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Tag: AI

AI, Core Updates, Development

WordPress 7.0 RC1: AI connectors and real-time editing

WordPress 7.0 RC1 adds the AI Connectors screen and turns on real-time collaboration, with over 134 changes since Beta 5. Here is what to test on staging before the April 9 release, including the RTC constant and the polling load it brings.

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AI, Core Updates, Development

WordPress 7.0 AI features and the off switch debate

The latest AI Contributor meeting covered the global off switch for WordPress 7.0, whether the MCP adapter ships standalone or bundled, and image editing graduating to a core feature. Here is what each of those decisions means for your sites.

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AI, AI in WordPress

LLM agent evaluation before you ship to production

Offline evaluation for LLM agents: routing accuracy, LLM-as-judge scoring, and RAG faithfulness, plus the quality gate I use to block a deploy when the scores come in under threshold.

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AI, AI in WordPress, Development

Plugin Directory MCP Server: submit plugins from your IDE

WordPress.org now runs an MCP server for the Plugin Directory. It lets AI editors like Cursor and Claude validate readme.txt, check submission status, and submit plugins without leaving the editor.

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AI, Core Updates, Development

AI 0.6.0: image editing and a cleaner feature model

AI 0.6.0 turns experiments into a type of feature, adds iterative image editing in the Media Library, and renames hooks. C2PA content provenance is queued for 0.7.0.

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AI, Core Updates, Development

WordPress MCP AI agents can now write to your site

WordPress.com has added 19 write abilities to its MCP integration, so an agent can create posts, pages, media and terms. Drafts by default, explicit approval before every change, and your own role still caps what it can reach.

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AI, Development

Causal inference and the accuracy trap in ML

A readmission model with 94% accuracy made things worse because it found a correlation, not a cause. Where Pearl’s ladder of causation comes in, and how DoWhy’s four steps keep you honest.

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AI, Development

Why NumPy and Pandas give different variance results

Same ten numbers, two different variances. NumPy defaults to the population formula and Pandas to the sample one. Here is what ddof does and how to set it so your pipeline and your prototype finally agree.

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AI, Development

Handling outliers and missing values in credit scoring

Mean imputation and unclipped outliers are what break credit risk models. This covers splitting first to avoid leakage, IQR clipping with training-set bounds, and what to do when the missingness itself is informative.

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AI, Development

Vibe coding without wrecking your codebase

An agent turned a missing database index into a 500-line middleware layer. What I do differently now: plan mode before any code, core WordPress functions first, and a read through the tool calls rather than just the final file.

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