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.
18 playbooks · 509 guides · 96 articles · 13 linked practice labs ·skip to practice
Fast pattern recognition for the production failures engineers see most often.
18 playbooks
Debugging Env Var Issues
A concise checklist for incidents where local works but CI, staging, or production changes behavior.
Debugging Cache Stampedes
How to recognize and contain request spikes when cached data expires.
Debugging CORS Origin Bugs
A practical way to inspect origin parsing, credentials, and header behavior.
Debugging Retry Bugs
Retry issues often hide the first failure and create duplicate work.
Debugging Timezone Bugs
Time bugs usually live at day boundaries, DST changes, and unit conversions.
Debugging Pagination Bugs
Pagination bugs hide in boundary math, cursor reuse, and new records arriving between pages.
Structured investigations for the failure modes engineers meet in real systems.
Flutter setState Not Rebuilding Widget – Debugging Stale UI
Diagnose why setState doesn't trigger a rebuild in Flutter: missing context, wrong state class, or identity issues. Includes concrete commands and fixes.
Flutter FutureBuilder Rebuilds Infinitely: The Real Causes and Fixes
A practical guide to stopping FutureBuilder from rebuilding endlessly in Flutter, with root causes, diagnosis steps, and real war stories.
React Native FlatList Performance: Why Your List Jitters at 200+ Items
A hands-on guide to diagnosing and fixing FlatList jank, dropped frames, and blank cells in React Native apps, with real profiling commands.
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 PyTorch Tensor Shape Mismatch Runtime Errors
A practical guide to diagnosing and resolving shape mismatch errors in PyTorch, covering common causes, debugging commands, and fixes.
PyTorch Gradient Explosion Producing NaN Loss
Diagnose and fix NaN losses caused by gradient explosion in PyTorch models. Covers gradient clipping, weight initialization, and data normalization.
Long-form thinking on debugging habits, observability, and the systems around the bug.
How to Read Error Messages: A Debugging Protocol
Most engineers skim error messages and miss the signal. Here's a repeatable protocol for extracting maximum information from stack traces, log lines, and crash dumps — with a real incident walkthrough.
When to Escalate a Bug: A Decision Framework for Junior and Mid-Level Engineers
A practical framework for junior and mid-level engineers to decide when to escalate a bug, with real-world examples and criteria beyond time spent.
Debugging vs. Firefighting: Why Treating Production Incidents as Debugging Sessions Fails
When production goes down, your brain wants to debug. That instinct costs you hours. Here's why firefighting requires a fundamentally different approach, and the specific process I use to switch modes.
Writing an Incident Runbook That Actually Gets Used in Production
A runbook isn't a document—it's a tool. Here's how to write one that reduces MTTR and doesn't embarrass you during the next PagerDuty alert.
Debugging a production incident with your boss on Slack: staying rational when everything is on fire
A personal account of debugging a cascading Redis failure while the VP of Engineering watched, and the mental models that kept the fix from turning into a rollback.
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.