Artificial Intelligence

Are you ready for Legislative Power 5.0? Artificial Intelligence is a powerful tool and ally in the art of Lawmaking. Technology has changed the rules of the game. This book teachs you how to master them.

Systems fail in ways that existing frameworks cannot see. Not because the knowledge was absent — because it failed to function. Eleven failure modes. One common grammar. A field guide for practitioners who run complex systems and know something is wrong before they can name it.

Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.

Most companies bolt AI onto old workflows. This book shows how to rebuild the company around it: orchestrating AI agents to run real operations, with the governance and cost control to keep them accountable. Written for founders, operators, and leaders who want systems they own, not another subscription.

MCP is the protocol powering the next generation of AI agents, and this is the only book that teaches you all of it. From Python fundamentals to low-level SSE transport, go from zero to production-ready MCP developer.

We’ve spent years designing screens.The future belongs to designers who can design systems that learn.Designing Adaptive Products explores adaptive UX, personalization, behavioral signals, dynamic interfaces, and the balance between automation and human control, redefining what product design looks like in the AI era.

Your first AI integration is usually simple: one provider, one SDK, one model. Then production happens. Costs rise. Rate limits appear. Teams need access control. Security wants API keys centralized. Product wants to test better models. Finance wants to know who is spending what. Suddenly, the real problem is not calling an LLM. The real problem is operating AI as shared infrastructure. The End of AI Vendor Lock-In shows how LiteLLM helps teams build provider-agnostic, resilient, cost-aware AI systems that can route across models, manage fallbacks, track spend, enforce budgets, and reduce dependency on any single provider. If you are building AI products that need to survive real-world production pressure, this book is for you.

92% of developers use AI daily. Entry-level jobs dropped 73%. This isn't a story about replacement — it's about transformation. Data-driven guide with a 30-day action plan for developers who want to thrive, not just survive.

Monday morning. You typed "add multi-tenancy to the billing service" into Claude Code. Eight hours later: forty-three files touched. Half the tests yellow. You cannot explain to your tech lead what was decided, by whom, or against which constraint.The code looks fine. The intent is gone.The Clarity Forge is the antidote — a small, opinionated pipeline that forces every fuzzy idea through explicit spec, structured interrogation, and tailored artifacts before a single line of production code is written.Six stages. Six copy-paste prompts. One iron rule:The spec is the durable artifact. The code is the side effect.Pairs the frontier-grade Architect (Opus, GPT-5, Sonnet 4.6+) with a local Contractor (Gemma, Qwen). Pairs OpenSpec's directory convention with the Grill skills that surface ambiguity before it metastasises into code.A weekend read. A Monday-morning toolkit. Worked example included.Stop vibe-coding. Start clarity-trading.

Your AI prototype works. Now ship it. Most AI frameworks are built for exploration. kdeps is built for production. Define your agent in YAML, declare its dependencies, and deploy it anywhere — Docker, Kubernetes, a standalone binary, an edge device — without rewriting a line when you switch LLM providers. AI Appliances is the hands-on guide to building autonomous AI agents and multi-agent systems with kdeps: deterministic pipelines, real error handling, real deployment, and no vendor lock-in. Write YAML. Run anywhere. Own everything.

Build defensible evaluation systems for Gen AI, RAG, agents and multimodal applications using datasets, metrics, release gates, monitoring and seven interactive labs.

A leader at a company opens Slack one morning. The brief their virtual employee wrote looks normal: same headers, same prose voice, same cadence as every other brief that week. The last line ends mid-sentence. The API returned `stop_reason: "max_tokens"`. The system shipped it anyway. No exception. No log line. No retry. That bug doesn't look like the normal kind. This book is about why, and what to build instead.

Most developers lose an hour a day to Claude Code without knowing why. The output looks wrong. A convention gets ignored. You press Yes 20 times and it still doesn't finish. It's not the tool. Nobody told you how it actually works. I built production agentic systems with Claude Code and wrote down everything I learned the hard way. Building with Claude Code: The Agentic Development Handbook 16 chapters. 6 failure modes. One complete real session from empty directory to working agent. Not documentation. Not a tutorial. The book you want after your first week.    

Learn to build multi-agent AI workflows that automate your job. Master LangChain, CrewAI, and Claude to create production-grade agent crews for coding, content, customer support, and sales automation.

45% do código gerado por IA tem vulnerabilidades. Este livro cobre prompt injection, vazamento de dados, codificação segura com ferramentas de IA, hardening de servidores MCP, sandboxing de agentes e compliance OWASP LLM. 14 capítulos com demonstrações de ataque e código de defesa.