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How to Deal with Jenkins Performance Issues
Jenkins, a popular CI/CD pipeline, is used for several critical operations in the organization, such as building applications, conducting automated tests, and deployments in pre-prod and prod environments. If Jenkins is down or slow, engineers' productivity will be severely hampered. Thus, extra care is given to major organizations to keep them up 24/7. Jenkins can... Continue Reading →
Java Mission Control (JMC) vs. yCrash JFR Player
The document compares Java Mission Control (JMC) and yCrash's JFR Player for diagnosing JVM issues. JMC provides raw telemetry analysis manually, while JFR Player automates diagnostics via AI and offers collaborative cloud-based features. Key differences include proactive data capture, enhanced security, and capabilities for parsing fragmented logs and heap dumps, boosting efficiency in performance troubleshooting.
AI-Powered RCA Summary: Instantly Understand What Went Wrong
yCrash enhances AI-powered root cause analysis by integrating structured JSON outputs from extensive diagnostics with Large Language Models (LLMs). This allows for clearer executive summaries and interactive reports, improving accessibility for technical and non-technical users. Key benefits include precise metrics, historical analysis, visualizations, and cost efficiency, ensuring effective incident troubleshooting.
“GC Log Analysis Using Deterministic AI” webinar
GC logs are essential for JVM performance analysis, but their complexity complicates interpretation. In a recent webinar, Ram Lakshmanan discussed how Deterministic AI can refine GC log analysis, enhancing accuracy and safety while addressing the limitations of traditional AI methods. This approach enables faster, reliable insights for troubleshooting performance issues.
JAX MAINZ 2026 – JAVA NATIVE MEMORY LEAKS & HOW TO FIX THEM
Every May, JAX MAINZ hosts a five-day conference focused on Java and Software Architecture for professionals. This year, architect Ram Lakshmanan discussed "JAVA NATIVE MEMORY LEAKS & HOW TO FIX THEM," addressing the challenges of detecting native memory leaks in Java, providing tools and techniques to identify and resolve these issues effectively.
JAX MAINZ 2026 – MACHINE LEARNING & MICROMETRICS TO FORECAST PRODUCTION PROBLEMS
Every May, JAX MAINZ hosts a five-day conference for Java and Software Architecture professionals. This year, architect Ram Lakshmanan presented on using machine learning and micro-metrics to forecast production issues. His approach highlights nine critical metrics, promoting proactive problem detection and effective root cause analysis through advanced observability techniques.