Artificial Intelligence

Your agent's dashboard is green. Your evaluators report an 87% pass rate. Then a customer complaint reveals the system has been confidently fabricating regulatory citations for three weeks. The evaluators weren't broken — they were measuring the wrong things. This field guide exists because the gap between "we have evals" and "our evals actually protect us" is larger than most teams realize.

A 1986 text adventure game written inside the Cray I/O Subsystem turns out to be architecturally isomorphic to modern transformer design. This capstone volume uses the playable game and its assembly language source as a case study in constraint-based design, including the bare metal thinking we stopped teaching around 1995.

What if time is not merely a clock, but a filter? What if systems fail not because they lack information, but because correction arrives too late to matter? What if technologies become dominant not because they are best, but because they have already reorganized the future around themselves? And what if Human–LLM collaboration is no longer just a sequence of prompts and answers, but an evolving ecology shaped by its own artifacts? Force–Information–Time: Essays on Structural Evolution is a companion volume to the FIT framework. Rather than restating the core theory, it explores what becomes visible when structural thinking is carried across learning, institutions, science, technology, Human–LLM collaboration, and human life.

A cross-domain methodology for governing AI agents: four pillars, implementation patterns, and the missing layer between capability and control.

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

You already know AI exists. You know it could save you time. But how do you actually bring it into your daily Business Central work — writing AL code, debugging, building integrations with Azure OpenAI, or preparing documentation for a client?This book shows you exactly how.

Don't waste your time to learn how to code. That's what AI is here for. This book focuses on the far more profound question: What to code? This is the new meta skill between now and 2030!

A great technical book should be more than just an accumulation of knowledge; it should serve as a starting point for deep thinking and continuous practice. May this book accompany you on your journey of exploring LLM. May you not only gain a profound understanding of technical principles and a solid improvement in application skills, but also develop a keen technical intuition through practice, experience those enlightening “aha!” moments on key issues, and keep progressing with joy.

Forget the six-month boot camp hype—real AI/ML engineering is a rigorous, hands-on discipline. This practical field manual bridges the gap between marketing promises and real-world execution, guiding you through everything from complex distributed systems to midnight CUDA debugging. Discover the unvarnished truth and gain the clear direction you need to transition from an AI enthusiast to a true practitioner.

In a world of superhuman machine speed, the last human advantage isn't what you know—it’s how well you understand your own knowing.Stop trying to "keep up" with AI and start building your metacognitive moat. Whether you are a manager making high-stakes decisions or a knowledge worker navigating augmented workflows, this book provides the cognitive architecture to thrive.Don't just compete with the algorithm; master the thinking that directs it.

Víš, že AI existuje. Víš, že by ti mohla ušetřit čas. Ale jak ji skutečně zapojit do každodenní práce s Business Central — do psaní AL kódu, ladění chyb, tvorby dokumentace nebo integrace s Azure OpenAI?Tato kniha ti ukáže jak.

Discover the secrets to winning listing presentations in record time and creating virtual staging for less than the price of a lunch. No technical background is needed, and most tools cost minder dan a daily coffee. Let’s get started!

Learn LangGraph the fast, practical way by building real projects from your very first chapter. This hands-on guide cuts out the fluff and walks you step-by-step from simple graphs to powerful AI agents like ReAct and RAG. If you prefer learning by doing, this book will get you up and running quickly without overwhelming you.

Transform your Apple devices into AI powerhouses with native MLX Swift and local LLMs. Master "Metal-to-Model" workflows, leveraging unified memory for lightning-fast, zero-copy inference. Implement LoRA fine-tuning, 4-bit quantization, and real-time streaming for a superior user experience. Build the next generation of privacy-centric, offline-first AI applications directly in Swift 6.

Unlock the power of custom Machine Learning on Apple platforms without a single line of Python. Master Create ML Studio to train bespoke Image, Sound, and Motion classifiers entirely in Swift. Leverage Swift 6 concurrency and advanced MLOps to automate retraining and ensure model quality. Stop relying on generic AI—build and deploy your own native, private intelligence today!