Results for “burnout-detection”

Diagnose MSBuild build performance bottlenecks using binary log analysis, covering timeline analysis, performance summary interpretation, and seven common bottleneck categories.

Classify AEM 6.5 LTS and AMS workflow issues by symptom, gather relevant logs and metrics, and map to runbooks or Splunk searches.

Diagnose pod crashes, restart loops, Redis failures, resource pressure, and other service misbehavior in the crs namespace on Kubernetes.

Guides developers through capturing diagnostic artifacts to diagnose production .NET performance issues, including tool selection and data collection across Windows, Linux, containers, and Kubernetes.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

Generates and runs load tests for API endpoints using k6, Locust, or Artillery. Detects the API framework, creates realistic user behavior flows, executes ramp-up, sustained, spike, and stress profiles, and produces a bottleneck analysis with optimization recommendations.

Diagnose and eliminate performance bottlenecks across web, API, database, and code with load testing, profiling, and optimization techniques.

Profiles application performance across database queries, API call chains, memory usage, bundle sizes, network waterfalls, and frontend rendering, then produces ranked optimization recommendations with estimated impact.