AI debugging your CI pipeline
From failing pipeline → fixed PR Here's how Chunk analyzes logs, finds real bugs, and ships working fixes automatically.
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Shipping trustworthy code with Chunk CLI
AI coding agents are fast. They generate functions, refactor modules, and wire up boilerplate faster than any human. What they don’t do by default is enforce the conventions a specific team has agreed on: the lint rules, the review patterns that senior engineers flag on every PR. A generated diff looks clean until someone runs CI or reads it carefully.
Terminal dependencies for CircleCI workflows: Always run what matters
When a job fails, gets canceled, or never runs, the work that still needs to happen afterward (cleanup, notifications, teardown) has no clean way to trigger. There is no easy way to express “run this no matter what” in your pipeline config without duplicating jobs or adding fragile workaround branches. Terminal jobs change that.
How Engineers Get Leadership Buy-In for Technical Initiatives
Getting leadership to greenlight your technical work isn't about having the right answer, it's about speaking the right language. CircleCI CTO Rob Zuber shares the frameworks he's developed over 12 years for translating engineering priorities into business impact, navigating organizational dynamics, and building the relationships that make buy-in happen before you ever enter the room.
Your test coverage can't keep up with AI. Chunk fixes that.
The faster you ship with AI, the wider your test coverage gaps get. Chunk scans your codebase, finds what's untested, writes the tests, and opens a PR.
How to set up rolling deployments with CircleCI
A rolling deployment updates running application instances in batches, replacing old instances with new ones while the application keeps serving traffic. The concept applies to any system that can run multiple instances of an application, but Kubernetes has it built in as the default deployment strategy. Kubernetes terminates an old pod only after its replacement passes the configured readiness check, so no requests land on an unready instance.
Auto-Generate Tests for Your Codebase with AI (CircleCI Chunk Tutorial)
AI coding tools help you ship features faster than ever, but test coverage often can't keep up. In this video, we show you how CircleCI's Chunk autonomous CI/CD agent finds untested code in your codebase, writes tests to cover it, and opens a pull request for your review. What you'll learn: Chunk works directly inside your CI/CD pipeline, giving it access to your build history, test results, and coverage reports. That means smarter tests, not just more tests.
CircleCI + Codex: Close the loop between AI code and your CI pipeline
AI agents write the code. CircleCI makes sure it actually ships. The CircleCI plugin for Codex is live.
CircleCI is now available as a Codex plugin
CircleCI is part of the latest wave of Codex plugin integrations, joining the directory alongside other popular development tools like Vercel, Cloudflare, Figma, Notion, Sentry, Hugging Face, Linear, and more. If you’re using Codex, you already know that writing code is rarely the hardest part of your job. It’s the delays, interruptions, and context switching that start when that code breaks on its way to production. The CircleCI Codex plugin closes that gap.
Building an agentic content production system with Claude Code
This post by an engineer explains how his team uses the.claude folder in Claude Code. The folder is the hidden directory where you store context files, behavioral rules, and automated workflows so Claude understands how to operate in a specific project. He’d set up coding conventions, tool configs, CI integrations. Very engineering-brained. The tool is called Claude Code, so fair enough. I run a web and content team. We write blog posts, tutorials, and technical guides for a living.
7 AI productivity lessons from the CTO of Superhuman
Most companies have built AI into their product by now, and many consider it the central feature of what they’re building. But plenty of those same companies are still figuring out how to get their own engineering teams to actually use AI tools day to day. When Loïc Houssier joined Superhuman as CTO in early 2025, his team was in that exact spot. The company had been shipping AI email features for years, but internal adoption of AI dev tools was still early.
RalphCI: The Self-Healing AI Coding Loop That Automatically Fixes CI Failures
RalphCI is an open-source, CI-enabled agentic coding loop built by the Loop Lab at CircleCI. You write a spec, and the agent breaks it down into tasks, builds your application step by step, commits to GitHub, and runs your full CI pipeline on every iteration. If anything fails—linting, tests, security scans, missing files—a CI Doctor sub-agent detects the failure, reads the stack trace, and fixes it automatically. In this video, Ryan Hamilton demos RalphCI by building a classic Snake game end-to-end with zero manual coding.
Why your team isn't getting faster with AI
Hitting your PR count feels productive, but maybe it's not. Sometimes you have to slow down to 10x.
Why you can't see your own bugs
You've been staring at the problem too long. Fresh eyes break what you can't.
5 devs + 5 AI tools = 1 messy codebase
Chunk analyzes your entire codebase, refactors what matters, and validates every change before opening a PR.
AI is shipping faster. Are you?
Shipping feels slow. Reviews take forever. Things break more than they should. That’s the bottleneck.