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Category: AI in WordPress

AI, AI in WordPress, Development

Why your data stack is not ready for AI agents

Dashboards are no longer the main consumer of your data. Here is what I would change in a data stack to make it readable by agents: fewer tools, open table formats, a real semantic layer, and feedback loops that act without waiting for a human.

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AI in WordPress, Design, Development

Why human strategy still runs an AI-accelerated workflow

AI raised the floor on production, and that changed what designers are actually paid for. Here is where human strategy still decides the outcome, and why judgment now carries the accountability.

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AI, AI in WordPress, Development

Machine learning at scale: availability beats accuracy

Machine learning at scale is an infrastructure problem before it is a modeling one: why availability beats accuracy, how label leakage fakes good metrics, and why heavy models and fallbacks belong on different hardware.

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AI in WordPress, Development

How I get production-ready code out of Claude Code

Vibe coding a feature is easy; keeping it out of the technical debt pile is not. A CLAUDE.md handbook, plan mode before any edit, and a review pass that assumes something is broken.

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AI in WordPress, Core Updates, Development

WordPress 7.0 Beta 2: AI connectors and the iframed editor

WordPress 7.0 Beta 2 adds one Connectors screen for AI providers and forces Block API v3 blocks into the iframed editor. Both change what your plugin code has to do before the April 9 release.

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AI, AI in WordPress

Claude Skills, subagents and the cost of context bloat

Claude Skills keep instructions in files the agent reads only when needed, which cuts the token overhead of always-on MCP tools. Subagents push it further by throwing away their own context once the job is done.

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AI, AI in WordPress, Development

Why AI-generated code stops being maintainable by month three

AI-generated code ships fast and then stalls, usually around month three, when nobody can safely change the file it all lives in. A WooCommerce example shows the monolith version next to the decoupled one.

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AI, AI in WordPress, Development

Agentic RAG vs classic RAG: pipeline or control loop

Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.

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AI, AI in WordPress, Development

Why an enterprise AI prototype stalls before production

Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.

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AI, AI in WordPress, Development

Why WordPress devs still need critical thinking in AI

AI now generates faster than any of us can type, which makes judging the output the actual job. Where LLM code tends to break in WordPress: hooks that no longer exist, uncached get_option calls in loops, and raw SQL that HPOS will punish.

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