This article is a comprehensive beginner's guide to Large Language Models (LLMs), explaining their core principles (Transformer architecture, self-attention), prompt engineering basics, and how to call LLM APIs (OpenAI, DeepSeek) with Python examples. It highlights the statistical nature of LLMs, their limitations, and practical tips for effective interaction.
原文翻译:
本文是一篇面向初学者的全面大语言模型(LLM)入门指南,解释了其核心原理(Transformer架构、自注意力机制)、提示词工程基础,以及如何通过Python调用LLM API(OpenAI、DeepSeek)。文章强调了LLM的统计本质、局限性以及有效交互的实用技巧。
This article provides a step-by-step guide on how to access and use the GPT-4 Turbo API from OpenAI. It covers account creation, API key generation, making requests with curl, enabling JSON mode, and pricing details. Ideal for developers wanting to integrate GPT-4 Turbo into their applications.
原文翻译:本文提供了如何访问和使用OpenAI的GPT-4 Turbo API的分步指南,涵盖了账号创建、API密钥生成、使用curl发起请求、启用JSON模式以及定价详情。适合希望将GPT-4 Turbo集成到应用中的开发者。
This article is a recap of an Anthropic session at Google Cloud Next '26, covering topics such as Anthropic's 54% coding market share in 2025, the evolution from chat to code to cowork, the definition of AI agents, the Solutions Machine concept, the Great Inversion of value, and three compounding opportunities. The session emphasizes that enterprise software will change more in the next two years than in the past twenty, with AI agents shifting from top-down planning to bottom-up execution.
原文翻译:
本文是Google Cloud Next '26上Anthropic session的回顾,涵盖了Anthropic在2025年占据54%编码市场份额、从聊天到编码再到协作的进化、AI代理的定义、解决方案机器概念、价值的大反转以及三个复合机遇。session强调企业软件在未来两年的变化将超过过去二十年,AI代理从自上而下的规划转向自下而上的执行。