WordPress 7.0 ships real-time collaboration over HTTP polling, which is fine until traffic picks up. Here is how I swap in a WebSocket transport with Yjs and the sync.providers filter, and what the auth side has to look like.
Machine learning projects fail on planning more often than on algorithms. What works for me: chase approvals early, block real hours for the main pipeline, and stop repointing the stack every time a new model ships.
Notes from the Dev Chat on WordPress 7.0: the AI Client and Connectors API, why the release cycle got extended, the left navigation rethink, and the argument over YouTube support in the Cover block.
Any plugin can claim a top-level spot in the WordPress admin menu, so after 40 installs the sidebar is a junk drawer. A Core proposal wants hierarchy and pinning. Here is what I use in the meantime.
Products drift when nobody writes down what they stand for. How Dieter Rams’ principles helped me kill an upsell on a WooCommerce checkout, plus the workshop and the CSS variables I use to keep principles in the code.
WordPress 7.0 brings AI into core through the AI Client and the Abilities API. Here is what wp_ai_client_prompt() actually does, how to test it now, and what the Core AI team will work on during Contributor Day at WordCamp Asia 2026 in Mumbai.
The CSS shape function replaces the SVG and clip-path hacks used for wavy dividers and blob shapes. How the curve commands and control points work, and why a fixed command count is what lets the browser animate between two shapes.
Jetpack 15.6 lets you set the message and image per network instead of pushing one caption everywhere. Notes on the rebuilt preview modal, the sharing activity log, and the publicize filter that keeps certain post types off social.
DenseNet connects every layer to every later layer and stacks channels instead of summing them the way ResNet does. Why that helps with vanishing gradients, how bottleneck blocks and transition layers keep the tensors in check, and where the memory cost shows up in PyTorch.
Frontier models stack a huge symbolic layer on an absent physical base, what Peter Zakrzewski calls the inversion error. The case for an enactive floor and for treating state-space reversibility as a hard constraint instead of an alignment patch.