Jonathan Gu

Every store has a row that says “You may also like.” Ours form a view of the shopper on the first visit and revise it after the next product they look at. A substitute and a complement are different questions, so each gets its own model, and what a shopper has already viewed is something the model weighs rather than a rule it obeys.

Search results reordered by what the shopper meant rather than the exact words typed, learned from how people search and what they then buy.

Collection pages and homepages, put in a different order for each visitor. One product view is enough for the grid to reconsider.

Events, features, training, evaluation, publishing and serving, in one place. A model learns the goal of the experiment it serves, which is usually revenue per visit. A client’s request becomes a working model within hours, and the model retrains every day.

Every model goes live as an experiment against whatever was on the page before, including established vendors in their own slots. A new model replaces the old one only when it is at least as good, and a gain that sits inside the noise does not count. It must also beat the best simple rule, which is more humbling than you might expect.

Every test, won or lost, teaches an engine that proposes the next ideas for every client. Agents build the ideas, and people decide which of them deserve a store’s visitors.

Each client’s request becomes a record that says what to show, what to learn from and how success will be judged, with custom code only where the record cannot say it. A short check proves that the data exists before any training is paid for. Clients are walled off from one another. A new model answers quietly beside the old one before anyone sees it, and the way back is written down before the switch.

I build all of this with a company of AI agents. One plans the work, another audits the plan, and builders take the rows. A grader from a different family of models checks what they produce, on the reasonable theory that nobody should mark their own homework. A piece of work is finished when a script prints PASS, not when an agent says so, and the person who owns a launch decides when it launches.