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.
The Art of the Minimal Reproducible Example: A Debugging Superpower
A minimal reproducible example (MRE) is more than just a snippet — it's a tool that isolates the bug, saves time, and gets you faster answers. Here's how to build one that works, with a real-world war story.
Retry Storms: When Retries Make a Cascading Failure Worse
Retries seem like a safety net, but in a distributed system under load they can turn a small hiccup into a full outage. Here's how retry storms form and what to do about them.
Caching Bugs Are the Worst: A Postmortem on Stale Data Disasters
A deep dive into the most insidious caching bugs, with real postmortems and practical patterns to avoid stale-data disasters.
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.
Debugging Flaky Tests for Good: A Systematic Approach to Root Cause Analysis
Flaky tests waste time and erode trust. Here's how to find and fix the actual root cause instead of just rerunning.
Idempotency Keys: The Safety Net Your Payment API Needs
How idempotency keys prevent double charges, lost refunds, and data corruption in payment APIs. With real-world examples and edge cases you haven't considered.