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CASE FILE / Decision automation
TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D ruNNtime giv…
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
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Konrad Reczko · @reczko_konrad
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
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Chinese translation
typeGPU + ruNNtime + Jev @typesafeai是一个非常有趣的组合：D ruNNtime为我提供了高效的本地推理，Typegu让推理和渲染直接以零副本共享图形处理器资源。这是3个独立的NN推断加上渲染，所有这些都是实时发生的 由于我们控制管道，Jev可以坐在中间添加语义位。 相机+麦克风| Moonshine + YOLO 26 + DepthART | Jev |灯光、阴影和绽放
What this case shows
- Use JEV as a typed, fast decision layer
- Wire probability into business rules
- Open the original post for the full demo