Operations | Monitoring | ITSM | DevOps | Cloud

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.

Run a few prompts. Looks good. Ship it. According to Laurie Voss, that's still how a surprising amount of AI software gets tested.

People want to support your community. Sometimes you just have to invite them in.

The real unlock is flagging what actually needs human review, not shipping 5,000-line PRs and hoping for the best.

Devs have a habit of solving their own problems before anyone else's and the AI boom is repeating that pattern.