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On March 24, 2026, the AI open-source ecosystem was impacted by a critical supply chain attack involving the widely used Python package LiteLLM. Attackers compromised the LiteLLM PyPI distribution pipeline and published malicious versions (notably in the 1.82.7-1.82.8 range), embedding a multi-stage payload designed to steal credentials and execute remote code.

You're tagging Docker images with build numbers. -Build is your latest production release on main. A developer pushes a hotfix to release-v2.1, that run becomes build. -Another merges to develop, build. A week later someone asks: "What build number are we on for production?" You check the registry. -You see,,, on main. The numbers in between? Scattered across feature branches that may never ship. Your build numbers have stopped telling a useful story.

--- Key Takeaway: Harness AI helped deflect 95% of the platform support tickets for a major financial institution --- These days, success is often measured by what doesn’t happen: When things go right, the software delivery platform is invisible. But what happens when an organization’s delivery velocity increases multifold? Can the platform still stay out of the way?

Modern engineering teams run on CI/CD. It’s where pull requests get validated, artifacts get produced, and releases get promoted to production. That also makes CI/CD migration very risky because you're not just moving a "tool"; you're moving the workflow that developers use dozens or hundreds of times a day. The good news: disruption is optional.

Modern software teams are under constant pressure to ship faster without breaking production. That’s why CI/CD best practices have become essential for high-performing DevOps organizations. Continuous integration and continuous delivery (CI/CD) help automate builds, testing, and deployments — but simply installing a pipeline tool isn’t enough. Without the right practices, pipelines become slow, flaky, and difficult to govern.

‍Flaky tests are automated tests that pass or fail inconsistently without changes to the code. In this guide, you’ll learn why flaky tests happen, how to detect them automatically in CI pipelines, and how modern platforms prevent them from slowing teams down. Your test went well three times yesterday. It didn't work this morning. You ran it again without changing anything, and now it works. Congratulations, you've just passed a flaky test, and now someone's day is going to be ruined.

A DevOps pipeline is a critical part of modern software delivery. It is a series of automated steps that move code from commit to production quickly, reliably, and consistently. At its core, a DevOps pipeline is a system that helps teams build, test, and release apps in an easier way. It cuts down on manual work and mistakes. This helps teams send out updates more often, make better software, and react quickly when the business needs change.

At SREday NYC 2026, the ShipTalk podcast welcomed Birol Yildiz, Co-founder and CEO of ilert, for a conversation about the next evolution of incident response. In the episode, ShipTalk host Dewan Ahmed, Principal Developer Advocate at Harness, spoke with Birol about how artificial intelligence is transforming reliability engineering—from simply assisting engineers during incidents to autonomously diagnosing and resolving outages.

Most engineering teams know the difference between “we have tests” and “we know we’re well-tested.” Your CI builds may be green, but without code coverage, it’s hard to prove how much of your code is actually exercised by automated tests. Code coverage measures what percentage of your code runs during tests (lines, branches, and functions), and when you wire it into CI gates, it becomes an enforceable quality signal and not a vanity metric.

Internal platform adoption usually doesn’t fail because developers “hate standards.” It fails because the platform doesn’t make their day easier. If your portal still means waiting, waiting on an environment, waiting on an approval, waiting on the platform team, it becomes one more tab that people stop opening. But if the platform lets engineers get the common stuff done quickly (with guardrails that keep things consistent), they’ll come back on their own.