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LangGraph Deep Agents: Building Long-Running Autonomous Systems: From Task Lifecycle to Production Deployment - A Complete Guide to Agents That Run for Hours, Days, or Weeks
English | June 8, 2026 | ASIN: B0H4F2P7LZ | 146 pages | Epub | 671.76 KB
Most AI agents die within seconds. They answer a question and vanish - stateless, ephemeral, forgetful. But the real work of organizations doesn't fit in a chat window. Investment research takes days. Market monitoring runs 24/7. Compliance audits span weeks. These workflows demand agents that persist, pause, resume, and collaborate with humans over extended time horizons.**LangGraph Deep Agents: Building Long-Running Autonomous Systems** is the definitive guide to building production-grade autonomous agents using the LangGraph Deep Agents framework (v0.3.15 / 1.2.0, May 2026).Written by AI systems architect Alex Chen, this book teaches you to build agents that:- **Run for extended durations** - 24-hour research agents, continuous market monitors, multi-day compliance workflows- **Survive interruptions** - checkpoint state every 15 minutes, resume from failures without losing progress- **Collaborate with humans** - interrupt for approval at critical decision points, escalate anomalies with severity classification- **Delegate intelligently** - break complex tasks into subtasks, assign to specialized child agents, aggregate results- **Report incrementally** - stream progress, findings, and status updates in real time through delta channels- **Deploy reliably** - containerize with Docker, orchestrate with Kubernetes, monitor with LangSmith, recover from disasters**What you will build:**- A task lifecycle management system with create, start, pause, resume, complete, and cancel operations- A human-in-the-loop framework with threshold-based, confidence-based, anomaly-based, and schedule-based interrupt triggers- A multi-agent delegation system with automatic task decomposition and hierarchical state management- A checkpoint persistence layer supporting SQLite, PostgreSQL, and Redis backends with delta compression- A delta channel streaming system for real-time agent monitoring and incremental reporting- A LangSmith-integrated observability pipeline with custom spans, metrics, and anomaly alerts- A 24-hour research agent that ingests documents, extracts claims, detects patterns, and synthesizes reports- A real-time trading monitor with baseline models, anomaly detection, risk assessment, and human escalation- A complete Docker and Kubernetes deployment with health checks, auto-scaling, and disaster recovery

LadybugDB for Edge Agent AI Memory (Graphs for AI Agents Book 2)
English | March 9, 2026 | ASIN: B0GRWCNV5Y | 312 pages | Epub | 3.01 MB
Vector stores don't think - they search. They find fragments that sound like your query, then forget they ever looked. Every session starts from nothing. Every context window is a memory that dissolves at sunset.But the deeper problem isn't amnesia. It's that when agents do remember, they remember in someone else's house - on servers you don't control, in formats you can't inspect, under terms you didn't write.It is a book about building something different: persistent, structured, queryable memory that lives inside your application - no external servers, no data leaving your process, no infrastructure you don't own. An embedded graph database that travels with your agent the way a nervous system travels with a body.You'll learn how to model not just facts, but relationships between facts. Causality. Temporal ordering. The layered structure of meaning that makes memory more than a search index. You'll build ontologies that enforce what can be known and how. You'll combine graph traversal with semantic search - so your agents find not just what's similar, but what's connected.The result is an agent that remembers the way you do: structurally, contextually, privately - with memory that belongs to you.

