineering reference to designing, building, and deploying autonomous AI agent systems. Across 38 chapters, it progresses from low-level LLM runtime primitives and a zero-dependency Python agent loop to cognitive architectures, persistent memory, stateful workflows, multi-agent coordination, secure tool execution, production observability, and enterprise deployment.

Unlock the full potential of large language models with RAG. This book takes you from core concepts to production-ready systems, exploring everything from embeddings and search to evaluation and security. With clear explanations, runnable code and practical insights, you'll learn how to build reliable AI systems grounded in the knowledge that matters.

Explore how modern GPU virtualization brings near-native performance to Linux virtual machines. From hardware and KVM/QEMU internals to virtio-nvgpu and NVIDIA's shared GPU architecture, this book takes you inside the stack with practical configurations, source code analysis and deployment patterns for real-world systems.

Build reliable distributed systems with Spring Boot, RabbitMQ and modern Java. Follow OrderFlow as it grows from a simple service into a production-ready event-driven platform, exploring architecture, messaging, observability, containers and the practical trade-offs behind every design decision.

Apa yang tersisa dari kerja kita, selain riwayat commit? Sebagian pekerjaan selesai ketika laptop ditutup. Sebagian karya terus berjalan: website yang tetap dibaca, sistem yang tetap dipakai panitia, dokumentasi yang membuat pekerjaan tidak berhenti di satu orang. Allah berfirman: إِنَّا نَحْنُ نُحْيِي الْمَوْتَىٰ وَنَكْتُبُ مَا قَدَّمُوا وَآثَارَهُمْ ۚ وَكُلَّ شَيْءٍ أَحْصَيْنَاهُ فِي إِمَامٍ مُبِينٍ “Sesungguhnya Kami menghidupkan orang-orang mati dan Kami menuliskan apa yang telah mereka kerjakan serta bekas-bekas yang mereka tinggalkan. Segala sesuatu Kami kumpulkan dalam kitab yang jelas.” (QS. Yasin: 12) Buku ini adalah pengingat sebelum menjadi ajakan: bahwa waktu, keahlian, dan kesempatan belajar yang Allah titipkan tidak seharusnya berhenti hanya pada urusan pekerjaan. Di dalamnya ada sepuluh bentuk karya digital yang bisa dimulai seorang programmer — dari satu halaman jadwal kajian, pencatatan donasi, arsip materi, sampai mengajari satu orang. Semuanya disertai pagar niat, ikhlas, ilmu, dan cara. Bukan ajakan untuk menjadi sibuk terus-menerus. Ajakan untuk memilih satu karya kecil yang manfaatnya bisa berulang.

A practical guide to building reliable LLM integrations that hold up in production. Learn how to design provider-agnostic APIs, handle failures, add observability and security, and keep your architecture maintainable. With a complete Python codebase, this book turns proven patterns into working software.

Build, Train, Optimize, Secure, and Deploy Large Language Models Under Full Infrastructure ControlLarge language models are increasingly treated as hosted services, but organizations seeking genuine control over their AI capabilities must understand and engineer the infrastructure behind them. Data ownership, compute resources, model weights, training pipelines, inference runtimes, security boundaries, and operational telemetry all contribute to the independence and reliability of an AI system.

Start Your Computer Science Journey with Strong FundamentalsA beginner-friendly guide covering computer fundamentals, number systems, algorithms, programming, problem-solving, OOP, data structures, and file handling—with clear explanations, practical examples, exercises, and a step-by-step learning approach.

Python فێرببە، کۆد بنووسە و بیرکردنەوەی بەرنامەسازی پەرەپێبدە. ڕێبەرێکی پڕاکتیکی لە بنەماکانی Python ـەوە تا OOP، API، Testing، Concurrency، Clean Code و پڕۆژەی کۆتایی.

Take a practical deep dive into modern Haskell development with GHC, Cabal, HLS and effect systems. Learn how the tools work under the hood, how they fit together and how to use them effectively in real production projects. Built for engineers who want to move beyond syntax and develop Haskell with confidence.

Multivariable calculus, classical optimization, vector calculus, and multiple integration — the four pillars every engineer leans on — are gathered here into one connected text instead of four separate modules. Starting with partial and total differentiation, Jacobians, and Taylor's series, the book builds outward into Lagrange multipliers, KKT conditions, Newton–Raphson and gradient descent, then into gradient, divergence, curl, and the great integral theorems of Green, Stokes, and Gauss. Every worked example is carried through in full, with no derivation shortened for brevity. Engineering applications run throughout each chapter, tying the math directly to real design problems. Written by Gangadharaiah YH, Ph.D., it's meant to serve equally as a first course and a lasting reference on the engineer's shelf.

Stop asking one giant prompt to carry your whole software system. Context Driven Development teaches you how to give AI coding agents explicit requirements, architecture decisions, constraints, tasks, and verification loops so they can work inside a reliable engineering environment.

Spring AI gives you one API for chat models, embeddings, vector stores and tools, and swaps the provider with a single property. This book teaches it by building: thirteen chapters, seventeen complete Spring Boot projects, and every program run on a free local model and on a paid cloud model before a word was written about it. Start chapter 1 with no account, no API key and no credit card.