Artificial Intelligence

Enough talk, let's get started today and make sure you get ahead in 2026! This is AI for Influencers: Beat the Algorithm, 10x Your Output, and Score the Biggest Brand Deals!

You are studying in one of the strangest moments in the history of education. The institution testing you is measuring skills from twenty years ago...!

You are not going to be replaced by AI. You are going to be replaced by a VA who uses AI. This book makes sure that VA is you.

Your AI agent is only as smart as what it remembers. Most developers treat knowledge representation as an afterthought -- a data structure problem, not an architecture problem. They flatten complexity into schemas that can't express relationships, deploy embeddings without grounding, and build agents that degrade under their own reasoning load. This book changes that. **TypeDB for Edge AI Agents** is a practitioner's guide to building knowledge systems that actually work at the scale and complexity your agents demand. TypeDB is built on type theory -- the same mathematics that powers formal verification and programming language safety. For agents, that means you can express constraints that prevent bugs in your knowledge layer, reason about what's possible and what's forbidden, and build memory systems that don't require constant hallucination detection. With the Rust rewrite in TypeDB 3.0, your agents can carry sophisticated knowledge graphs on-device -- no round-trip to a server, no latency, no external dependencies. This book covers everything from the PERA data model and OWL ontologies to promise graphs for multi-agent coordination, giving you the patterns to design knowledge systems that scale without becoming incoherent. Whether you're an ML engineer, AI architect, or backend developer building production agent systems, this book bridges the gap between type theory and working code. You'll learn to design knowledge graphs that don't decay, structure agent memory that doesn't degrade over time, and coordinate multi-agent systems that respect causality and distributed constraints. Stop building agents that confidently hallucinate -- start building agents that reason correctly, remember reliably, and coordinate with certainty.

Meaning is not a treasure waiting to be found. It is a craft waiting to be made.

Gradient-boosted trees have dominated tabular ML for a decade. A new class of pretrained models just broke through — making accurate predictions on unseen datasets in seconds, with zero gradient steps on your data. This book explains why it works, when it fails, and how to deploy it.

从 AI 使用者，升级为 AI 系统创造者——亲手打造你的 24 小时智能助理。

A book sharing both the conceptual and practical part of experiments carried out with AIs on the integration of humans and AIs as team members.

The reason you can't have everything is also the reason anything exists at all.

If a humanoid robot carries about $28,000 a year in fixed cost, then at $60 contribution per billable hour it breaks even at roughly 467 billable hours per year, or just under 9 hours per week. But if supervision is heavier and contribution drops to $28 per hour, break-even jumps to 1,000 hours per year, or about 19.2 hours per week.

Stop building static interfaces for dynamic AI and master the art of reactive, intelligent design.Leverage Swift 6 concurrency and @Observable to handle real-time token streaming and async outputs.From SwiftData persistence to animated visualizations, learn to architect production-ready Apple apps.The ultimate guide to building fluid, professional user experiences powered by modern AI models.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

AI doesn't just assist physicians anymore — it reasons. It generates diagnoses, synthesises evidence, and executes clinical workflows. The question isn't whether to use it. It's whether we'll use it wisely.Vibe Medicine introduces the Orchestrator Physician: a clinician who commands AI systems while providing what they cannot — the right questions, human context, judgment under uncertainty, and full accountability. Part manifesto, part practical guide, part honest reckoning with the dangers — this is the book the medical profession needs right now.

Stop building dashboards. Start building outcomes. A guide to designing "Invisible Engines" and agentic systems for a world tired of software.

Most organizations know they have an AI problem. Few have the tools to diagnose it honestly, measure it rigorously, and close it systematically.