A price tracker taught me that a hand-built Google scraper dies within two days. Here is why I hand that job to SerpApi now, plus the PHP wrapper I reuse and the latency setting that matters at scale.
A hanging WooCommerce checkout on Black Friday taught me that a nicer spinner does not calm anyone down. Notes on cognitive tunneling, single-tasking flows, transient locks against panic clicks, and what an emergency mode looks like.
A client thought their WooCommerce recommendation engine was broken because accuracy at a 0.5 threshold read 51%. The ranking was fine, the threshold was not. This walks through checking AUC in a spreadsheet with sorted scores and the trapezoidal rule.
A WooCommerce recommendation engine kept timing out the server, and the weights oscillated instead of settling. Swapping the basic update rule for Momentum and then Adam is what fixed it, and this explains why adaptive step sizes matter on messy data.
A client handed me an MVP he had built over a weekend in Cursor. It looked polished until I tried to add one calculation and found no schema underneath any of it. This is the case for settling the data model before you open a coding assistant.
A client once sent me a 50-page persona PDF that said nothing about why people abandon checkout. Functional personas built with AI from support tickets and order notes tell you what a customer is trying to finish and where they stop.
A $5M WooCommerce store shrugged off a 40% checkout drop-off until I stopped showing percentages and started showing session recordings and validation logs. The board approved the fix in ten minutes.
A WooCommerce client’s GPT search assistant was inventing products and discount codes. Prompt engineering did not fix it. Confidence thresholds, source links, and honesty about the limits did.
A client’s AI-built WooCommerce analytics dashboard crashed the moment we filtered by customer lifetime value. Rebuilding it meant sketching the data model and writing a DAL spec before touching any UI.
A client spent six months AI-powering their onboarding flow and ended up with hallucinated shipping dates. The prompt was never the problem, the context was. Here is what UX has to own before the model ships.