Cursor’s indexer is a RAG pipeline: tree-sitter splits code at logical boundaries, embeddings land in Turbopuffer, and Merkle trees keep the index in sync. Here is how the pieces fit together, and what actually leaves your machine.
WP-Bench is the official WordPress AI benchmark. It runs model-written code inside a real WordPress runtime instead of asking a human whether the code looks right. How to run it, and what it still gets wrong.
Most data science applicants in 2026 are studying the wrong things and applying everywhere at once. Notes from reading hundreds of resumes: what to learn first, how to aim applications, and why referrals do the work.
Prompt engineering has stopped being the interesting part of LLM work. This is about context engineering, caching structured context in transients, and checking that the model’s reasoning holds up.
Zero-shot prompts make the model guess your coding standards. Feeding it two or three working examples from your own code gets output that matches your prefixes, namespaces and security patterns.
Anthropic’s January 2026 report puts the correlation between prompt sophistication and response sophistication at 0.925. Prompt engineering did not die, it moved from magic phrases to knowing your domain well enough to frame the problem.
WordPress.com switched its MCP server to OAuth 2.1 with PKCE, so AI agents no longer need long-lived tokens pasted into a config file. What the code verifier protects against, and what an authorized agent can do on your site.
Low-tech logistics companies do not need AGI, they need to stop counting stock with a pen. Voice-driven inventory counts in n8n, damage reports generated from a photo, and a WordPress REST endpoint that takes the data in without breaking WooCommerce stock.
Perplexity and BLEU tell you nothing about whether an automated customer journey actually goes anywhere. The CDP framework scores continuity, deepening and progression against a taxonomy of journey stages instead.
LLM-as-judge asks a model that hallucinates to catch hallucinations. Displacement Consistency scores the vector between a question and its answer against grounded neighbors instead, with no source document and no second model call at inference time.