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IronNathanAlvares
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Remove pip from the runtime image, which is what the first scan was for
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#25:
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Pin the Trivy action to a commit, having first named a version that d…
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GPU sharing measured, and it says ADR-0003 is right for the wrong reason
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#23:
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DRA: the shapes are real, and half of it can be tested without hardware
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#22:
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M4a: preemption, autoscaling on demand, and two claims put to the test
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#21:
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Screenshots, run instructions, and the service worker verified
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#20:
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M3 part one: the on-device tier as a real page, and security worth as…
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Let mypy run without matplotlib installed
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Commit the reports the README quotes and the charts are drawn from
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M2 result: the escalation rate is 0.0%, and that is the failure
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Figures for the README, light and dark, from the saved reports
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Check the on-device 4 MB claim: unverified, and here is why
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#14:
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Publish the accuracy floor as a trade curve, not a value I made up
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#13:
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Fix label parsing: two class names contain a space
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#12:
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The escalation rate alone is not a verdict
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#11:
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Bake the model into the image, when there is one
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Runbook: kind nodes restart themselves under memory pressure
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Two-way split for evaluation-only datasets
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Finding 7: measuring escalation on one holdout answers the wrong ques…
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Export single-file ONNX, and test the serving path's silent failures
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#6:
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Wire the ONNX serving path, and a runbook for the traps
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#5:
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Let mypy run without torch installed
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#4:
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M2 infrastructure: datasets, splits, calibration, escalation measurement
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#3:
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M1 complete: Gateway API, TLS and rate limiting at the edge
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#2:
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M1: cluster, node pools, CPU tier, observability, CI
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#1:
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