Flutter + Physical AI: Release Checklist for Performance, Safety, and Reliability V-Modal published a production release checklist for connecting Flutter apps to physical AI systems such as smart glasses, NVIDIA Jetson, ROS 2, and AI models, covering latency, stale data, degraded modes, privacy, and recovery. The guidance calls for sustained testing on supported devices that records p95/p99 frame timings, source-to-display age, memory growth, dropped updates, and reconnect time, plus command acknowledgement and reconciliation, privacy-reviewed logs, a documented model/API capability matrix, and rollback thresholds. The company also points to its Flutter and Android SDKs on GitHub. A production release connecting Flutter to smart glasses, Jetson, ROS 2, and AI models needs more than a smooth demo. Review latency, stale data, degraded modes, privacy, and recovery. Run sustained tests on supported devices. Record p95/p99 frame timings, source-to-display age, memory growth, dropped updates, and reconnect time. Cover network loss, gateway restarts, unavailable model capabilities, permission revocation, low memory, thermal throttling, and stale telemetry. Ensure important commands have IDs, authorization, acknowledgement, timeout, and reconciliation behavior. Distinguish unknown outcomes from confirmed failures. Ensure logs are useful without exposing raw media, secrets, or unnecessary personal data. Correlate client and gateway diagnostics. Document supported device/model combinations, performance baseline, and thresholds that trigger rollback. Monitor regressions after rollout. This Dart snippet illustrates one part of the design. Adapt types, transport behavior, timestamp semantics, and platform APIs to your actual system before production use. js const releaseChecks =