LLMOps in Actions: Effective Strategies for Large Language Model Deployment and Maintenance
English | January 25, 2026 | ASIN: B0GJQ6TZ1K | 222 pages | Epub | 6.22 MB
LLMOps in Action: Effective Strategies for Large Language Model Deployment and MaintenanceIn today's fast-paced world, deploying and maintaining large language models (LLMs) can be a daunting challenge. From managing infrastructure and scaling operations to ensuring continuous optimization, the hurdles are many. But what if you could streamline these processes, ensuring high-performance models with minimal overhead?LLMOps in Action provides the ultimate playbook for transforming LLM deployment from a complex task into an efficient, scalable, and cost-effective operation. This book focuses on actionable strategies, offering you the tools and knowledge to deploy and manage LLMs seamlessly, no matter the scale.Through clear, real-world examples, you'll learn how to:Build scalable infrastructure for real-time LLM deployment with zero downtime.Implement continuous monitoring and performance optimization strategies to ensure models run efficiently and remain highly available.Automate model updates to ensure that your models stay relevant and accurate with evolving data.Achieve cost-effective operations without sacrificing the performance of your LLMs.Maintain security and compliance, ensuring that your LLM deployment meets all necessary regulatory standards.By the end of this book, you will have acquired the essential skills to navigate the complexities of LLMOps, helping your team scale models faster, optimize performance continuously, and maintain compliance-all while cutting operational costs. Whether you are an AI engineer, data scientist, or an enterprise leader, this book is designed to equip you with the exact strategies you need to succeed in the fast-evolving world of AI.Don't let complex deployments hold you back. LLMOps in Action arms you with practical, actionable insights that you can implement today. Take the next step toward mastering LLM deployment-grab your copy now and lead your team to success.

LLM-Kurs - Ein semantisches Buchempfehlungssystem bauen (Python, OpenAI, LangChain, Gradio)
Deutsch | 29 April 2026 | ASIN: B0GYZNSQTH | 87 Seiten | Epub | 2.15 MB
LLM-KURS - ERSTELLE EIN SEMANTISCHES BUCHEMPFEHLUNGSSYSTEMPython, OpenAI, LangChain, GradioHast du dich jemals gefragt, wie KI-gestützte Buchempfehlungssysteme funktionieren? Möchtest du über einfache Keyword-Suchen hinausgehen und einen **semantischen Buchempfehlungsdienst** entwickeln, der Bücher wirklich versteht?Dieses Buch nimmt dich mit auf eine spannende und praktische Reise in die Welt der **LLMs (Large Language Models), Embeddings und Vektorsuche** - aber mit einem Twist. Du wirst nicht einfach trockene akademische Erklärungen lesen. Stattdessen folgst du **Anya Sharma**, einer frischgebackenen Informatik-Absolventin, die aus einer Mischung von Ehrgeiz und Trotz beschließt, einen KI-Empfehlungsdienst zu entwickeln, um ihren überheblichen Kommilitonen Marcus programmtechnisch in den Schatten zu stellen.Durch **praktisches Programmieren, Humor und reale KI-Anwendungen** wirst du:**Embeddings und Vektorsuche** beherrschen, um ähnliche Bücher basierend auf ihrem Inhalt zu finden - nicht nur nach Keywords.Die **OpenAI-API** nutzen, um intelligente Empfehlungen zu generieren.**FAISS** für Echtzeit-Suche in großem Maßstab implementieren.Eine funktionale **Gradio-Benutzeroberfläche** entwickeln, die die KI ansprechend (oder zumindest benutzbar) aussehen lässt.KI-Halluzinationen handhaben, Anfragen optimieren und dumme Fehler vermeiden, die Geld kosten.Wenn du jemals mit KI-Projekten, Debugging-Albträumen zu kämpfen hattest oder einfach einen **unterhaltsamen und praktischen Leitfaden zur semantischen Suche** möchtest, ist dieses Buch genau das Richtige für dich. Was du lernen wirst (ohne dabei den Verstand zu verlieren):**Wie Embeddings funktionieren:** Hör auf zu raten und fang an zu verstehen, wie KI Text in einem mehrdimensionalen Raum darstellt.**Die Macht der Kosinus-Ähnlichkeit:** Entdecke, warum KI Bücher basierend auf ihrer Bedeutung vergleichen kann.**LLMs in Aktion:** Beschwöre GPT-4 wie ein dunkler Magier und lass es Bücher intelligent analysieren.**KI für Kosten und Leistung optimieren:** Denn niemand will einen Empfehlungsdienst, der einen in den Ruin treibt.Dies ist nicht einfach nur ein weiteres KI-Buch. Es ist eine **praktische, unterhaltsame und leicht chaotische** Reise durch die Entwicklung eines realen **semantischen Buchempfehlungssystems** - eines, das wirklich funktioniert.Wenn du bereit bist, intelligentere KI-Systeme zu entwickeln und dabei etwas zu lachen, **klicke jetzt auf „Jetzt kaufen" und beginne noch heute mit der Programmierung deines KI-gestützten Bibliothekars!**

