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Tag: Backend

AI in WordPress, Development

Applied statistics and machine learning in production WordPress

Notes on porting statistical models into WordPress: where research gets diluted instead of distilled, how real-time aggregates in user meta leak data through race conditions, the atomic SQL fix, and why a bigger LLM is the wrong answer to arithmetic.

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AI, Development, E-commerce Development

What breaks in retrieval-augmented generation at scale

Retrieval-augmented generation is past the demo stage. Notes on chunk size as an experimental variable, why HNSW precision degrades as the index grows, the latency cost of extra retrieval layers, and a transient wrapper for caching retrieval in WordPress.

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AI, AI in WordPress

Demand forecasting with graph neural networks in WooCommerce

Rolling-window forecasts treat every SKU as independent. This post models a WooCommerce catalog as a graph instead, with GraphSAGE over shared plants and attributes, the PHP that extracts SKU neighbors, and where heterogeneous transformers go further.

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AI, Development

How local LLMs found a faster matmul on a MacBook

A multi-agent Autogen loop running Mixtral 8x7B locally on an M3 MacBook rewrote a Rust matrix multiply until it hit NEON SIMD and Rayon, taking it from 760ms to 359ms. Notes on how the loop was set up and where the generated code went wrong.

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AI, Development

Data poisoning: who manipulates training data and why

Poisoned training data does not crash anything. It shifts your model’s weights while the accuracy score stays high. Who does it and why, from malware authors to artists using Nightshade, plus what a sanitization layer on your ingestion path needs to check.

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AI, Development

Killing the yellow halo in AI image compositing

RGB alpha blending averages channels, so background color stays baked into the edges of every cut-out. Moving the operation into Lab and deprojecting the chroma vector removes the spill, and a three-tier mask fallback keeps the pipeline running when a model chokes.

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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, 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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