Books

AI can help engineers ship code they cannot explain. This complete, illustrated guide helps engineering managers and CTOs grow judgement alongside that speed, with a six-session mentoring course, reusable exercises, and the business case for junior hiring.

Build real machine learning applications with TensorFlow.js, from your first model to production-ready systems. Learn how to train and deploy models in the browser and Node.js, tackle real-world challenges and explore modern techniques with practical JavaScript and TypeScript examples you can run and adapt.

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.

A hands-on guide for engineers and architects to build, optimize, and deploy scalable production-grade AI systems and LLM infrastructure.

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.

A model that crashes is a good day. The dangerous failures return answers — plausible, fluent, and wrong. 100 failures. 63 diagnostic instruments. One rule: inspect what actually happened.

"I am not qualified to endorse theology. But I can witness: the structure is real. It was discovered, not invented."

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.

An Advanced Engineering Reference for GPU Architecture, Low-Level Optimization, and High-Performance ComputingModern GPU programming requires more than exposing parallel work to thousands of threads. It demands an understanding of the relationship between hardware architecture, instruction execution, memory movement, synchronization, and the software systems that coordinate computation across multiple GPUs. Mastering NVIDIA CUDA: Expert GPU Programming is a 100-chapter technical reference for experienced developers, high-performance computing engineers, researchers, and AI systems architects.

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.

Assess your AI visibility, identify the gaps and prepare your website for AI search — step by step, without SEO expertise. Built for small business owners who want to do the work independently.

Transform your AI potential into practical action. This handbook provides the professional toolkit you need to build, test, and deploy intelligent agents with confidence.