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Tag: AI

Development, E-commerce Development

The modern data stack is hitting a ceiling for WordPress

Databricks and Snowflake are running into a market ceiling while the fees to connect your own data keep climbing. Why WooCommerce reporting is usually better off staying in WordPress, on lean SQL and a WP-CLI command.

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AI, Development

Fixing AI/ML data transfer bottlenecks in PyTorch

GPU starvation, not GPU speed, is what slows most training runs. How I find AI/ML data transfer bottlenecks in an nsys trace, then fix them with num_workers, pinned memory and a CUDA stream prefetcher.

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AI, Core Updates, Development

The WordPress AI Client heads for Core 7.0

WordPress AI Client v0.4.0 rewrites the Google, Anthropic and OpenAI providers, adds PSR-14 events and a caching layer. What that means for plugin authors ahead of the February 19th Beta 1 for Core 7.0.

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AI in WordPress, Development

WP-Bench: measuring how well AI models know WordPress

WordPress now has WP-Bench, a benchmark that tests AI models on Core APIs, security patterns and block development. It runs the generated code in a real WordPress runtime instead of grading answers on paper.

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AI, Development

Technical debt in AI development: the God function problem

AI can write a WooCommerce feature in seconds, and it usually writes it as one 150-line God function with a hard-coded API key. This is what that debt costs and how to refactor it into something maintainable.

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AI, AI in WordPress, Development

AWS Bedrock instead of direct API calls for WordPress AI

Hardcoded API keys hold up until the first model deprecation. Here is how I use AWS Bedrock as the gateway instead: Boto3 calls, inference profiles, IAM permissions, and a triage assistant that runs on Lambda rather than inside the WordPress request cycle.

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AI, Development

How I actually use Claude Code as a WordPress dev

Chatting with an LLM in a browser tab is a bottleneck. This is the Claude Code setup I use instead: slash commands for the prompts I repeat, an AGENTS.md file for project memory, and plan mode before any code gets written.

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AI, AI in WordPress, Development

How Infini-attention keeps long context from eating memory

Longer context windows cost server memory, not just compute. Infini-attention replaces the growing KV cache with a fixed-size compressive memory matrix updated by a Delta Rule, which cut memory use by 114x in Google’s tests.

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AI

Why correlated features break Shapley value explanations

SHAP is treated as the default for model transparency, but correlated features dilute the signal through the Symmetry Axiom. Grouping features into concepts and picking them iteratively gives explanations you can act on.

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AI, Development

How I run coding agents in parallel without losing track

Running several coding agents at once only works if you plan the work first and keep them off each other’s files. Here is the four-step workflow I use with Claude Code and Warp, plus the migration mess that taught me the isolation rule.

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