LLM Primer VI Scaling AI Systems: Architecting Low-Latency LLM Inference for Production Scale
English | June 17, 2026 | ASIN: B0H5PRDMPK | 389 pages | Epub | 6.76 MB
A single user generating real-time text leaves a high-end H100 GPU 99.7% idle. Inference engineering is the discipline of finding work for those idle units.LLM Primer VI is the practitioner's playbook for architecting LLM inference at production scale. It walks the full stack between "the model fits in memory" and "the system serves millions of requests a day under contract latency" - token mechanics, KV cache management, hardware selection, model compression, batching and speculation, serving engines, Kubernetes orchestration, and the unit economics of every layer.What's insideThe autoregressive bottleneck - why decoding is memory-bandwidth-bound and prefill is compute-boundKV cache math: the formula, MHA/GQA/MQA trade-offs, and the invention of PagedAttentionNVIDIA (H100/H200/B200/L40S) vs AMD MI300X vs specialized silicon (Groq, Inferentia, TPU, Gaudi)Quantization with AWQ, GPTQ, GGUF, SmoothQuant - and Blackwell's FP4 hardware accelerationPruning, 2:4 sparsity, and Patient Knowledge DistillationContinuous batching, prefix caching, and KV cache eviction (H2O, InfiniGen)Speculative decoding: EAGLE, Medusa, MTP, Lookahead, n-gram, and the verification bottleneckvLLM, TensorRT-LLM, SGLang, TGI, Ollama - when each fitsRay Serve, KServe, Triton, BentoML - the platform layerDisaggregated prefill/decode pools with LeaderWorkerSet, NVIDIA Grove, KAI SchedulerKEDA + Knative autoscaling, CRIU cold-start mitigation under 5 secondsToken economics: input-vs-output asymmetry, conversation accumulation, invisible reasoning tokensServerless vs dedicated breakeven math, microVM sandboxes for agent codeIntelligent model routing, semantic caching, batch APIs, context compactionIncludes four reference appendices: math formulas and cost modeling, hardware specifications guide, deployment configurations with Python and Kubernetes YAML, and benchmarking methodology with TTFT, ITL, and TPS definitions.Code examples in Python and YAML - runnable, not pseudo-code. Nearly 400 pages of real engineering.Written for platform engineers, SREs, and infrastructure architects responsible for LLM inference at scale. Assumes general LLM familiarity at the level of Volume I and comfort with distributed systems.Volume VI of the LLM Primer series.

LLM Engineer's Bible: [3 in 1] The Ultimate Guide to Building, Fine-Tuning, and Deploying Large Language Models for Real-World Applications and Production-Scale AI systems
English | August 10, 2025 | ASIN: B0FLXV3SLQ | 262 pages | Epub | 1.80 MB
The Ultimate 3-in-1 Guide to Building, Fine-Tuning & Deploying Large Language Models at Scale LLM Engineer's BibleDesign smarter, ship faster, and scale with confidence in the age of AI.Are you struggling to move beyond demos and prototypes while others are building real AI products?Do you feel overwhelmed by the complexity of deploying LLMs at scale, managing prompts, or keeping up with daily changes in tools and APIs?You're not alone-and this book is your solution.Whether you're an aspiring AI engineer, a startup builder, or a seasoned ML practitioner, LLM Engineer's Bible gives you the complete, end-to-end system to build, optimize, and operate large language model applications in the real world-without wasting time on hype or guesswork.Inside this comprehensive 3-in-1 manual, you'll learn:✅ Design Patterns for LLM-Powered Systems - Architect modular, robust, and scalable pipelines using the latest production blueprints.✅ Prompt Engineering for Real-World Use - Master advanced prompting techniques like chaining, role-based design, and task-specific tuning for reliable outputs.✅ Fine-Tuning and Optimization - Apply instruction tuning, LoRA, adapters, and RLHF workflows to control behavior and improve domain performance.✅ MLOps & LLMOps at Scale - Deploy, monitor, and govern LLM systems in production with CI/CD pipelines, drift detection, safety filters, and audit trails.✅ Cost & Performance Efficiency - Learn token budgeting, GPU optimization, model tiering, and caching strategies used by top AI teams.✅ Industry Case Studies - Go behind the scenes of how LLMs are transforming healthcare, finance, e-commerce, automotive, and education-with architecture breakdowns and lessons learned.🎁 Includes 2 Expert Bonuses:✅ Prompt Engineering Bonus - Unlock high-impact prompt techniques, prompt orchestration patterns, and a real-world template registry.✅ LLMOps & Deployment Bonus - Learn cloud vs. on-prem trade-offs, observability patterns, human-in-the-loop systems, and cost modeling strategies.This isn't just a book. It's a blueprint to future-proof your career and launch real AI products.📈 Whether you're building an AI startup, leading enterprise integration, or leveling up as an engineer-this is the guide the market's been waiting for.In 2026, LLMs won't be optional.They'll be embedded into every product, every service, and every workflow.The question is: Will you be the one building them-or watching from the sidelines?👉 Grab your copy of the LLM Engineer's Bible today-and become the builder the AI future needs.

