Operations | Monitoring | ITSM | DevOps | Cloud

If your team bIf your team builds with Python or.NET, your packages have likely lived separately from your code, stored in a registry with distinct login, permissions, and billing. Starting today, they don’t have to. Bitbucket Packages now supports the Python Package Index (PyPI) and NuGet, integrating Python and.NET package management into the platform where your team writes code, reviews pull requests, and runs pipelines.

Europe is in the middle of one of its most severe heat events on record, and the effects go well beyond public health. As temperatures across France, Spain, the UK, and Germany push into record territory, the strain on power grids, cooling systems, and physical infrastructure is becoming impossible to ignore. For data center operators, maintaining uptime in this environment demands the real-time visibility that modern Data Center Infrastructure Management (DCIM) software can provide.

Interface counters tell you a port is busy. Bytes in, bytes out, errors, drops. That’s enough to know a link is saturated, but not enough to know which conversations are saturating it, which devices are involved, or how a problem propagates across your network. For that you’ve traditionally needed dedicated network performance monitoring tools, usually expensive, usually a separate console from the rest of your monitoring.

Agentic coding tools like Claude Code can write, refactor, and debug across an entire codebase, but by default they read code as plain text, the way grep does. The Language Server Protocol (LSP) changes that: it’s the same code-intelligence layer an IDE uses, and wiring it into an agent lets it read code by meaning instead of by string match. The bigger the codebase, the more a wrong guess about a symbol costs, and the more that structural view pays off.

The core of ROI is visibility. If you can clearly see … 1. What it costs to produce the thing you make, and 2. How much money it makes you … then calculating ROI is easy. But with AI, as with the cloud before it, getting that visibility is extremely challenging. Why? Because the cost data associated with each is inherently chaotic.

CloudZero Explorer now tracks your query history and lets you save up to 12 configurations and 10 favorite dimensions, so your most-used cost analyses are one click away.

The Preservation Hold Library silently inflates SharePoint storage. Replace it with Squirrel Recycle Bin Capture for compliant retention in customer-owned Azure.

Today, we’re launching Kubex support for the KAI Scheduler and automated GPU sharing for inference workloads. As AI inference moves into production, platform teams are being asked to serve more models, support more teams, and control GPU costs at the same time. But many inference workloads do not need an entire GPU all the time. When teams reserve full GPUs or oversized GPU fractions to stay safe, expensive capacity can sit idle across the cluster.

When you’re building with AI, you can get a lot done in 30 seconds. Waiting minutes for CI feedback on your latest change can feel like an eternity. Chunk sidecars are designed to give you feedback fast, running your full test suite against the same Linux environment as CI, directly inside the agentic loop. Traditional CI pipelines can take five or ten minutes to catch a basic lint error or failing unit test.