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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.

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.

When we introduced native Pipelines authentication for Bitbucket Packages, we made it easier to publish artifacts from CI/CD without relying on personal credentials. Now we’re extending that integration further: package artifacts published through the Pipelines integration can display a Source Pipeline, making it easy to trace an artifact back to the pipeline run that created it.

In November 2025, we introduced new triggers and workflows to Bitbucket Pipelines to help teams manage and scale complex CI/CD workflows. We later extended that foundation with additional event-based triggers for pipeline, deployment, and pull request events. We’re now extending that model with a new package-artifact-created trigger.

Machine learning models are not like other software artifacts. A single fine-tuned LLM can weigh 70 GB. A model family may share 95% of its weights across dozens of variants. When hundreds of developers, training jobs, and GPU clusters all need the same model at the same time, the infrastructure underneath needs to be built for it.

Eight buyer questions for SharePoint archiving 2026. Compares Microsoft 365 Archive, AvePoint Opus, Archive360 and Squirrel on data, Copilot, labels.

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.

Ask a software engineer what they do and the answer, for years, has been some version of "I write code." That assumption is unwinding fast. AI agents can now write code, review pull requests, run tests, and ship to production, and they're taking on a fast-growing share of that work. As agents absorb more of the execution, the human role shifts.