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AI coding tools turned every engineer into a 10x developer. Now your CI/CD pipeline is the bottleneck. Learn how to handle 10x more deploys per engineer with Qovery's dual deployment model. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Codex is OpenAI’s coding agent, powered by the GPT-5 family of models. It reads your files, proposes edits, and runs commands directly in your local environment. It ships as both a desktop app and an open source CLI, and it extends through plugins that connect it to external tools and services. Like any AI coding tool, Codex is strongest when the code it generates gets validated automatically.

Since Anthropic announced Project Glasswing and the capabilities of Claude Mythos Preview, and OpenAI announced GPT-Cyber – my calendar has looked the same every day: Back-to-back calls with CISOs, AppSec leads, and security architects. And every call starts with the same question.

Local development and remote validation were always meant to work together: developers iterate on their machine, run a few manual checks, then push to CI to clear code for production. But AI development broke that balance, flooding CI with a volume of commits no developer has read, let alone tested. Chunk sidecars restore the balance: lightweight, preconfigured environments that run alongside your local workflow and validate changes as they happen.

At Atlassian, we use Merge Queues to ship frequent changes with confidence and streamline pull request merges. Across some of our busiest codebases, Merge Queues have sharply reduced incident frequency and turned merging from a stressful bottleneck into a background task. Today, most of our largest repositories rely on Merge Queues—over 70 large repos across products like Jira, Rovo, Trello, and others—having safely landed 30,000 pull requests since adopting Merge Queues Beta last quarter.