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 Reproduce a Flaky Test: From CI Logs to Reliable Failure
A practical guide to turning intermittent test failures into reliably reproducible bugs, with techniques ranging from loop testing to thread sanitizers.
Property-Based Testing: Finding the Off-by-One That Lived in Production for 18 Months
A concrete story of how switching from example-based tests to property-based tests caught a timing bug that had survived code review, QA, and 18 months of production traffic.
How We Reduced End-to-End Test Flakiness by 80% with Deterministic Seeding
After months of chasing intermittent failures in our CI pipeline, we implemented a three-part strategy — deterministic seeding, isolated state per test, and a structured retry budget — that cut flaky end-to-end test failures by 80%.
Snapshot Testing: The 5 Hidden Failure Modes That Will Break Your CI
Snapshot tests seem like a safety net, but they often hide real bugs, bloat diffs, and silently break your CI. Here are the five failure modes I've seen in production and how to fix them.
Testing Async Code: Avoiding Flakiness and False Positives in JavaScript
Async testing is full of traps: unhandled rejections, race conditions, and tests that pass when they shouldn't. Here's how to write reliable async tests in JavaScript.
Testing Error Handling Code Paths: A Practical Approach
Error handling code is often the least tested and most brittle. Here's how to cover those paths with fault injection, mocks, and chaos engineering.