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.
OpenTelemetry Metrics Not Exporting: A Field Guide
A direct, actionable guide for debugging OpenTelemetry metrics that fail to export. Covers collector configuration, SDK timing, and common silent failures.
PyTorch CUDA Out of Memory: Diagnosis and Recovery
A practical guide to diagnosing and fixing CUDA out-of-memory errors in PyTorch, covering memory fragmentation, gradient accumulation, and monitoring with nvidia-smi.
Debugging Elasticsearch Slow Queries: From Shard Contention to Circuit Breakers
A field guide to diagnosing and fixing Elasticsearch slow queries, covering shard contention, hot threads, circuit breakers, and real-world mitigation strategies.
Debugging Cumulative Layout Shift (CLS): From Symptoms to Root Cause
A practical guide to diagnosing and fixing CLS issues in web performance, covering real-world causes, tools, and verification.
LCP Slow: How to Diagnose and Fix Largest Contentful Paint Issues
A practical guide to diagnosing and fixing slow Largest Contentful Paint (LCP) in production web apps.
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.