AI Game Production

Learn how modern LLM inference engines work by building one from scratch in Rust. From transformers and tokenization to KV caching, quantization, batching, and GPU optimization, this book combines theory, hands-on code, and performance engineering to help you create fast, production-ready AI systems.

Machine identities outnumber humans 82:1 in modern cloud environments. Every IAM role, every OIDC federation from your CI/CD pipeline, every service account — they accumulate silently. No one monitors them. No one audits them. Each one is a potential entry point for attackers. This book shows you how to build the platform that makes them visible.

Take a character from approved concept to a consistent, usable 3D game asset.

Organize AI generators, jobs, and settings for a repeatable game asset workflow.

Choose AI models for game assets by comparing real jobs, results, and costs.

Write clearer image-generation briefs for consistent game assets.

Direct AI-assisted game art from visual briefs to final polish. A practical workflow for solo indie developers.

Business and board leaders who need to understand cyber risk; solution and enterprise architects designing services; IT and security professionals working across teams; consultants advising customers; and service owners, people managers, and communications professionals with responsibilities during disruption.

Build Your School's Software with AI* is a practical, plain-language guide for teachers and school staff. No programming experience is required. Everything is explained the first time it appears, every prompt is copy-paste ready, and every chapter ends with a checklist and a small exercise.

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.

Explore the Future of AI-Powered RoboticsDiscover how Artificial Intelligence is transforming robots into intelligent machines capable of learning, seeing, communicating, navigating, and making decisions. Explore Machine Learning, Computer Vision, NLP, Deep Learning, autonomous systems, smart factories, healthcare robots, drones, humanoids, agricultural robotics, ethics, and future trends.

About the Book Running an LLM is one problem. Understanding its memory footprint, execution phases, and serving behavior is another.LLM Inference Engine Architecture explores the engineering behind language-model inference—from prefill and decode to KV-cache management, attention kernels, quantization, and request scheduling.Written for software engineers and ML engineers with a working knowledge of Python and transformer fundamentals, the book connects mathematical explanations with implementation examples and serving-system design considerations.What You Will ExploreInference fundamentals: Trace the request lifecycle and understand the different computational characteristics of prefill and decode.Memory budgeting: Work through model-weight and KV-cache estimates, including the effects of context length, KV-head count, precision, and concurrent sequences.Attention and memory management: Explore PagedAttention, non-contiguous KV allocation, and the relationship between attention computation and memory access.Quantization: Examine weight and KV-cache precision choices, including FP8 and low-bit weight-quantization approaches.Serving architecture: Study continuous batching, request scheduling, multi-GPU execution, and latency and throughput metrics.Kernel development: Explore CUDA and Triton concepts, kernel fusion, and a quantized matrix-multiplication case study.The book includes numerical examples, reference code, and an educational asynchronous serving simulator. These examples are intended to explain mechanisms and trade-offs—not to replace hardware-specific profiling or production validation.Read the sample to review the mathematical level, coding style, and scope before purchasing.

Master deep learning with PyTorch & Lightning. Core concepts, practical Q&A, and production-ready code with a full companion GitHub repository.

Build and publish TapGlint, a fast reflex web game with a global leaderboard, using browser-based AI tools and no prior coding experience. This hands-on beginner's guide teaches clear prompting, step-by-step app building, online data with Supabase, debugging, mobile-friendly polish, and deployment to a live link you can share.

Learn Python from the ground up with clear explanations, practical examples, and a beginner-friendly approach. Build a strong foundation in programming and gain the confidence to move toward real-world Python projects.