LLM ENGINEERING MADE SIMPLE: Build Practical AI Applications Step by Step - No Coding Background Needed
English | December 19, 2025 | ASIN: B0G8YFNCT9 | 169 pages | Epub | 5.97 MB
This book is a user guide intended solely for educational and informational purposes. It provides general guidance on large language models, prompt engineering, and AI-assisted workflows. It does not constitute professional, legal, financial, medical, technical, or business advice.The field of artificial intelligence is rapidly evolving. Tools, platforms, models, and best practices may change over time. While every effort has been made to ensure accuracy at the time of writing, no guarantees are made regarding completeness or ongoing applicability.Readers are responsible for how they apply the concepts discussed in this book. Any actions taken based on the information provided are done at the reader's own discretion and risk.Artificial intelligence is no longer reserved for engineers, programmers, or research labs. Large Language Models (LLMs) are already shaping how we write, research, automate tasks, and make decisions-yet most people use them without truly understanding how they work or how to use them effectively.LLM ENGINEERING MADE SIMPLE was written to change that.This practical, beginner-friendly user guide demystifies large language models and shows you-step by step-how to work with AI systems confidently, even if you have no coding or technical background.Most AI books fall into one of two extremes:overly technical manuals filled with code and math, orsurface-level content that offers shortcuts without real understanding.This book takes a different approach.Instead of teaching you how to build models, it teaches you how to work with them intelligently. You'll learn how LLMs think, why they respond the way they do, and how to guide them toward reliable, repeatable outcomes using clear language, structured prompts, and practical workflows.Written as a hands-on user guide, this book focuses on real-world application rather than theory. Concepts are explained in plain English, with practical examples that help you move from curiosity to confidence.By reading this book, you will learn how to:Understand large language models without technical jargonWrite clear, effective prompts that produce consistent resultsDesign step-by-step AI workflows without writing codeUse LLMs for content creation, research, automation, and productivityReduce AI errors, hallucinations, and inconsistent outputsBuild simple, repeatable AI-assisted solutions you can actually useWhether you are a student, professional, entrepreneur, educator, or lifelong learner, this book gives you a structured path into AI without overwhelm.You won't just learn what AI can do.You'll learn how to work with it intentionally.If you've ever felt that AI was "too technical," "too confusing," or "not meant for people like you," this book was written with you in mind.LLM ENGINEERING MADE SIMPLE is not about shortcuts or hype.It is about understanding, clarity, and practical skill.Start reading today and turn AI from a mysterious tool into a reliable assistant you can use with confidence.

