One LLM cannot draft emails, read documents and optimize a sales pipeline well. A look at splitting the work across reasoning, orchestration and execution layers, with TD learning doing the sequential decisions.
WordPress.com shipped an alpha Telegram bot for managing a site from your phone, opened Studio Sync to Personal and Premium plans, and split MCP read and write tool access per site.
Voice picking used to mean vendor-locked hardware. ElevenLabs voice AI does the same job off an ordinary smartphone, and this covers the WordPress side of it: a cached text-to-speech wrapper, plus the latency and noise problems that sink most builds.
Waiting for the F1-score to drop means the fraud already happened. A FIDI Z-score on the symbolic layer flagged drift a full window earlier across five seeds. The code is below, along with the covariate blind spot it will not catch.
WooCommerce 10.7 is mostly a performance release: the /wc/v4/orders endpoint drops from 271 queries to 132 and checkout loses about 15% of its SQL. It also ships a Fulfillment API beta and moves the Fulfillments namespace, which breaks old use statements.
Vector search on its own leaves an agent repeating things that stopped being true months ago. Here is the memory lifecycle I build instead: decay scores, contradiction detection, confidence values, compression, and a background worker that does the cleanup.
Production models rarely decay on a smooth curve. They hold steady and then lose a fifth of their recall in an afternoon. Here is the R-squared test that tells you which regime you are in, and the three triggers I use instead of a retraining calendar.
Jetpack now lets you comp a paid subscription from the Subscribers screen, so gifting access no longer means a custom VIP role and a pile of expiring metadata. Here is what changes, plus the math on Substack’s 10% cut.
Power BI’s new custom calendars shift prior-year dates by a day across a leap year, because DATEADD works from the distance to the parent period. Stepping back 12 months instead of one year fixes it.
VLA models put vision, language and motion in one latent space instead of three separate pipelines. Notes on why that unification works, and how action tokenization, diffusion heads and flow matching differ once the model has to output real movement.