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Code review has always been a time sink. AI just makes the dysfunction undeniable. Luca Rossi, founder of Refactoring.fm and builder of the open source tool Tolaria, has been running one of engineering's most-read newsletters for five years, with over 170,000 subscribers. He's also been doing what a lot of engineering leaders talk about but rarely do: building a real product with AI agents to pressure-test what's actually possible today.

AI makes it easy to write more code. The question is whether that's actually a good thing.

Without constraints, feedback, and clear standards, AI can create complexity faster than teams can manage it.

The question isn't whether your proposal is good, it's whether it's more important than everything else competing for the same time, people, and budget. That's why the strongest proposals explain the impact, not just the work.

Give Codex access to CI feedback and it can diagnose failures, make a fix, and keep iterating until the pipeline is green. Here's the CircleCI plugin in action.

A fresh Chunk Sidecar took 45.6 seconds to start. With a snapshot, it only took 12.

AI wrote the code, but when something breaks, that's still on you. Code ownership matters more than ever.

Learn how to connect OpenAI Codex to CircleCI so your coding agent can check pipelines, diagnose failed builds, fix bugs, and iterate until your CI pipeline is green. In this step-by-step demo, we walk through how to install and authenticate the CircleCI CLI, install the CircleCI plugin for Codex, validate your CircleCI configuration, and give Codex direct access to CI feedback. You’ll see how Codex can.

Waiting minutes for CI feedback while you're building with AI agents kills your momentum. Chunk sidecars snapshots let you freeze your environment after setup, so every boot skips the install and starts from a cached state instead of scratch. In this tutorial, we demo how to set up Chunk sidecars, snapshot it, and boot from that snapshot. We also benchmark the difference: a cold sidecar start took 45 seconds. Booting from the snapshot took 12 seconds. That's 30+ seconds saved on every run, multiplied across every developer, branch, agent, and session.

Claude Code + CircleCI MCP = no more hunting through build logs.

When AI writes the code and something breaks, nobody knows what it was supposed to do.

AI agents write code fast, but without a validation layer, fast just means faster bugs. In this video, we connect Claude Code to the CircleCI MCP server so Claude can trigger pipelines, pull build failures into context, and iterate until everything is green. No context switching. No copy-pasting logs.

Technical proposals can be like campfire stories. You start at the beginning, walk through every discovery & technical detail, and eventually arrive at the point. The problem? By then, you've lost the audience.

Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.