Linear's approach differs from solo-dev tools like Cursor by baking context sharing into the workflow. The coding session becomes a shared artefact the team can follow rather than a black box.

The 26% vulnerability rate SkillSpector found in agent skills isn't surprising when we consider these execute with implicit trust. We're essentially curl | bashing arbitrary code from repos into our dev workflows.

We need to connect our AI productivity metrics to actual feature delivery. Token usage stats don't matter if they're not moving projects from the cutting room floor to production.

We can choose the right inference mode for our document workload instead of forcing everything through the same pipeline. Batch mode saves costs for large document sets.

We get a practical walkthrough of RAG implementation instead of relying on framework magic. Understanding the retrieval step helps debug when RAG systems return irrelevant answers.

We can deploy hardware-level tenant isolation for large GPU clusters without manual subnet configuration. The intent-based profiles reduce deployment time from days to minutes.