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Tag: AI

AI, Core Updates

WordPress AI features in 7.0: the MCP adapter splits off

WordPress 7.0 ships the MCP adapter as a separate plugin and leans on reversible decisions while contributor time is short. What that changes for dependency management, plus how to register a custom ability.

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AI, Development

Robust variable selection for scoring models that hold up

Scoring models that look great on one training set often break in production. These are the four filter rules I run across stratified folds so variable selection survives data the model has never seen.

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AI in WordPress, Development

Run OpenClaw on open-source models for a tenth of the cost

Anthropic’s subscription ban pushed me off Claude for OpenClaw. Here is the OpenRouter setup I use with Kimi-K2.5, what it costs, where it runs slower, and the GDPR problem with hosted weights.

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AI, AI in WordPress, Development

Why AI team coordination needs more friction

Automating every quick question makes tasks faster and teams weaker. What the research on informal communication and AI burnout shows, plus three ways to put useful friction back into the workflow.

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AI in WordPress, Development

WordPress AI plugin 0.8.0 makes image generation stable

Version 0.8.0 of the WordPress AI plugin moves image generation out of experiments, adds a Refine from Notes experiment that applies editorial notes for you, and ships the first AI dashboard widgets.

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AI, Development

Cutting a 220,000-token document down to 4,000 with K-means

Part 2 on summarizing a 200,000-word handbook: validating K-means clusters with UMAP and silhouette scores, picking the chunk nearest each centroid, and where the final reduce step still drops topics.

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AI, Development

Why byte-level models beat LLMs for cross-script name search

Levenshtein and Soundex break the moment a query crosses script boundaries. Notes from a sanctions screening build on byte-level transformer embeddings, ANCE hard negative mining, and where CJK still causes trouble.

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AI, Development

Zero-shot classification instead of regex for messy data

Regex and K-means both fall apart on short free-text data where the same idea gets phrased three different ways. Zero-shot classification with a local model like Gemma 2 handles the paraphrasing, and this is the Ollama pipeline I ran over several thousand security annotations.

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AI, Development

Scaling RL with Approximate Solution Methods

Tabular RL breaks the moment the state space grows past a toy grid. Approximate Solution Methods swap the lookup table for a weight vector and semi-gradient updates, so one state teaches you about every state like it. Includes where linear models still beat a neural net.

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AI

How a local SLM fixed my nightly CI/CD pipeline

Twenty-three pipeline failures in six weeks, none of them caused by my code. This is how I replaced GPT-4 with a seeded Qwen2.5 on Ollama, plus the GitHub Actions caching and Pydantic validation details that made it stick.

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