A credit model can score well and still fall apart in production if its variables drift. Two checks before estimation: whether each variable holds a consistent risk direction, and what PSI says about distribution shifts between datasets.
One best model is one point of failure. I go through the three layers of a stacking ensemble, how predictions become features for the next layer, and what the extra training time actually costs you.
Multimodal RAG usually fails at retrieval, not at vision. I walk through the proxy-pointer approach: parse a document into a section tree, keep image pointers on each node, and retrieve whole sections instead of sliding-window chunks.
Some loops will not vectorize, and Python object overhead eats whatever is left. Here is how PyO3 and Maturin let you move a hot loop into Rust, how Rayon uses the rest of your cores, and what the throughput looks like afterwards.
Prompt caching only pays off when the prefix stays byte-identical. I go through the mistakes that break it, a semantic cache layer for WordPress, loading tool definitions on demand, and what to keep or drop between turns.
set_detect_anomaly points at the wrong layer and costs you 10x on CPU. A forward hook adds roughly 3ms per pass, names the layer that produced the NaN, and pairs with a gradient norm check that fires a step earlier.
Version 0.7.0 of the experimental AI plugin adds content classification, bulk alt text generation, and hooks for changing system instructions before results are saved. Here is what holds up, what to watch on a slow host, and what 0.8.0 has queued.
Row-wise iteration is what makes most Pandas code slow. This walks through profiling with %timeit and memory_usage, replacing iterrows with vectorized operations, cutting memory with category types, and when to move to Polars or DuckDB instead.
WordPress 7.0 ships the MCP adapter as a separate plugin and leans on reversible decisions while contributor time is short. What that changes for dependency management, plus how to register a custom ability.
Scoring models that look great on one training set often break in production. These are the four filter rules I run across stratified folds so variable selection survives data the model has never seen.