second pass: name the real GPU mechanism (resource request schedules, init container gates startup, probes track runtime health), scope the network DR and reversibility claims to what config backup actually gives you, drop production-grade, and colour the education hash instead of the first letter

src/data/skills.ts

@@ -25,7 +25,7 @@ export const skills: SkillGroup[] = [
     blurb: "Serving vision and language models on real hardware at the edge.",
     points: [
       "Fail-closed computer vision on single-node OpenShift - probes that catch a silently dead model, not just a dead pod.",
-      "One GPU, two jobs - a single card time-sliced between LLM serving and video transcode, with neither noticing.",
+      "One GPU, two workloads - a single card time-sliced between LLM serving and video transcode instead of reserving separate hardware.",
       "Vendor video-analytics stacks re-platformed as first-class Kubernetes workloads, not appliances.",
       "Local models behind an OpenAI-compatible gateway - swap the backend, keep every client.",
     ],