Abstract: Embodiments of the disclosed technologies include generating a first thread classification prompt based on a first thread portion of an online dialog involving a user of a computing device, sending the first thread classification prompt to a first large language model, receiving a first thread classification generated and output by the first large language model based on the first thread classification prompt, formulating a plan execution prompt based on the first thread classification, sending the plan execution prompt to a second large language model, receiving a second thread portion generated and output by the second large language model based on the plan execution prompt and the online dialog, and generating a label for a third thread portion of the online dialog.

Abstract: Embodiments described herein are capable of providing synthesized and personalized supplemental information to a user. The embodiments describe determining, based on a search result selected by a user, using a LLM, a first set of categories, a first set of keywords, a second set of categories, and a second set of keywords. The embodiments further describe retrieving a set of digital content items. A first digital content item of the set of digital content items is retrieved based on a first category of the first set of categories, and a second digital content item of the set of digital content items is retrieved based on a first category of the second set of categories. The embodiments further describe generating, using a LLM, a summary of one or more digital content items of set of digital content items. The embodiments further describe causing the summary to be displayed on a device.

Abstract: Embodiments of the disclosed technologies include generating a first thread classification prompt based on a first thread portion of an online dialog involving a user of a computing device, sending the first thread classification prompt to a first large language model, receiving a first thread classification generated and output by the first large language model based on the first thread classification prompt, formulating a plan execution prompt based on the first thread classification, sending the plan execution prompt to a second large language model, receiving a second thread portion generated and output by the second large language model based on the plan execution prompt and the online dialog, and generating a label for a third thread portion of the online dialog.

Abstract: Techniques for distributed processing and pre-fetching content using an in-browser neural network model are disclosed herein. In some embodiments, a server transmits a neural network model to a client device, where the neural network model is stored a persistent store of a browser on the client device, and, during a networking session in which the browser on the client device is accessing a page of an online service, the client device predicts at least one link from a plurality of links on the page using the stored neural network model. The client device then fetches content associated with the predicted link(s) from a server of the online service prior to any selection of the predicted link(s) during the networking session.

Abstract: Techniques for distributed processing and pre-fetching content using an in-browser neural network model are disclosed herein. In some embodiments, a server transmits a neural network model to a client device, where the neural network model is stored a persistent store of a browser on the client device, and, during a networking session in which the browser on the client device is accessing a page of an online service, the client device predicts at least one link from a plurality of links on the page using the stored neural network model. The client device then fetches content associated with the predicted link(s) from a server of the online service prior to any selection of the predicted link(s) during the networking session.