OpenAI Cookbook: Fixes to Codex CLI and Deep-Research Examples Restore Reproducibility

This commit addresses issues in the Cookbook's Codex CLI tooling and a set of deep-research examples that had drifted from working configurations. By repairing CLI behavior and correcting example inputs/outputs, the repository becomes a more reliable source of reference implementations for model integration and experiment replication.

For engineering and research teams, the immediate effect is reduced time spent debugging canonical examples. Working examples accelerate onboarding, lower the cost of prototyping with Codex and related models, and improve confidence when adapting exemplar code into production integrations. In research contexts, corrected examples improve reproducibility - a material benefit when models and prompts are under continual evolution.

Actionable steps for leaders: ensure your teams pull the updated Cookbook and re-run baseline demos as part of their development checklist. Integrate the repository into CI smoke tests so that future regressions are detected early, and pin dependency versions used by the examples to avoid environment-driven breakage. Where the Cookbook forms part of an audit or compliance trail, snapshot the exact commit hashes used in experiments or client deliverables.

Strategically, this update highlights the importance of treating vendor-provided examples as living code that requires governance. Allocate maintenance time to vet and adapt upstream examples before using them in production; set a policy for how example fixes are tracked, tested, and propagated into internal templates and libraries. That discipline reduces downstream operational risk and speeds time-to-value when adopting new model capabilities.