AI Game Production

LLMs are too big for single GPUs, but quantization fixes that. This book cuts through the hype to show you how to actually compress models using GPTQ, AWQ, GGUF, and NF4 without losing quality. You get real benchmarks, working code, and a clear way to pick the right tool for your hardware. Stop guessing and start deploying efficient models today.

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.

Make your Python AI code up to 100× faster using NumPy vectorization, Numba, parallel execution, and GPU acceleration. Learn through practical benchmarks and real-world optimization examples.

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.

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.

Building production-grade AI systems requires more than just calling an API or running a basic notebook—it demands mastery over hardware constraints, memory bandwidth limits, and distributed inference architectures.​My new book, AI Systems Engineering: From Prototype to Production, is now officially available. It’s a code-first, rigorous engineering manual covering everything from vLLM runtime internals, PagedAttention, and FP8/INT4 quantization to HNSW vector search, hybrid RAG, and autonomous multi-agent swarm architectures.​Stop wrestling with out-of-memory errors and sub-optimal latency. Master the infrastructure that powers modern AI at scale.

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.

Systems: How AI Scales reveals how production AI handles thousands of requests, shared models, queues, state, failures, security, evaluation, cost, and continuous change. Through intuitive stories, practical architectures, and accessible mathematics, discover what it takes to transform an AI capability into a service people can trust.Volume V of The AI Systems Series.

A model can recommend an action. But what gives it permission to act—and how does it verify what happened? Agent explores tools, authority, planning, execution, recovery, security, and oversight. It begins where Reasoning ends and leads to Systems, where one successful action must become a dependable service.

A fluent answer is not necessarily a reasoned answer. Through stories, examples, visual models, and real mathematics, Reasoning reveals how AI represents problems, searches possibilities, and verifies conclusions. It begins where Context ends—and stops at one crucial boundary: a decision is not an action. That is where Agent begins.

The known surrounds us, but only some of it becomes context. The unknown is what we ask AI to resolve. How does a fluent model know what matters—to this user, from this source, at this moment? Context follows one ambiguous instruction through retrieval, RAG, memory, graphs, MCP, provenance, security, and evaluation—showing how AI drives the known to derive the unknown.

Your company's answers are already written down. This book builds the machine that finds them.

AI in software development is about much more than generating code.Beyond Code Generation explores how AI can support the entire software development lifecycle—from understanding requirements and designing systems to development, testing, code review, deployment, and operations.