An average AI code review had doubled in cost to about $2.46, mostly because of model changes and much longer conversations.

They fixed how the system prompt was cached so every repository shares the same saved start, cut the number of model calls, and tested cheaper model and effort settings. Reviews also got faster.

A single agent that held all the knowledge for a data pipeline worked in demos but started making things up in production.

A simple router that does no domain work and sends each job to one of 14 specialist agents, each loading only its own knowledge.

Nvidia's previous server chip, Grace, had slow memory access compared with rival chips.

A close look at Vera's wide memory setup, large shared cache and link between chips. The tests show memory delays stay low even under heavy load.

Giving every ad its own ID means a brand-new ad starts with nothing learned about it.

A tokenizer that turns each ad, product or page into a short set of shared tokens based on its content. A new running shoe ad can then reuse what was learned from similar ones.

Running models on Trainium used to require a separate library and compiler-style code, with fixed shapes and frequent recompiling.

Support for TorchNeuron in Transformers, Diffusers and other libraries, so Trainium works like any other device.