Large language models

Transform static maps into intelligent systems with Geospatial AI (GeoAI) with Python Programming. Bridge the gap between GIS and AI, from satellite image analysis to autonomous LangChain agents. Master production-ready code for GNNs, U-Net architectures, and real-time spatial dashboards. Stop just looking at the map—teach your code to understand it.

Stop building chatbots and start architecting autonomous digital workers that act, plan, and collaborate. Master multi-agent orchestration with CrewAI and build self-healing, cyclic workflows using LangGraph. Move beyond simple prompts to implement the OODA loop, browser automation, and production-grade security. Transform LLMs into reasoning engines capable of managing entire software agencies without intervention.

AI governance changes when AI stops merely producing answers and begins taking action. Runtime AI Governance provides the architecture, controls, evidence and assurance methods practitioners need to govern agentic AI while consequential actions are still observable, interruptible and accountable.

Your agent passed every load test and timed out in production anyway. This free practitioner's guide shows why the traditional performance playbook breaks for agentic AI, and what replaces it: latency budgets, bounded autonomy, token SLOs, and production-grade observability, from a performance engineering and observability architect with two decades in production.

The book is free — pay-what-you-want with a $0 minimum. If you find it useful and want to contribute, you decide how much. If you can't or don't want to, just download and read without worry: the Creative Commons BY-SA 4.0 license even allows you to redistribute and modify it, as long as you credit the source and maintain the same license.

Una guida pratica a Claude Code, scritta con Claude Code.Su Claude Code circolano due tipi di contenuti: video YouTube che si fermano ai primi dieci minuti e thread social che promettono "guide complete" in cambio di un'email. Questo libro nasce dall'esigenza opposta: avere finalmente un manuale che spieghi lo strumento per intero, senza hype e senza muri di lead generation.140 pagine costruite a partire dalla documentazione ufficiale Anthropic, verificate su Claude Code v2.1.123, con esempi reali invece che screenshot da marketing. Si parte dall'installazione su macOS, Linux e Windows, si passa per Plan Mode, CLAUDE.md, gestione del contesto, sicurezza dei permessi, Skill personalizzate, subagent custom, integrazione MCP, e si arriva al prompt engineering aggiornato al 2026 — quello vero, dopo che le formule magiche del 2023 si sono sgonfiate.Il libro è pensato per due lettori in parallelo: lo sviluppatore senior che vuole integrare Claude Code nel proprio workflow di produzione, e chi si avvicina per la prima volta agli strumenti AI agentici e ha bisogno di una mappa. I capitoli iniziali costruiscono le basi senza dare nulla per scontato; quelli avanzati entrano nei meccanismi che fanno la differenza tra un uso superficiale e uno strategico.Tre cose che troverai e che altrove fatico a trovare:Il prompt engineering riletto al 2026. Niente ruoli posticci, niente "agisci come un esperto di...". Le tre leve che contano davvero — istruzioni esplicite, contesto adeguato, esempi curati — applicate al contesto specifico della CLI.Workflow reali, non demo da slide. Esempi concreti su WordPress e Node.js presi da progetti veri, non da repository giocattolo.In apertura c'è una prefazione scritta da Claude Opus, a cui ho dato il manoscritto completo chiedendogli un commento onesto, anche dove il libro non funzionava. È rimasta lì come l'ha scritta: è il modo più trasparente che mi sia venuto in mente per inquadrare il libro.Il libro è gratis — pay-what-you-want con minimo zero. Se ti torna utile e vuoi contribuire, decidi tu quanto. Se non puoi o non vuoi, scarichi e leggi senza pensieri: la licenza Creative Commons BY-SA 4.0 ti permette anche di redistribuirlo e modificarlo, basta citare la fonte e mantenere la stessa licenza.

This book is the summary of everything you are going to need to learn how to deploy LLM to production. All the tips and tricks, and some interview questions.

ဥပမာအားဖြင့်၊ သင်က AI ကို "မိုးရွာရင် ထီးယူသွားပါ" ဟု ခိုင်းထားလျှင်၊ မိုးသည်းထန်စွာ ရွာနေသော်လည်း အိမ်ခေါင်မိုး ပြိုကျနေပါက ၎င်းသည် ထီးကိုသာ ကိုင်၍ အိမ်ထဲတွင် ငုတ်တုတ်ထိုင်နေပေလိမ့်မည်။ အဘယ်ကြောင့်ဆိုသော် "အိမ်ခေါင်မိုး ပြိုလျှင် ပြေးပါ" ဟူသော စည်းမျဉ်းကို သင်က ထည့်မပေးထား သောကြောင့် ဖြစ်သည်။

Reliable Generative AI bridges business use and technical architecture. It teaches the foundations of prompt design, RAG, agentic workflows, tool use, structured outputs, safety patterns, and evaluation without assuming the reader is a software engineer. The focus is practical: understanding how AI workflows fail, how to design around those failures, and how to build systems that professionals can trust.

Master automated workflows for your Python code with GitHub Actions for Python Projects. This practical guide covers automated testing, dependency management, coverage reports, documentation builds, and artifact generation to help you build reliable CI/CD pipelines effortlessly.

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.

This book gives senior technology leaders a practical operating system for enterprise AI strategy. It turns scattered pilots into a board-ready plan by walking through strategy, economics, vendor decisions, ownership, governance, GenAI architecture, and roadmap assembly: AI Platform Scorecard, Use-Case Prioritization Matrix, Budget Model + Cost Guardrails, Vendor Decision Framework, RACI Matrix.

Il giudizio non si delega. Il perimetro entro cui l'agente si muove, la verifica che deve passare, la condizione che lo ferma: quelli sì. Come un Senior trasforma vent'anni di mestiere nei vincoli che governano un agente, e come decisioni, criteri e lezioni restano quando la sessione finisce: è l'amplificazione del titolo.

Running LLMs locally is easy. Running them well is an engineering problem. Practical LLM Inference gets into the details that matter, from quantization and GGUF internals to GPU offloading, benchmarking and production deployment. Learn how to size hardware, find bottlenecks and build fast, reliable inference systems without the AI hand-waving.