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This article covers several recent updates to OpenAI's Codex and Responses API. Key highlights include the introduction of skills for Codex, new multi-provider support, and enhancements like Gmail and Google Calendar integration. The updates aim to improve usability and efficiency for developers.
This article explains the Codex agent loop, which is the mechanism that allows the Codex CLI to interact with users and perform software tasks. It details how user input is processed, how queries are sent to the model, and how responses are generated, including tool calls for executing commands.
This article provides guidance on optimizing the Codex model for coding tasks using the API. It covers recommended practices for prompting, tool usage, and code implementation to enhance performance and ensure efficient task completion.
OpenAI announced several updates, including Open Responses, an open-source spec for building multi-provider LLM interfaces. The introduction of GPT-5.2-Codex enhances complex coding tasks, while new skills and connectors improve usability and integration with other platforms.
This article walks you through creating an MCP server using Semaphore's API. It covers setting up the project, coding the server, and integrating it with tools like OpenAI's Codex for conversational CI/CD interactions.
This article explains the architecture and functionality of the Codex App Server, which powers various Codex applications. It details the agent loop, conversation primitives, and how developers can integrate Codex into their products effectively.
This article outlines recent enhancements to the GPT-5.2-Codex model, focusing on its ability to handle complex tasks, support for reusable skills, and new API capabilities. It details improvements in usage limits and integration with various platforms like Gmail and Google Calendar.