Buglyst Blog
Learn to debug under pressure.
Playbooks for fast pattern recognition, guides for the full investigation, and articles for the engineering judgment around the edges.
17 playbooks · 509 guides · 95 articles · 12 linked practice labs ·skip to practice
Fast pattern recognition for the production failures engineers see most often.
1 playbook in Observability & Performance
Structured investigations for the failure modes engineers meet in real systems.
Slow API response: how to debug latency issues
An API endpoint that used to return in 50ms now takes 3 seconds. Find the bottleneck before your users notice.
Observability missing logs: how to debug gaps in logging and monitoring
A critical error happened in production but there are no logs for it. The log level is too high, logs are dropped under load, or the log pipeline has a silent failure.
Log says success but the user still fails: how to debug misleading logs
Your application logs 'Operation completed successfully' but the user sees an error or gets no result. The log is lying — it is logging intent, not outcome.
Diagnosing Hidden Performance Bottlenecks in React Apps
Cut through noise and pinpoint real React performance issues. This guide details actionable profiling, interpretation, and advanced optimization techniques.
Diagnosing Node.js Event Loop Lag and High Latency in Production
An advanced guide to investigating and resolving unexpected event loop lag and high request latency in live Node.js applications.
Node.js Heap Snapshot Analysis: Finding the Leak That Survived GC
A practical guide to analyzing Node.js heap snapshots to find memory leaks that survive garbage collection. Covers snapshot comparison, retaining paths, and hidden class instances.
Long-form thinking on debugging habits, observability, and the systems around the bug.
Logs, Traces, and the Lies They Tell: Debugging a Stuck Queue in Production
A single stuck queue brought down an entire checkout flow. Here's how we traced the failure across services, what the logs didn't say, and the tools that finally showed the truth.
Reading Distributed Traces to Find Latency: A Field Guide
Tracing tools generate a firehose of data. Here's how to filter the signal from the noise and actually find the root cause of high latency.
Why Your P99 Latency Is Lying to You (and What to Use Instead)
The 99th percentile is the go-to metric for tail latency, but it's easy to fool. Here's a real outage caused by trusting P99, and how to build a more honest monitoring stack.
Reading Flame Graphs: The Non-Obvious Patterns That Reveal Real Bottlenecks
Flame graphs are everywhere in performance profiling, but most engineers only scratch the surface. Here are the patterns that actually tell you where your code is slow.
Observability vs. Monitoring: Why Your On-Call Rotation Still Wakes You Up at 3 AM
Monitoring tells you something is broken. Observability lets you figure out why—without guessing, without SSH, without restarting the pod.