TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D

The clip posted with this case (hosted by the collector’s media CDN); original post on X · 35s.

Combined TypeGPU, ruNNtime, and Jev so camera and mic input feed three local models, and Jev sets lights, shadows, and bloom in realtime.

The original post

TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D

ruNNtime gives me efficient local inference, TypeGPU lets inference and rendering share GPU resources directly with zero copy. That’s 3 separate NN inferences plus rendering, all happening in realtime

Since we control the pipeline, Jev can just sit in the middle and add the semantic bit.

camera + mic → Moonshine + YOLO26 + DepthART → Jev → lights, shadows and bloom

Engagement when collected

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Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.

Where this case fits

Filed under agents & workflows, video, animation & media. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the agents & workflows page.

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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post