During January 2025, Slabe enhanced observability for the AI-Hypercomputer/JetStream repository by developing a Prometheus-based monitoring feature in Python. He introduced a new gauge metric, jetstream_model_load_time, to measure and report the duration of loading engine parameters within the server library. By adding a sanitized model_name label to these metrics, Slabe enabled granular, per-model performance analytics, allowing for more precise diagnostics and tuning across JetStream deployments. His backend development work focused on improving the reliability and transparency of model startup processes, demonstrating depth in metrics instrumentation and monitoring using Prometheus and Python within a production backend environment.