Abstract: Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of using relevant objects to add content to a collaborative repository. The relevant objects are selected from URI-addressable objects based on each relevant object satisfying one or more relevance criteria. The relevant objects are recommended via a user interface. A determination is made that a specified relevant object is selected from the relevant objects. In one example, the content may be added to (e.g., created in) the collaborative repository based on the specified relevant object. In another example, the specified relevant object may be used to represent a new object type in a new content menu. For instance, the new object type may be selectable to cause an object of the new object type to be created in the collaborative repository.

Abstract: Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Abstract: Techniques are described herein that are capable of using relevant objects to add content to a collaborative repository. The relevant objects are selected from URI-addressable objects based on each relevant object satisfying one or more relevance criteria. The relevant objects are recommended via a user interface. A determination is made that a specified relevant object is selected from the relevant objects. In one example, the content may be added to (e.g., created in) the collaborative repository based on the specified relevant object. In another example, the specified relevant object may be used to represent a new object type in a new content menu. For instance, the new object type may be selectable to cause an object of the new object type to be created in the collaborative repository.

Abstract: Aspects of the present disclosure relate to systems and methods for pinning items that span across multiple containers in a collaboration environment. In one aspect, a set of pinned items may be provided based on a container. For example, a first container may comprise a folder having a first metadata driven view that includes a first set of pinnable items. In another example, a second container may comprise the folder having a second metadata driven view that includes a second set of pinnable items. Pinnable items from the first set of pinnable items may be pinned as a tile in a top position within the first metadata driven view. Pinnable items from the second set of pinnable items may be pinned as a tile in a top position within the second metadata driven view.

Abstract: Aspects of the present disclosure relate to systems and methods for creating and progressing files through a workflow using a workflow board. In one aspect, a file repository may be rendered within a file sharing tool. The file repository may include at least a workflow board. A workflow board view associated with a workflow comprising one or more stages may be generated. The generated workflow board view may be displayed as the workflow board. In one example, the workflow board view includes a visual representation of a file status of one or more files progressing through the one or more stages of the workflow.

Abstract: A mount point folder in a collaborative service library may reference contents of a mounted folder that resides elsewhere and includes actual files. File operations may be performed on files inside the mounted folder that may actually live across domains and changes to the contents of either folder may appear in the other. The mount point folder may be represented on the server as a file with .url extensions but in the client user experience, it may be represented as a folder. Mount point folders may be created at a root of a document library or within any other folder of the document library.