Codex-maxxing for long-running work

OpenAI has published a blog post detailing how developer Jason Liu uses Codex to handle long-running, complex coding projects. Liu demonstrates techniques for preserving conversational context across multiple prompts, allowing Codex to maintain a coherent understanding of tasks as they evolve. Rather than relying on a single query, he chains interactions to manage project scope, track changes, and iterate over time. The post covers practical strategies such as condensing previous exchanges into a summary prompt, breaking work into sub-tasks, and using explicit instructions to carry over decisions. For developers building AI-assisted workflows, this shows how to move beyond one-shot code generation toward sustained, collaborative coding sessions. The approach is especially relevant for codebases where context length limits can disrupt continuity. Liu’s methods offer a blueprint for integrating large language models into daily development without losing progress between prompts.