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.
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.