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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Share packages across your workspace with internal packages

We launched Bitbucket Packages to give teams one place to manage code, pipelines, and artifacts, and we’ve been expanding it ever since, first with Maven and npm, then PyPI and NuGet. Every one of those packages has shared one trait: it lives inside a single repository and inherits that repository’s permissions. Most of the time, that’s exactly what you want. But it also means sharing a package across repositories has meant granting people access to source code they never needed to see.

Cortex Platform Walkthrough

In this video, Becka gives a guided tour of Cortex, the Engineering Operations Platform that runs mission control for your AI software factory. Learn how to centralize visibility, clarify ownership, and automate standards across your entire software ecosystem. What we cover: Why Cortex: Cortex is where engineering leaders run mission control for the AI software factory: the visibility, intelligence, and controls to keep teams shipping fast without letting accelerated output turn into accumulated risk to reliability, security, and cost.

Scanners aren't enough. Here's the layer most teams are missing.

Vulnerability scanners tell you what's wrong with what you already pulled. They don't stop a malicious package from entering your environment in the first place. A private registry with minimum release age policies adds that missing layer – packages newer than two or three days don't reach developers until the industry has had time to identify and remove malicious ones from public registries. Active campaigns like TeamPCP are targeting npm, PyPI, and Maven Central right now. The gate has to come before the scan. Cloudsmith provides that gate.

Don't add a read replica until you've read this

As the size and complexity of their relational database workload grows, every company eventually goes through the process of off-loading work on a read replica. It comes with lots of benefits, but at a cost of increased complexity. This article is about how we dealt with that, a lot of learnings, and some useful techniques. incident.io is an incident management product relied on by thousands of customers to be the thing that supports them through anything from a minor blip to a full outage.

Preparing for CIP-015: Building Operational Resilience Through Visibility, Detection, and Segmentation

Utility organizations preparing for CIP-015 should think beyond compliance. The organizations that will be best positioned are those investing in comprehensive network visibility, continuous operational intelligence, and segmentation to strengthen cyber resilience and operational continuity. This article explains what CIP-015 means, why it matters, and how Teneo helps utilities build a practical roadmap toward operational resilience.

Introducing AI Agent Deployment in Harness Continuous Delivery | Harness Blog

‍Teams building agents have converged on something that looks a lot like the software development lifecycle, but reshaped around a system whose output isn't deterministic: prototype an agent against a framework, evaluate it against a dataset of expected behavior, deploy it somewhere real, observe how it behaves against live traffic, and feed what you learn back into the next prototype. Call it the agent development lifecycle (Agent DLC).

Instant Kubernetes Observability with Proxymock #speedscale #kubernetes #ebpf #devops #cloudnative

Learn how to get instant observability into your Kubernetes cluster by installing the Speedscale operator and proxymock tool. In this step-by-step tutorial, we walk you through setting up the operator to capture live network traffic (including encrypted traffic, API calls, and database calls) without complex instrumentation or manual configuration.

Introducing Harness AgentTrace: An Observability and Guardrail Framework for AI Agents | Harness Blog

AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.