Artificial Intelligence

Add Gmail search, reading and drafts to an existing OpenAI Agents SDK agent on Windows, with explicitly authorized sending.

Configure Artemis shared memory, documents and local work continuity for an OpenAI Agents SDK agent on Windows.

Install the Artemis HDSM/HDSD gateway on Windows and connect ChatGPT to external memory records and documents.

Persistent memory, hooks, permissions and token cost: what worked when using Claude Code every day in a real small-business operation.

In Place of a PrefaceSometimes a thought arrives before the language to express it. It is already there: you can sense it, circle around it a few times, argue about it, put it to use - and still not quite understand what is happening. Until a thought has a name, it remains almost invisible. Not because it does not exist, but because we keep looking at it through old words.This work began at precisely that point. Not where a complete theory, a rigorous methodology and a settled system of concepts are already in place. Earlier: at the moment when familiar language began to fail, and it became clear that something important was happening, but the old words were no longer enough to name it.So what you have before you is not a conclusion. Not a treatise. Not even a theory in the strict sense of the word.It is a prelude to a hypothesis.The first recorded trace of a question still searching for its own form.That qualification matters. The first account of a new idea cannot be expected to contain everything that must come later: a fully operational definition, an empirical foundation, a classification of cases, a legal framework, an educational protocol and a definitive methodology. All of that will be needed. Perhaps that is exactly what the next step should be. But the first step works differently: it notices rather than proves; it opens rather than closes; it offers no finished map, but draws a first line where there may once have seemed to be nothing.Of course, we are not starting from scratch, nor do we claim that the question itself is wholly new. Long before today, others...

Вместо предисловияИногда мысль появляется раньше своего языка. Она уже есть: ее можно почувствовать, несколько раз обойти вокруг, спорить о ней, пользоваться ею — и все же не до конца понимать, что именно происходит. Пока у мысли нет имени, она остается почти невидимой не потому, что ее нет, а потому, что мы продолжаем смотреть на нее через старые слова.Эта работа возникла именно в такой точке. Не там, где уже есть законченная теория, строгая методология и окончательная система понятий. А раньше — в моменте, когда привычный язык начал давать сбой и стало ясно: происходит нечто важное, но назвать это прежними словами уже недостаточно.Поэтому перед вами не итог. Не трактат. И даже не теория в строгом смысле слова.Перед вами — пролог к гипотезе.Первый зафиксированный след вопроса, который еще только ищет собственную форму.Эта оговорка важна. От первого текста о новой мысли не- льзя требовать того, что должно появиться на следующих этапах: полной операционализации, эмпирической базы, классификации кейсов, юридической модели, образовательного протокола и окончательной методологии. Все это понадобится. Возможно, именно это и должно стать следующим шагом. Но первый шаг устроен иначе: он не доказывает — он замечает; не закрывает — открывает; не дает окончательную карту — проводит первую линию там, где раньше, возможно, казалось пустое место.

This is a hands-on, code-first book. Every example is real, tested, and runs against a local model with no cloud key required, except where a capability genuinely needs one. You get working Maven projects, not snippets, and a clear-eyed account of each stack's strengths, gaps, and surprises. The full source code is on GitHub under an open-source license What you'll learn:Build a working AI service in three stacks and grade them with one shared testGround answers in your own documents with RAG, and tune what retrieval returnsGive a model memory and tools, and reach external systems over the Model Context ProtocolAdd vision and audio, structured output, production observability, and securityGo from assistants to agents, and see where the JVM's agent story is headingWho it's for: Java developers and architects choosing an AI stack, or already committed to one and curious what the alternatives do differently. Comfort with modern Java and either Spring Boot or Quarkus is all you need. No machine-learning background required.

Build an Obsidian second brain your AI can actually use, without making it read everything you have ever written.

Ready to move beyond AI prototypes? Learn how to build agents that reason, plan, use tools and tackle real-world tasks. From core concepts to production-ready architectures, this practical guide covers the code, design decisions and engineering challenges you need to build reliable, secure and scalable agentic systems.

The senior iOS interview now has an AI round. 120 questions with detailed, senior-level answers on Foundation Models, Private Cloud Compute, Core AI, cloud LLMs, RAG and AI system design, current for iOS 27 and WWDC26.

Go from “AI as a copilot” to a team of AI testing agents that analyze, design, run, triage and review tests, with a human test lead making the decisions. Hands-on, in TypeScript, and vendor-agnostic: Claude, GPT, Gemini or a local model, with tools shared through MCP.

Blockchain does not remove trust. It moves trust into code everyone can verify.

vLLM and SGLang, side by side. Learn how LLM serving works, measure both engines fairly, tune them with evidence, and take a model from an empty GPU to a deployment with a cost per million tokens you can defend.