Claude loop engineering is the practice of building bounded, observable, tool-using agentic workflows on top of Anthropic’s Claude. This post covers loop anatomy, stop conditions, tool design, and the production patterns that keep agents from drifting, looping forever, or burning budget.
Breaking down Anthropic’s Claude Certified Architect track — the domains it tests, the production patterns it expects you to know, and a practical study plan for engineers who already ship LLM systems.
A hands-on, engineer-focused walkthrough of the first seven days with Claude — from setup and prompt patterns to tool use, RAG, and the production habits that separate demos from real systems.
Introduction Anthropic’s Claude has become a cornerstone for enterprises that need safe, reliable, and controllable large‑language‑model (LLM) capabilities. While the model itself garners most of the headlines, the real differentiator for production‑grade deployments is the Claude Control Plane (CCR) – a dedicated orchestration layer that separates control from compute. CCR (sometimes referred to as Claude Control Runtime) is not a single monolithic service; it is a collection of APIs, policies, and observability tools that enable: ...
Introduction Algorithmic trading has moved from a niche hobby of a few quant firms to a mainstream tool for retail and institutional investors alike. The secret sauce behind successful strategies is real‑time market data: price ticks, order‑book depth, news headlines, and even social‑media sentiment that arrive in milliseconds and must be processed instantly. In the past, building a low‑latency data pipeline required deep knowledge of networking protocols (FIX, UDP multicast), specialized hardware, and expensive data‑vendor licenses. Today, the combination of Python—the lingua franca of data science—and Claude, Anthropic’s large language model (LLM), offers a surprisingly powerful, cost‑effective way to ingest, enrich, and act upon live market streams. ...