Computers and Programming

Building reliable distributed systems is harder than it looks. This book takes you from the fundamentals of message queues to the realities of distributed transactions, with working code, practical patterns and production lessons to guide you. Learn how to handle failures, maintain consistency and build systems that keep working when things go wrong.

Your AI application works on your machine. What happens when it reaches production? Learn to deploy, monitor, secure, and scale AI systems using Docker, CI/CD, Kubernetes, cloud infrastructure, and practical production engineering. Move beyond prototypes and build AI applications you can operate with confidence.

Build AI inference systems that run fast, use less memory and keep your data where it belongs. This hands-on guide walks you through model deployment, performance tuning, security and production engineering with practical code and real benchmarks. No cloud dependency, no surprise bills, just efficient AI systems you control.

Go from your first MATLAB script to professional engineering projects. Master programming, data analysis, visualization, numerical computing, and AI-assisted workflows through practical, project-based learning.

A 3-day path for people who want to code with AI but get stuck before the first line: Python basics in the browser, then a real setup on your own computer, then a page with a data table published on GitHub Pages. No memorizing. You learn to read, run, and check the code the AI writes.

Plain-language explanations, Java implementations, and step-by-step visual walkthroughs help you connect the problem, the code, and the changing state of the data. Every algorithm is printed in full color, so rows, columns, regions, and changing values stand apart at a glance and are easy to trace. 

Build a real compiler from the ground up with Rust. Follow the journey from source code to native x86-64 machine code as you design Lumina, explore optimization and register allocation and tackle the practical challenges that come with building a production-quality compiler.

The engine never crashed. Every test passed. Then the opening auction cleared, order flow jumped fivefold, and for eight minutes it matched every order correctly against a book that was already 700 milliseconds out of date. Several hundred clients got fills that were individually plausible and collectively wrong. Nothing in the logs said why.This book is about that gap between "thread-safe" and "deterministic," and how to close it: lock-free CAS design, cache-line layout measured with perf c2c, zero-allocation hot paths, jitter hunted down to the interrupt level, and proof that the same event log replays byte-for-byte identical ten thousand times, including after a crash.

What does it take to turn an AI agent from a clever demo into something you can trust in production? This hands-on guide uses DeepSeek Harness to show how to build reliable, secure and observable agents, from a simple script to a production-ready service with budgets, tracing, evals and multi-tenant support.

Build reliable LLM applications from first prompt to production agent. AI Engineering Visualized uses memorable doodles, practical Python, RAG, evals, tool calling and safety patterns to help you ship AI systems that work on Monday.

Den här boken lär oss Podman genom att faktiskt använda det, laboration för laboration, från vårt allra första podman run till en komplett, rootless produktionsmiljö som startar sig själv efter en omstart. Vi bygger egna images, kopplar ihop containrar över nätverk vi själva skapar, sätter upp persistent lagring för riktiga databaser och lär oss felsöka system som faktiskt gått sönder, med flit.

Build a Deep Technical Understanding of How Modern Browsers Reveal Device IdentityBrowser fingerprinting has evolved far beyond traditional cookies and persistent tracking identifiers. Modern websites can derive identifying signals from browser internals, graphics hardware, JavaScript runtimes, network protocols, rendering engines, media subsystems, and operating system characteristics. When combined, these signals can help distinguish devices and correlate browsing sessions even when conventional tracking mechanisms are unavailable.Modern Browser Fingerprinting: Techniques and Code is a comprehensive technical guide to the architecture, mathematics, implementation, and practical applications of browser fingerprinting. It examines how individual signals are generated, how their identifying value can be measured, and how multiple observations can be combined to construct probabilistic device profiles.Combining technical explanations with source code, controlled experiments, telemetry analysis, and statistical methods, the book explores browser fingerprinting across security research, fraud prevention, bot detection, web analytics, and digital privacy. It also examines the limitations of fingerprint-based identification, including measurement noise, browser updates, hardware changes, signal instability, and privacy-preserving countermeasures.

A capture that's too big, a complaint that's too vague: this book teaches you to get from there to the packets that explain it. Twenty chapters and 80 exercises on real captures take you from your first capture to TLS decryption, scan detection and your own Lua dissector, with the output of commands run in the lab cited on the page so you can check it yourself.

Get more from Gemini 4 Argon with practical prompting techniques that work on real-world tasks. This guide covers coding, research, legal and financial work, long-horizon workflows, optimization and prompt evaluation, with clear examples and strategies for getting reliable results from one of the most capable AI models available.