In this article, you will learn what loop engineering is, where it came from, and how to design autonomous AI agent cycles that run reliably without constant human supervision.
Making developers awesome at machine learning
Making developers awesome at machine learning
In this article, you will learn what loop engineering is, where it came from, and how to design autonomous AI agent cycles that run reliably without constant human supervision.
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around.
In this article, you will learn how LangChain, LlamaIndex, and raw API calls each solve a different layer of the LLM application stack, and how to choose among them based on what your project actually requires.
In this article, you will learn why agent accuracy degrades as a tool catalog grows, and six practical techniques for keeping tool selection accurate and efficient at scale.
In this article, you will learn how the seven layers of a production AI agent stack fit together, from the foundation model down to deployment infrastructure.
In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing code in advance, or a model reasoning at runtime.
In this article, you will learn how to build AI agents that can browse and interact with real websites using Playwright, browser-use, and LangGraph.
In this article, you will learn how to build multimodal AI capabilities — image classification, image captioning, and speech transcription — that run entirely in the browser using Transformers.js, with no server, no API key, and no data leaving the user’s device.
In this article, you will learn what AgentOps is, how it differs from traditional LLM monitoring, and how to build a production-ready observability stack for autonomous AI agents.
In this article, you will learn how sentence embeddings work and how to build a fully client-side semantic search engine using Transformers.js, with no server, no API key, and no backend infrastructure required.