Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
Over the last year, MCP accomplished a rapid rise to popularity that few other standards or technologies have achieved so quickly. This article details the unlikely rise of the Model Context Protocol (MCP) and its journey to becoming a generally accepted standard for AI connectivity.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
This article details new prompting techniques for ChatGPT-4.1, emphasizing structured prompts, precise delimiting, agent creation, long context handling, and chain-of-thought prompting to achieve better results.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
The article discusses four open-source AI research agents that serve as cost-effective alternatives to OpenAI’s Deep Research AI Agent. These alternatives offer robust search capabilities, AI-powered extraction, and reasoning features, allowing researchers to automate and optimize their workflows without incurring high costs.
A script utilizing OpenAI's Llama models to interact within a terminal environment, allowing the models to execute Python code and communicate based on predefined prompts.
