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.
Why Your Production Logs Are Lying to You
Logs tell you what the code reported. They almost never tell you what actually happened. Here is the gap, and how to close it.
Debugging Production Issues Without a Debugger: Approaches That Work
Attaching an interactive debugger in production is usually impossible. Here’s how I gather signal, reproduce issues, and restore service using other techniques.
Defensive Logging: Patterns for Surviving Production Data Rot
Most logging advice stops at 'log more'. Here's how to log defensively—handling nulls, encoding, PII, and context propagation before they rot your observability pipeline.
Tracking Down a 200 MB Leak with Python Memory Profilers
A production API was silently leaking 200 MB of RAM every hour. Here's how memory profilers found the culprit—a forgotten NumPy array reference—and how you can apply the same techniques.
Reading CPU Flame Graphs: What the Hot Colors Actually Tell You
Flame graphs are everywhere, but most engineers read them wrong. Here's how to identify real bottlenecks, avoid common misinterpretations, and turn profile data into actionable fixes.
When Logs Lie: The Gaps Between What You Log and What Actually Happened
Logs are the first thing we reach for during an incident. But they can be misleading, incomplete, or outright wrong. Here's when to trust them and when not to.