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.
WordPress 7.0 stopped hardcoding which blocks support Block Bindings and Pattern Overrides. Support is resolved through a filter now, so any lookup table you were trusting is out of date. Also new: a native Accordion block and an Icon block.
The data science career path in 2026 asks for more than Python. Notebook work is the easy half; the rest is MLOps, domain knowledge, and keeping your own judgment while agents handle the tedious parts.
Most WordPress resilience testing is refreshing the checkout page and hoping. Intent-based chaos engineering starts from a stated hypothesis about behavior instead, with fallback paths around external calls and abort signals tied to checkout completion rather than p99 latency.
Ghost is an agent-first Postgres platform. Notes from using its MCP server and database forking to let AI agents test schema changes and index strategies without touching a shared staging database.
Streaming interfaces jump around and drag the scrollbar back to the bottom. What fixes it: tracking whether the user scrolled away, batching renders into the animation frame, and marking the container up as an ARIA log region.
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.
WordPress.com’s new short-form blogging theme stores micro-posts as ordinary WordPress posts, so replies stay comments and reblogs keep their attribution. Here is what that buys you over renting space on a social platform.
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.