Every line of code is risk. AI makes it easy to write more code, but is that always a good thing? Learn more: https://circle.ci/4exRPGv
CircleCI
@circleci.com
CI/CD built to accelerate code delivery with limitless scale and speed. Validate code autonomously. Ship confidently.
Runs, jobs, step output, test results. All from the terminal. The new CircleCI CLI brings interactive debugging, predictable JSON, and built-in MCP support for coding agents directly into your workflow. Learn more ⬇️
Rebuilding the CircleCI CLI from scratch - CircleCI
We rebuilt the CircleCI CLI from the ground up in Go: human-first output, –json everywhere, browser OAuth login, a built-in debugging TUI, and an MCP server for your coding agent.
circleci.com
What do teams shipping 9x more validated code have in common? 1️⃣ More finished work per engineer 2️⃣ Validation beyond the push 3️⃣ Fewer cycles per shipped change The data behind the habits: https://circle.ci/49HdjQ7
The latest data is in! The State of Software Delivery Pulse Report looks at what changed over the last quarter, what the highest-performing engineering teams are doing differently, and why the performance gap is continuing to grow. Read on: https://circle.ci/4aHL6c0
When AI generates code and an incident hits, who speaks to what it was supposed to do? When that line blurs, debugging gets really hard, really fast. Watch the full convo: https://circle.ci/4exRPGv
A concerning amount of AI software is making it to production via ~vibes~ More from @seldo.com and Rob: https://circle.ci/4ek2PbC
Your CI pipeline wasn't built for agents running hours-long tasks. We break down what that means for validation infrastructure ⬇️
Agentic validation needs different infrastructure - CircleCI
Validating agent code is expensive and slow. Here’s how Chunk sidecars make it 10x cheaper and 3x more efficient.
circle.ci
Attending AWS Summit Japan? Come say hi! We'll be breaking down the inner and outer loop of software delivery, and how Chunk sidecars are closing the gap between local development and CI with less context switching and faster feedback. See you there. 🇯🇵
Devs tend to solve their own problems first, and according to @seldo.com, they're doing it again with AI: using coding agents as the proving ground before applying those lessons everywhere else. More from Rob and Laurie on why the pattern keeps repeating: https://circle.ci/4ek2PbC
The more autonomous the agent, the more validation matters: tests, hooks, coverage checks, architecture rules, feedback loops. We break down the patterns emerging around agent validation and why they're becoming essential as agents take on longer-running work.
Patterns of validation - CircleCI
Prompting agents to validate their work isn’t enough. Here’s what actually works.
circleci.com
Control vs. velocity is a false choice. Join us at #PlatformCon to see how platform teams are using self-service infrastructure, policy-as-code, and platform standards to accelerate delivery without sacrificing governance: https://circle.ci/4xk4q8L
Uber burned its AI coding budget in four months. Most teams see that and focus on token costs. But a 50-person eng team can lose ~$700K/year to the coordination overhead that happens after the code is written. Read the latest Confident Commit: https://circle.ci/4rjo5SG
22s vs 69s. Same repo. Same agent. Same lint+test gates. We ran a controlled A/B inside chunk-cli comparing sidecar validation to push-per-task CI. The result: 3.1x faster feedback. LLM cost? Basically flat. Tokens tracked fixes, not wait time. Full breakdown ⬇️
The Sidecar Race: 22 Seconds vs 69 Seconds Inside the Agent Loop | CircleCI Loop Lab
A controlled A/B in chunk-cli: same lint+test gates, sidecar remote validate vs push-per-task CI. Median time to signal 3.1x faster on sidecar; LLM cost even slightly better.
circle.ci
AI made building cheap, but it didn't make owning cheap. Our CTO's three questions for build vs. buy in the AI era: -Does it touch what customers pay you to deliver? -What's the cost of being wrong? -What's the long-term cost of ownership?
