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.
WordPress 7.0 ships three pieces of AI plumbing: the Abilities API for registering what a site can do, the AI Client for provider-agnostic model calls, and the MCP Adapter that exposes both to assistants like Claude.
Output entropy tells you a translation model is unsure but not why. This walks through bidirectional cross-attention, the misalignment features it exposes, and a small QE head that scores tokens without retraining the NMT model.
A walkthrough of quantum simulation in Python: a one-qubit circuit, the Hadamard gate, measurement into a classical bit, and a depolarizing noise model that makes the clean 50/50 split look more like real hardware.
Reading revision numbers off 4,700 engineering drawings by hand was going to cost 160 person-hours. PyMuPDF handled every file with a text layer, GPT-4 Vision took the 1990s scans, and the batch finished in 45 minutes at 96% accuracy.
Claude Code will scaffold a WordPress plugin faster than you can spec one, which is the problem. The workflow I use: a CLAUDE.md spec first, the terminal agent second, and a manual review of everything before it ships.
Proprietary MMM platforms hand you a number and hide the math behind it. This is the stack I use instead: Google Meridian for the Bayesian side, a local Mistral 7B to turn coefficients into something a marketing manager can act on.