Artificial Intelligence

Artificial intelligence is a production tool here before it is a topic of debate, and that is what gives this category its angle: the articles come from systems actually built, not from demos. You will find the full RAG chain, from choosing an embedding model to the evaluation metrics that lie, the shift to CAG once the context window explodes, agent and MCP architecture, persistent memory and Claude Code hooks, prompt caching and what it really does to the bill. You will also find criticism of that same ecosystem when it slips: subscriptions sold as unlimited and then rationed, token consumption industrialized, AI text detectors that should never have existed, a French-language hype bubble that talks a great deal and ships very little. And at regular intervals, the questions the engineering does not settle: what automation takes away from the person writing the code, what a model makes the dead say, where the line runs between a tool and a crutch. Written by someone who deploys these systems in production and publishes the failures alongside the results.

Claude Code Hooks : The Nervous System Nobody Documents

Anthropic’s official documentation devotes half a page to Claude Code hooks. This article fills the gap: a complete taxonomy of the eight hook types, the stdin payload map nobody has published, and five production hooks annotated line by line. You’ll discover the multi-agent “Claude supervises Claude” pattern via the SDK, the infinite recursion trap, and the fleet deployment strategy for enterprises. The final installment of the trilogy after CLAUDE.md and SKILL.md, this guide turns Claude Code from a conversational assistant into a semi-autonomous development agent. If CLAUDE.md is the memory and SKILL.md the procedures, hooks are the nervous system.

What You’ll Learn, and Why Most Explanations of RAG Miss the Point

Artificial Intelligence

If you’re using an LLM with your own documents (or thinking about it) you’ll run into RAG, Retrieval-Augmented Generation. The premise sounds simple: feed your files to the model so it answers better. In reality it’s a pipeline of seven technical decisions, where each link determines whether your AI answers from your data or invents with confidence. This guide covers the full mechanics with the 2026 benchmarks, the concrete tools (NotebookLM, ChromaDB, LangChain), and the traps I hit building my own pipelines. From theory to a working Python pipeline in 30 lines.