Confidential computing protects workloads while they run. Verification answers a different question: did the expected workload actually run, and can we establish that continuously? The two ideas are starting to converge around enterprise AI infrastructure.

Inference Labs @inference_labs · 6 天前

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#1

From fuel leaks to smoke and fire, many airport risks are visual before they become operational problems. @hudsongrae_me is showing how Sertn can train models to detect these anom…

Inference Labs @inference_labs · 1 天前
#2

1/ Airports already have cameras covering stands, baggage halls, terminals, and airfield movement. The opportunity is not always adding more hardware. It is turning existing foota…

Inference Labs @inference_labs · 2 天前
#3

Long-horizon AI is making outputs much larger and workflows much harder to inspect. The challenge is no longer only whether a model can finish the task, but whether the path there…

Inference Labs @inference_labs · 3 天前
#4

In a factory, airport, or logistics site, the physical event may last seconds. The model output may trigger a workflow immediately. Sertn is useful because evidence can be created…

Inference Labs @inference_labs · 4 天前
#5

NVIDIA now describes physical AI safety as something that has to span hardware, software, models, sensors, and deployment. That is important. A safe robot is not just a good model…

Inference Labs @inference_labs · 5 天前
#6

1/ The first generation of enterprise AI borrowed human infrastructure. Service accounts. API keys. User credentials. That worked while AI was mostly assisting people. It becomes…

Inference Labs @inference_labs · 2026-09-29

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