Abstract: Systems and methods for classifying user intent based on the user interaction with an entry bar on a user device include receiving an input from a user in an entry box; classifying the input into a category of a plurality of categories including a category for an Artificial Intelligence (AI) session for an AI agent; and performing an action responsive to the classified category, including utilizing the AI agent for the AI session when the category is the category for the AI session and bypassing the AI agent for other categories of the plurality of categories.

Abstract: Systems and methods for classifying user intent based on the user interaction with an entry bar on a user device include receiving an input from a user in an entry box; classifying the input into a category of a plurality of categories including a category for an Artificial Intelligence (AI) session for an AI agent; and performing an action responsive to the classified category, including utilizing the AI agent for the AI session when the category is the category for the AI session and bypassing the AI agent for other categories of the plurality of categories.

Abstract: Systems and methods for Artificial Intelligence (AI) model bundling and splitting for widescale distribution include obtaining an Artificial Intelligence (AI) model having a plurality of layers; producing a plurality of slices, each slice is a layer and an associated encoding of the layer; determining a header and a trailer each defining a previous slice and a next slice, respectively, and optimization coefficients; and serving the plurality of slices and corresponding headers, trailers, and optimization coefficients.

Abstract: Systems and methos for using general-purpose Artificial Intelligence (AI) models as special purpose classifiers include utilizing a prompt for a general-purpose model with the prompt including instructions to perform a classification of an input into one of a plurality of categories; tokenizing the prompt and the input into a plurality tokens including a unique token for each of the plurality of categories; biasing weights of the plurality of tokens such that the unique token for each of the plurality of categories have greater weights than other tokens of the plurality of tokens; and inputting the plurality of tokens with their corresponding weights into the AI model.

Abstract: Systems and methods are provided as an AI-powered web browser companion. A method, according to one implementation, includes a step of locally monitoring user interactions during a web browsing session associated with a client device operating within a browser environment. The method also includes a step of locally determining user interests, user preferences, and behavioral patterns based on the monitored user interactions and further based on previously stored web browsing sessions. Furthermore, the method includes a step of providing personalized content and predictive navigation assistance in the browser environment based on the user interests, user preferences, and behavioral patterns.

Abstract: Systems and methods are provided as a web page management tool. A method, according to one implementation, includes a step of analyzing a plurality of browsing pages that are currently open and active on a Graphical User Interface (GUI), each browsing page related to a user browsing session. The method also includes a step of employing a Machine Learning (ML) model to determine when to close one or more pages of the plurality of browsing pages based on predefined conditions. Before closing the one or more pages, the method also includes a step of archiving metadata and at least a portion of content related to each of the one or more pages to thereby enable a user to retrieve at least a portion of the one or more pages at a later time.