The AI SaaSpocalypse is a mirage
AI hasn’t changed what’s worth owning
circle.ci
Cloud changed infrastructure from fixed to dynamic capacity. Chris from @augmentcode.com thinks agents are about to do the same thing for engineering teams. Full convo: https://circle.ci/4uuNWc8
27 seconds vs 5 minutes. That’s what inner loop validation looks like with Chunk sidecars. Fast feedback in the inner loop that validates changes while your agent is still working. Cleaner commits in the outer loop. Available for all CircleCI users today! Learn more: https://circle.ci/43h8dWR
Engineering leaders are budgeting AI wrong. Running your team at 2x for 3 weeks, then throttling them back to 1x because they hit a token cap, isn't cost optimization. It's just introducing friction into the system. Full ep with Chris from @augmentcode.com: https://circle.ci/4uuNWc8
Toronto, we'll see you next week at CTO Craft Con! 👋 Find our booth to talk about how to keep pipelines moving at AI speed without constant human effort to hold it together & catch Rob discussing how to lead through market disruption. Join us: https://circle.ci/4u9pCMQ
Turns out Chunk exists outside the terminal too. You can now 3D print your own Chunk and keep it close for moral support when CI has other plans. Download files on Thingiverse: https://circle.ci/4cTPNkt
Introducing Chunk sidecars: fast, pre-configured environments that bring validation into the inner loop in 60 seconds or less. Microbuilds run the checks, providing faster, cheaper feedback in the inner loop & more green pipelines in the outer. Read on: https://circle.ci/4ncdrw4
📣 We’re hiring an AI Community Engineer! 📣 We’re looking for someone relentlessly curious, building agents just to see what happens, breaking things on purpose, and sharing what they learn.
AI Community Engineer - CircleCI
Rapidly release code with confidence on CircleCI’s modern continuous integration and delivery platform. Offered on hosted cloud, Enterprise, and macOS platforms.
circle.ci
Debugging failing CI pipelines is slow because bugs are hard to find. 🔎🐛 Chunk finds the issue, writes the fix, and opens a PR you can actually merge. Full tutorial: https://circle.ci/48ZdOEc
Most codebases are getting worse over time and AI is increasing the pace of change—making it easier than ever to see what’s working and what isn’t. 😬 Compound engineering is the alternative: systems improving with every change, so the work gets easier and the codebase gets stronger as you go.
The Compounding Software Factory 📈
The third and final episode of our software factory series!
refactoring.fm
GitHub, GitLab, or Bitbucket? 🧐 Turns out the answer says a lot about how your team ships. We pulled the pipeline data to find out: https://circle.ci/4rjo5SG
30 years of engineering experience, and Rob still thinks the most important thing a leader can do right now is use the tools. Full convo: https://circle.ci/47Z5Bj2
Your AI coding tools are shipping features faster than your test suite can keep up with. Luckily, Chunk can scan your codebase, find the gaps, write the tests, and open a PR. Full walkthrough: https://youtu.be/nbUHX3SVt9U
Chunk regrets to inform you those retries were not, in fact, different this time.
The fastest way to find your delivery bottlenecks isn't an audit, it's a team running at full AI speed until something breaks. Rob joined @thoughtworks.com to discuss, plus what our 2026 State of Software Delivery data shows about the teams pulling ahead ⬇️
The velocity trap: Why genAI is exposing broken foundations
In 2026, software ROI beats AI hype. Experts discuss why AI code stalls value and how elite teams use "Tiger Teams" to cut technical debt and speed up delivery.
circle.ci
Teams aren’t struggling to generate code, they’re struggling to make it all work together. We’re joining O'Reilly Media on April 16 to dig into how AI is actually shaping software delivery end-to-end:
Software Development Superstream: AI-Assisted Software Delivery
Leverage intelligent automation across the entire software delivery lifecycle
oreilly.com
AI delivery gut check: % to prod in 24h? Review time? Failure rate? That’s the bottleneck. See how you compare: https://circle.ci/3NASptw