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How to Use Claude Code with CircleCI to Fix Failed Builds

Give Claude Code direct access to CircleCI and let it diagnose failed builds, fix issues, and keep iterating until your pipeline is green. In this tutorial, we walk through how to connect Claude Code to CircleCI using the CircleCI CLI. You’ll see how Claude can read pipeline results, identify test failures, make fixes, trigger new builds, and monitor CircleCI without leaving the terminal.

How to set up CircleCI with Cursor Origin

CircleCI now integrates with Cursor Origin, bringing scalable CI/CD to Origin-hosted repositories. In this demo, see how to connect an Origin repository to CircleCI, configure your pipeline triggers, run a build, and report CI status back to your Origin pull request. Already using CircleCI? Your existing.circleci/config.yml works as-is, with no Origin-specific CI syntax or separate config to maintain.

AI can write database code fast. Here's how to keep it safe before production.

AI can write database schema changes in seconds, but nothing should reach production until it's validated, tested, and approved. In this discussion, Ken Muse (GitHub), Steve Jones (Redgate), and Huxley Kendall (Redgate) show how a governed pipeline keeps AI-generated database changes safe without slowing teams down.

Cribl On Your Coffee Break Episode 2 - Setting up Syslog

In our second video Leon picks on Syslog (because honestly, it deserves it). Cribl is the perfect tool to whip that disorganized, loud, unruly mess of a data stream into shape. By the time the month is over, you will have a pretty good idea of what Cribl can do, and how to do it. You’ll also have consumed more caffeinated beverages than is strictly appropriate...

Resilience Testing Agents: Find Resilience Risk Before It Reaches Production

Most engineering orgs know that resilience testing matters, but proving ROI before you invest time and money is hard. Harness RT Agents solve that by scanning your CD pipelines for resilience risk first, no instrumentation needed, so you get a real report before you commit to chaos experiments, load tests, or DR testing. In this video: Resilience Testing is free to start, with a full fault library, a hosted control plane, and RBAC included.

The invisible challenge that kills IT projects

The verdict on enterprise AI is starting to sound familiar: It didn't deliver. But the real question is: Did we ever define what deliver meant in the first place? If success was never tied to measurable business outcomes, AI was always going to fall short, no matter how capable the technology was. Before the next budget cycle dismisses AI, ask whether the problem is the technology or the lack of clear success metrics.

The gap between individual AI productivity and team performance

As a product manager at Upsun with a computer engineering background, Kateryna Dvornichenko had spent months researching competing tools in the agentic development space, running tests, comparing features, and building a picture of where the market was heading. She realized the tools were impressive, but something kept standing out. "Collaboration was not the strong point of any of them," she says. "Everyone stays on their own machine with their own setup.".

Repo rightsizing: audit every model call in a repo you already shipped

Repo rightsizing is a single-pass audit of every real model call in a codebase you already shipped: SDK invocations, sub-agent dispatch sites, and agent frontmatter pins. Each call site is scored on the job it actually does, and the result commits as one blueprint file you can diff next quarter. It replaces one-skill-at-a-time reviews, which miss files where a single model key covers two different jobs.

AI usage tracking: Monitor spend by team, feature & model

AI usage tracking means measuring who and what consumes AI across your company, by team, feature, and model, then converting the usage into spend and cost per unit of work. Provider consoles stop at totals per API key. Tracking puts names on those totals: which team, which product, which model, and whether any of it was worth the money. In May 2026, CNBC reported that “almost every Fortune 500 is tracking overall AI usage,” quoting ModelOp CTO Jim Olsen. The same reporting carried his warning.