WordPress 7.0 benchmarks show TTFB about 10% worse than 6.9 while LCP holds steady, which puts the cost on the PHP side. Notes on the script module dependency ticket, oversized Link headers that make Nginx return 502, and polling in real-time collaboration.
Binary churn flags tell you that a user left but say nothing about the timing. This post covers discrete time-to-event modeling: why fixed intervals fit WordPress data, how right censoring skews a model, and a small PHP function for hazard rates.
An LLM can retrieve the right document and still quote the wrong price. This is the detection and healing layer I built to catch that: five failure patterns, a faithfulness score, deterministic patches, and a SQLite drift monitor that caught a real bug in staging.
LangChain is fine for a prototype. Once there is real traffic, several agents coordinating and a pager attached, the abstraction costs more than it saves. This is the case for writing the orchestration layer yourself.
Utility software gets designed to be invisible, which is why people resent it. Linear, Vercel and MacPaw treat the maintenance layer as something a person actually sits through, and it shows.
Ridge, Lasso and ElasticNet land within 0.3% of each other on prediction accuracy, but Ridge fits eight times faster and Lasso’s feature selection falls apart once features are correlated. Three numbers tell you which one to fit.
Hard-coded constants assume a perfect environment. This covers four ways to model uncertainty, from robust optimization to two-stage recourse, and refactors a fixed cron batch size into one that reacts to load.
One ballot label counted as two parties made English local elections look like they were splintering. The fix was normalizing party families before aggregation instead of after, and the headline finding reversed once we did.
Appending text to a container is the easy part. This post covers the scroll flag that stops snap-back, batching renders to keep layout shift down, ARIA live regions for screen readers, and cleaning up after a broken stream.
High accuracy is the easiest number to fake. This post covers the machine learning pitfalls I keep finding in production systems: library defaults nobody checked, data leakage from scaling before the split, and metrics that ignore what the model actually decides.