LINUX, KALI, ARCH, AND RASPBERRY PI: A Field Guide to Security-Oriented Systems (Guidance Compliance And Solutions In The Age Of Artificial Intelligence)
English | April 18, 2026 | ASIN: B0GXNN3QS3 | 91 pages | Epub | 937.02 KB
The manuscript is organized into five parts, covering everything from the history of hardware to the physics of radio:Hardware Anatomy: A technical history of the Raspberry Pi, from the original 2012 Model B to the high-performance Pi 5 and the niche "Zero" and "Compute" modules. It even distinguishes the Pico as a microcontroller that (mercifully) does not run Linux.Imaging & Boot Reality: A deep dive into the "trust hierarchy," covering image formats (ISO vs. IMG), cryptographic verification, and the safe use of low-level tools like dd. Installation Tracks: Detailed guidance on three distinct paths: Kali Linux: Using it as a controlled workstation rather than a "lifestyle choice". Arch Linux: Building a system from scratch to ensure the operator actually understands what they've done. Raspberry Pi OS & DietPi: Lightweight options for dedicated defensive nodes. Post-Install Hardening: A checklist-heavy section on "First Boot Triage," service minimization, SSH hardening, and establishing an update cadence.SDR & Radio Intelligence: Using the Pi as a signal platform for ADS-B aircraft surveillance, AIS marine tracking, and ISM band reconnaissance.

LINEAR ALGEBRA for MACHINE LEARNING : A Visual, Step-by-Step Guide with Python to Master Vectors, Matrices, PCA, SVD, and Neural Networks
English | May 9, 2026 | ASIN: B0H15P5Q89 | 370 pages | Epub | 5.88 MB
Master the math behind modern AI without getting lost in theory.Whether you want to understand neural networks, build machine learning models from scratch, or finally make sense of matrices and vectors, this book gives you a practical, visual, and beginner-friendly path into the linear algebra that powers artificial intelligence.Linear Algebra for Machine Learning transforms difficult mathematical ideas into clear, intuitive concepts with real-world ML applications, visual explanations, and hands-on Python examples.Inside this book, you'll learn how linear algebra drives:Machine learning algorithmsNeural networks and deep learningRecommendation systemsPCA and dimensionality reductionImage compression and embeddingsOptimization and backpropagationsearch engines and vector databasesThis book is designed specifically for:Machine learning beginnersData science studentsSelf-taught AI learnersComputer science studentsPython programmers entering AIAnyone who wants to truly understand ML mathWhat makes this book different:Step-by-step explanations with intuition firstMinimal prerequisites - only basic algebra requiredVisual approach to vectors, matrices, and transformationsPython + NumPy examples throughoutReal ML applications in every sectionPractical projects and worked examplesClear explanations of PCA, SVD, neural networks, and optimizationInside, you'll discover:Vector operations and geometric intuitionMatrix multiplication and transformationsLinear regression from scratchOrthogonality and projectionsEigenvalues and eigenvectorsPrincipal Component Analysis (PCA)Singular Value Decomposition (SVD)Neural network math simplifiedFeature engineering and embeddingsOptimization fundamentalsReal-world machine learning projectsYou'll also build:A recommendation systemAn image compressor using SVDA mini neural networkA PCA visualization projectA document search engineAnd moreBy the end of this book, you won't just use machine learning libraries - you'll understand the mathematics behind them.If you're ready to finally connect linear algebra with real AI systems, this book will give you the foundation you nee

LangGraph Deep Agents Building Long-Running Autonomous Systems From Task Lifecycle to Production Deployment - A Complete

LadybugDB for Edge Agent AI Memory (Graphs for AI Agents Book 2)

LLMs, Generative AI and Ethics Artificial Intelligence and Machine Learning, #3

LLMOps in Actions Effective Strategies for Large Language Model Deployment and Maintenance

LLM-Kurs - Ein semantisches Buchempfehlungssystem bauen (Python, OpenAI, LangChain, Gradio)

LLM Primer VI Scaling AI Systems Architecting Low-Latency LLM Inference for Production Scale

LLM Engineer's Bible [3 in 1] The Ultimate Guide to Building, Fine-Tuning, and Deploying Large Language Models

LLM ENGINEERING MADE SIMPLE Build Practical AI Applications Step by Step - No Coding Background Needed

LINUX, KALI, ARCH, AND RASPBERRY PI A Field Guide to Security-Oriented Systems

LINEAR ALGEBRA for MACHINE LEARNING A Visual, Step-by-Step Guide with Python to Master Vectors, Matrices, PCA, SVD