Event-driven architecture (EDA) is a way of building systems in which services communicate asynchronously by reacting to facts that have already happened, instead of asking each other for something and waiting for a reply. It is the foundation behind platforms like Uber, Shopify and Netflix — and increasingly behind autonomous AI agents and robot fleets too.
Weights are the billions of numbers in which an AI model stores everything it has learned. They determine what a model can do, how much memory it needs, and whether you can run it on a laptop or only in the cloud.
Every large language model goes through several distinct training phases: first it absorbs trillions of tokens from the internet, then it learns to behave like a useful assistant. Understanding that split is the key to deciding when fine-tuning is worth the effort, when RAG is the right answer, and when neither makes sense.
The context window is a language model's working memory — it sets how much text the AI can take into account at once. Understanding how it works and where its limits lie explains both the cost and the surprising mistakes of modern models.
The Model Context Protocol is an open standard that unifies how AI models connect to external tools and data. It matters because, in under eighteen months, it became the de facto common integration language across the industry — from Anthropic to OpenAI and Google DeepMind.
Language models increasingly handle long, multi-step tasks, yet their attention mechanism scales poorly with context length. LLM Sleep proposes that — much like biological sleep — a model should periodically "sleep on" recent context and consolidate it into persistent weights before clearing its cache.
World models are AI systems that learn an internal simulation of reality and predict what happens next — instead of merely reacting to the current image. They are one of the directions in which AI is shifting from pattern matching toward planning and reasoning about cause and effect.
Machine learning (ML) is the branch of artificial intelligence in which computer systems build their own decision rules from data instead of receiving them as hand-written code. It is today the core of nearly every practical AI technology — from spam filters to large language models and autonomous robots.
Vector databases are specialized systems storing data as high-dimensional numerical representations that enable search by meaning rather than keywords. They form the critical foundation of RAG architecture, allowing language models to access current, verifiable external knowledge.
ReAct is an architectural pattern for large language models in which the model alternates between generating thoughts (reasoning), calling external tools, and reading observations back into its context. Understanding ReAct is a prerequisite for building any modern AI agent, because the Thought–Action–Observation loop sits underneath most agentic systems shipped today.
Prompt Caching is an optimisation mechanism in large language model APIs that allows repeated reuse of processed prompt fragments without recomputing them from scratch. For queries containing long, repetitive contexts — such as extensive system instructions, documents, or few-shot examples — it can make API calls up to ten times cheaper and several times faster.
Released on May 28, 2026, Claude Opus 4.8 is the current leader in comprehensive AI intelligence rankings. With its Adaptive Thinking mechanism, one-million-token context window, and revolutionary agentic capabilities, the model redefines standards for autonomous coding, data analysis, and enterprise business tasks.
