Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Distributed API accounting is disclosed, including: receiving from respective ones of a plurality of agents, a plurality of local counter values corresponding to an application programming interface (API) service counter identifier; logging the plurality of local counter values corresponding to the API service counter identifier; receiving a request for an aggregated counter value associated with the API service counter identifier; combining at least some of the logged plurality of local counter values associated with the API service counter identifier to determine the aggregated counter value corresponding to the API service counter identifier; and storing the aggregated counter value corresponding to the API service counter identifier.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Distributed API accounting is disclosed, including: receiving from respective ones of a plurality of agents, a plurality of local counter values corresponding to an application programming interface (API) service counter identifier; logging the plurality of local counter values corresponding to the API service counter identifier; receiving a request for an aggregated counter value associated with the API service counter identifier; combining at least some of the logged plurality of local counter values associated with the API service counter identifier to determine the aggregated counter value corresponding to the API service counter identifier; and storing the aggregated counter value corresponding to the API service counter identifier.

Abstract: Distributed API accounting is disclosed, including: receiving from respective ones of a plurality of agents, a plurality of local counter values corresponding to an application programming interface (API) service counter identifier; logging the plurality of local counter values corresponding to the API service counter identifier; receiving a request for an aggregated counter value associated with the API service counter identifier; combining at least some of the logged plurality of local counter values associated with the API service counter identifier to determine the aggregated counter value corresponding to the API service counter identifier; and storing the aggregated counter value corresponding to the API service counter identifier.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: An assisted shipping system is described. Physical specifications of the item to be listed in an online marketplace are determined. Specifications of a shipping material for the item are determined based on the physical specifications of the item. A recommended shipping means is generated based on the specifications of the shipping material and the physical specifications of the item. Historical shipping information for shipped items previously listed on the online marketplace are stored. The physical specifications of the item, the specifications of the shipping material for the item, or the recommended shipping means may be determined based on the historical shipping information.