Books

What if your browser could run AI without sending your data to a server? AI in the Browser shows you how to build fast, private and fully local AI applications using WebGPU, WebAssembly and on-device LLMs. From GPU compute to a complete local chat interface, you’ll build everything yourself with practical code.

Somewhere there is a server with two GPUs for which no operational process exists. This book turns that into an operable platform — hands-on, in 23 labs: vLLM, KServe, LiteLLM, the NVIDIA GPU Operator, Keycloak, OpenBao, ArgoCD, pgvector. At the end your developers write code with local and external models through a governed gateway.

Forecasting is changing fast. This practical guide takes you from ARIMA and exponential smoothing to Transformers, PatchTST and foundation models like Chronos and TimesFM. With clear explanations, hands-on Python examples and an honest look at what works and what fails, you’ll learn how to build forecasting systems that hold up in the real world.

Build production-ready applications with TypeScript in the age of AI. Go beyond the basics to master type-safe development, modern full-stack architectures, LLMs, intelligent agents, tool calling and the Model Context Protocol. With practical, runnable examples throughout, this book shows you how to build, test, secure and deploy serious TypeScript AI systems.

My pitch was that rules written in CLAUDE.md get skipped, so you need enforcement. Then I measured it. In 22 trials with the ban written in, the banned command was never attempted. In 17 without it, 17 out of 17. This is the whole record: the instrument, every trial, the limits, and the harness so you can check me.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.

Cloud GPU rental costs more than compute time. It can also introduce recurring expenses, external dependencies, and additional questions about where your prompts, datasets, and model artifacts are processed. A local machine learning workstation gives you direct control over the hardware, software stack, and data workflows. The Local AI Stack is a technical engineering reference for building a Windows-based machine learning workstation with Linux environments, NVIDIA GPU acceleration, and virtualization. Across 140 pages, it follows the stack from firmware configuration and Windows virtualization features to CUDA-enabled containers, model inference, storage optimization, and system recovery.

The Quantization Black Book The definitive engineering reference for 4-bit quantization of large language models — from 70B all the way to 400B parameters. Written for the people who actually have to fold these models onto real hardware and keep them fast, accurate, and deployable. This is not an overview. It is a deep technical field manual covering the full quantization stack: the math, the algorithms, the hardware setup, the calibration process, the runtime kernels, and the memory tricks that make the difference between a model that runs and a model that doesn't.

Building a model that performs well on technical reasoning tasks requires more than scaling up training data. It requires deliberate control over problem difficulty, data quality, feedback signals, verification, and the progression from foundational skills to complex multi-step tasks.Advancing Technical AI via Curriculum Reasoning is a 119-page engineering blueprint for designing, training, and evaluating reasoning-focused language models. It presents a structured, 48-step workflow that connects curriculum learning with synthetic data generation, process supervision, automated verification, preference optimization, and evaluation across demanding technical domains.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.

What happens when AI agents stop working alone and start working as a team?Artificial Intelligence is entering a new phase in which intelligent systems can do more than respond to individual instructions. Multiple specialized agents can collaborate, divide complex tasks, communicate with one another, use tools, evaluate results, and coordinate their actions toward a shared objective.Multi-Agent AI Systems: The Complete Handbook for Building Intelligent, Scalable, and Autonomous Agent Teams provides a practical roadmap for understanding this emerging paradigm.The book begins with the fundamentals of multi-agent systems and explains why collaboration between specialized agents can be valuable for complex workflows. Readers will learn about hierarchical, peer-to-peer, and hybrid architectures, along with roles such as manager, planner, worker, critic, and supervisor agents.It then moves into modern frameworks and technologies, including CrewAI, AutoGen, LangGraph, MetaGPT, LLMs, vector databases, and agent memory systems. Practical chapters explain how to design agent teams, decompose tasks, establish communication protocols, manage shared memory, coordinate workflows, integrate tools and APIs, and recover from failures.Readers will also explore advanced concepts such as dynamic replanning, parallel execution, swarm intelligence, agent debates, self-organizing systems, human-in-the-loop workflows, and multimodal agents.The book goes beyond experimentation and addresses the challenges of deploying multi-agent systems in real environments. Cloud deployment, Docker, Kubernetes, monitoring, logging, scaling, evaluation, benchmarking, testing, and cost optimization are included.Real-world applications demonstrate how agent teams can support software development, research, customer service, content creation, and business operations.Equally important, the book examines AI safety, privacy, security, transparency, governance, alignment, and responsible AI development.Whether you are a student discovering agentic AI, a developer building your first agent team, a researcher exploring collaborative intelligence, or a professional preparing for the next generation of AI applications, this book provides a foundation for moving from individual AI agents toward coordinated intelligent systems.Understand the architecture. Design the team. Build the agents. Coordinate the intelligence.

The first thing many people hit with an AI coding assistant is a plateau. A correction made on Monday is gone by Thursday. This book is about the operating model above the prompt, where truth lives, how a correction becomes a standing rule that holds, and which judgments stay in human hands.

Build real-world web applications faster with Claude Code by your side. This hands-on guide takes you from web development fundamentals to production-ready full-stack engineering with React, Next.js, TypeScript and PostgreSQL. Learn practical AI-assisted workflows while building, testing, securing and deploying applications that are ready for real users.

Teaser TextExplore six advanced AI applications shaping the future.Build intelligent solutions for smart farming, AI-powered content creation, predictive healthcare, smart-city traffic optimization, conversational AI, and AI-generated music.From predicting crop yields and monitoring health to optimizing traffic and creating music with GANs, this volume demonstrates the practical possibilities of modern Artificial Intelligence.Six projects. Six domains. One journey toward advanced AI innovation.

Teaser TextBuild the future with advanced Artificial Intelligence.The final volume of the 50 AI Projects series presents 10 advanced projects covering AI code review, news bias detection, blockchain voting, deepfake detection, smart farming, AI content creation, predictive healthcare, smart-city traffic optimization, conversational AI, and AI-generated music.Explore emerging AI technologies while following the complete Software Development Life Cycle—from problem definition and system architecture to development, testing, deployment, and future enhancement.50 projects. 5 volumes. One complete journey into practical AI development.