Computers and Programming

Master Tcl 9.1 from the ground up with a practical guide that goes far beyond the basics. Explore the language, TclOO, metaprogramming, internals, C extensions and production architecture through clear explanations and runnable examples. Whether you are new to Tcl or upgrading, build the skills to write clean, reliable software.

You don't owe the world your code. Open-source culture has a default: build something good, publish it, license it, answer the issues, keep it compatible forever. The Sovereign Developer asks the question that default skips: what does this developer actually gain by making this work public, and what do they give up to do it?This is not an argument against open source. It's an argument against treating openness as the automatic answer for every project. Drawing on economics, law, and the realities of maintainer burnout, the book makes the case that keeping software private can be the better choice for a given developer, and if you've already published something that no longer serves you, it offers a framework for deciding what to do next.For developers who have felt pressure to open-source something without ever weighing the cost, this book makes the case for the other option.

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.

Build a real LLM inference engine from the ground up in portable C. Learn how transformers work, load SafeTensors weights, implement KV caching and optimize inference on the CPU. By the end, you will run TinyLlama locally and generate text with a complete working runtime.

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.

A Comprehensive Engineering Guide to Linux Kernel Graphics Architecture, DRM/KMS, GPU Memory Management, Command Execution, Scheduling, Virtualization, and Driver Development...

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.

Modern C++ Memory Safety shows you how to make existing C++ systems safer without starting over. Learn practical ways to find and fix memory safety issues using modern C++, compiler tools, sanitizers, static analysis and fuzzing, with techniques you can apply directly to real production code.

Run Jev-style decision models on your own hardware with Ollaya. Learn how these fast, structured models differ from LLMs, then build real applications with typed outputs, Python, JavaScript and MCP. From first setup to production, this practical guide shows you how to make AI faster, cheaper and more private.

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.