Beau Carnes writes about a new hands-on beginner's course on the freeCodeCamp.org YouTube channel designed to help developers master OpenAI Codex. The tutorial covers essential topics including installation, pricing tiers, and interface navigation, while also exploring advanced workflows like Plan Mode and Go Mode for autonomous software development.
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
Mahnoor Faisal writes that OpenAI Codex tends to over-engineer simple tasks by refactoring surrounding code, adding abstractions and defensive guards not requested, and she fixes this by appending a single boundary line to every prompt telling it to make the smallest change that fully solves the task and not add extras unless strictly required.
- The same one-line tweak previously improved prompts for Claude, Claude Code, NotebookLM and ChatGPT
- Over-scoping complaints are common on Reddit, especially with GPT-5.6 Sol
- OpenAI'''s focus on long-running autonomous work makes the model eager to find adjacent improvements
>"Make the smallest change that fully solves the task. Do not add abstractions, fallbacks, defensive guards, refactors, or features unless they are strictly required.”
- The same one-line tweak previously improved prompts for Claude, Claude Code, NotebookLM and ChatGPT
- Over-scoping complaints are common on Reddit, especially with GPT-5.6 Sol
- OpenAI'''s focus on long-running autonomous work makes the model eager to find adjacent improvements
>"Make the smallest change that fully solves the task. Do not add abstractions, fallbacks, defensive guards, refactors, or features unless they are strictly required.”
Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
Lightweight coding agent that runs in your terminal, allowing chat-driven development with the power to execute code and manipulate files.
