Artificial Intelligence

Build agents that write code, fix tests, and commit changes while you sleep — safely, cheaply, and entirely on your own hardware (622 manuscript pages).

Most people use AI. Few understand how far they can take it. AI Levels gives you a practical roadmap from prompting and everyday AI use to workflows, agents, specialized systems, and AI building—one skill at a time.

AI 越来越会回答、会行动，但下一种真正重要的智能，会不会是"发现"？《当 AI 开始发现》借 AlphaEvolve、自驱实验室、自动证明与世界模型，带你看见智能的一次转向。

La inteligencia artificial está cambiando las reglas. AI Levels te lleva, paso a paso, de usuario de IA a creador de soluciones inteligentes: aprende, crea, automatiza y construye con IA.

Meta Muse changes the way you work with AI. This practical guide shows you how to write better prompts, build reliable workflows, automate everyday tasks and avoid common mistakes. Whether you're new to agentic AI or already putting it to work, you'll learn how to get more done with less friction and more confidence.

What does it take to turn an AI agent from a clever demo into something you can trust in production? This hands-on guide uses DeepSeek Harness to show how to build reliable, secure and observable agents, from a simple script to a production-ready service with budgets, tracing, evals and multi-tenant support.

Find your next useful Claude prompt in a library of 1,000 templates across 20 domains. Customize clear instructions for writing, business, development, research and everyday work—with practical guidance for checking the results.

Get started with Claude through 18 accessible lessons, original diagrams and 28 adaptable prompts. Practice with guided exercises and learn to check your results—no coding experience required.

What if the biggest danger in AI isn’t that models fail—but that people trust them when they shouldn’t?Cognitive Trust Architect explores how to build AI systems that are not merely powerful, but worthy of reliance. Blending AI architecture, security, safety engineering, human factors, statistics, formal methods, governance, and agentic systems, it shows how to surround probabilistic models with boundaries, evidence, oversight, and deterministic controls.

Build the engineering foundations required to design and develop production-grade AI systems—from Python and FastAPI to databases, security, semantic search, RAG, LLM architecture, and AI application performance.Go beyond Python programming and start thinking like an AI Engineer.The AI Engineer — Volume I takes you from professional Python engineering to the foundations of modern AI application development.You will build the engineering knowledge required to create reliable AI systems—not just experimental notebooks or simple API calls.A project-based AI engineering book covering Python production engineering, FastAPI, PostgreSQL, authentication, testing, reliability, embeddings, semantic search, RAG, LLM architecture, AI pipelines, caching, and performance optimization.AI EngineeringAI EngineerArtificial IntelligenceGenerative AILLMLarge Language ModelsPythonFastAPIRAGRetrieval Augmented GenerationSemantic SearchEmbeddingsVector SearchLLM ApplicationsAI SystemsMachine Learning EngineeringBackend EngineeringProduction AIAI ArchitectureSoftware Engineering

You watched a language model do half an hour of work on its own. This book is about the question that came next: how much of it can you trust?

Get more from Gemini 4 Argon with practical prompting techniques that work on real-world tasks. This guide covers coding, research, legal and financial work, long-horizon workflows, optimization and prompt evaluation, with clear examples and strategies for getting reliable results from one of the most capable AI models available.

Build an AI app people pay for, no coding required. The SHIP Method shows non technical readers how to find a money problem, prove demand, build with AI, get users and charge.

Inference engineering is now its own discipline, with unique challenges around speed, cost, reliability and scale. This practical guide takes you from core concepts to production systems, showing how modern inference architectures work, how to optimize them and how to make better engineering decisions through measurement and trade-off analysis.

Build one real AI system end to end, from raw job postings to a deployed, tested API, and learn production AI engineering on the way. 24 chapters, runnable code, and numbers you can reproduce.