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Tag: AI

AI, AI in WordPress, Development

Drift detection: why a live ML model quietly gets worse

The difference between data drift and concept drift, the two tests I reach for first (K-S and PSI), and how I wire the check into a WP-CLI command so a drifting model triggers an alert instead of a bad quarter.

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AI in WordPress, Development

Your agent picks the wrong tool because of your MCP schema

Agents pick the tool with the longer description, not the right one. What that length bias looks like in a WooCommerce integration, how to keep internal arguments out of the prompt, and why a proxy beats editing someone else’s MCP server.

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AI, AI in WordPress, Development

AI coding agent context: give it a map, not your whole repo

Three habits that stop a coding agent inventing hooks and tables: an AGENTS.md file it has to keep updated, real documentation and schema files in the context, and a fresh thread when the task changes.

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AI in WordPress, Development

The plugin queue doubled in 2025: what that means for your code

The Plugins Team 2025 retrospective puts submissions at about 330 a week and reviews up 40.6%, with the same size team. Here is what the automated scans catch and how to run the Plugin Check plugin before the reviewers do.

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AI, Development

Fourier features and the spectral bias problem in MLPs

A neural network trained on the Mandelbrot set comes out blurry because of spectral bias. Multi-scale Gaussian Fourier Features fix that by putting the high frequencies into the input instead of asking the model to find them.

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AI, AI in WordPress, Development

Retrieval-augmented forecasting: giving models a memory

Retrieval-augmented forecasting adds a search step to a time-series model, so it can pull similar windows out of history instead of leaning on frozen training weights. Covers embeddings, FAISS indexing and four ways to fuse retrieval with a forecaster.

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AI, Development

Why small-scale credit scoring cannot be private and fair

Differential privacy noise hides the fairness gap when a bank only has a few thousand records. Across half a million records a federated model reached 96.94% accuracy with a 0.069% fairness gap, which is the case for joining a consortium.

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AI, Development

Why visual anomaly detection models fail in production

High AUROC on MVTecAD does not mean a model survives a production line. Notes on input resolution, center cropping, background masking, and where early stopping usually goes wrong.

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AI in WordPress, Development

Probabilistic multi-variant reasoning beats one-shot LLM code

An LLM hands you one confident answer and hides the trade-offs. Probabilistic multi-variant reasoning asks it for several options plus the ways each one fails, and leaves the decision with you.

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AI, AI in WordPress

Where AI in UX actually saves me time

Two years of daily use, boiled down: AI in UX is good at sorting research transcripts, flagging the obvious usability problems and drafting copy from client bullets. The judgment stays with you.

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