WordPress 7.0 ships the WordPress AI Client, a standardized PHP API for generative AI in plugins. This guide builds a provider-agnostic image generation tool for the Media Library using the prompt builder, support checks, and a REST endpoint.
The Titanic dataset is the classic first project in data science. Using Python, Pandas, and Seaborn, this walkthrough shows how gender, passenger class, and family size shaped who survived, and what that teaches developers about reading data.
WordPress 7.0 real-time collaboration has been pulled from the release. Matt Mullenweg cited race conditions, memory use, and bugs found in fuzz testing. Here is why holding it back was the safer technical call.
Most AI agent projects fail because of weak evaluation, not weak models. These are the 12 metrics I run against every response, grouped into retrieval, generation, agent behavior, and production health, with PHP and Python examples.
WordPress 7.0 development lost real-time collaboration this month over race conditions and load concerns. What’s left for the May 20 release: a native content types system, the @wordpress/grid package, and HEIC and per-block CSS fixes.
Price hikes often get blamed for churn that is really about a finished project. This covers flagging project milestones in WooCommerce Subscriptions and using DiD and Shapley values to tell a price shock apart from value exhaustion.
PDFs are where automation goes to die. I built the same B2B document extraction pipeline twice, first with regex and then with a local LLaMA 3 model. This is where each approach wins, where it breaks, and when the extra cost is worth it.
Rare event forecasting breaks most models because they optimize for accuracy instead of impact. Here is how a multi-head Transformer, the Generalized Pareto Distribution, and the True Skill Statistic model the tail, with solar flares as the running example.
Run C code in the browser with WebAssembly, no local toolchain required. This walkthrough uses GitHub Codespaces and Emscripten to compile and run your first WASM app, then wires it to JavaScript.
My reproduction of the Maas et al. (2011) word vectors for sentiment analysis paper: why semantic context alone misses polarity, and how a hybrid objective plus a linear SVM gets the accuracy back.