The cross-entropy layer is where LLM training tends to run out of VRAM. This post walks through fusing the linear projection and the loss into one Triton kernel, why tiling and online softmax cut peak memory by 84%, and where my first attempt went wrong.
Built-in DAX time intelligence handles a standard Gregorian calendar fine, but custom fiscal years are another story. Where the built-in functions stop helping, and how a row rank column took one client’s moving average from 500ms to 110ms.
The 2025 Web Almanac covers 17.2 million sites. Variable fonts and text-wrap are finally common, but median mobile page weight is up 202.8% since 2015 and two thirds of sites strip their focus outlines.
The Core-AI team shipped v0.1.0 of the WordPress PHP AI Client SDK. It is a provider-agnostic Composer package you bundle into your own plugin, so swapping the LLM behind a feature stops being a refactor.
Healthcare treats data as an ontology rather than a row, which is why its knowledge graphs outlive the systems built on them. Layered ontologies, regulated vocabularies, and what web developers could borrow from both.
Version 0.2.0 of WordPress AI Experiments adds excerpt generation that authors can edit or reject, and an Abilities Explorer screen for testing every registered ability in the browser.
Keeping up with CSS is a filtering problem more than a reading problem. I traded years of RSS folders for the I Heart CSS squad on DailyDev, and this is what a curated feed does that a raw aggregator never did.
Cursor turns a four-hour refactor into a 20-minute one, but only with a plan. My four steps: spot the trigger, write a plan.md, let Claude execute with test scripts, then compare the output against the original.
Six months in, WordPress Core AI has a stable shape: the Abilities API lands in 6.9, WP Client AI abstracts the LLM providers, and the MCP adapter turns a site into a server that agents can call.
Junk topics usually mean the pipeline is wrong, not the data. Here is how seeded KeyNMF and an LLM summarization step keep a topic model focused without burning the whole compute budget.