Amazon Technologies, Inc.

Michael V Zampani

Brett Richard Taylor

Ameet Nirmal Vaswani

Ranganath Atreya

Faizal S Kassamali

Ryan Crawford Tucker

Keith H Anderson

Christopher Heino, Jr

Daniel D Allen

Features are disclosed for detecting meaningful updates to network accessible content, including but not limited to web pages. The portion or portions of content that are meaningful can be automatically determined based on a previously defined content profile, an analysis of user interactions with the content, algorithms and automated content analysis techniques, some combination thereof, or other techniques. Content can be monitored and determinations can be made regarding whether and to what extent the content has changed. Client devices or users thereof may be notified of detected meaningful content updates. Notifications can include updated portions of the content. The updated content may be displayed to the user on a client device, and visual treatments may be applied to the updated portions to draw the users' attention to the presence and substance of the updates.

Automated content update notification

Various features are disclosed for generating and presenting users with resource metadata regarding the usefulness or predicted usefulness of particular pages, sites, and/or other network resources. The metadata may be based partly or wholly on the monitored browsing behaviors of many users, including behaviors reflective of whether particular resources were useful to the users. The metadata may, in some cases, be personalized for particular users based, for example, on their browsing environments or contexts. For example, the metadata presented to a smartphone user regarding a particular link may reflect a determination of whether the target resource is well suited for display on smartphones generally or on the user's particular model of smartphone.

Browser-based provisioning of quality metadata

An embodiment of the disclosed system provides the user of a computing device with information concerning the expected usefulness of an item, such as a hyperlink, within a network resource, such as a search result webpage, with the expected usefulness information based at least in part on an attribute of the user's computing device. For example, the system may provide the user with information identifying a particular website as poorly suited for the user's device, based on data that the system collected identifying an aggregate bounce-back rate from computing devices with a similar attribute to the user's computing device.

Device attribute-customized metadata for browser users

Technologies are described herein for providing an automated reputation system for source code and software development personnel. Code quality metrics regarding code changes associated with a code artifact are automatically generated in response to development lifecycle events in a development environment. The code quality metrics are collected and utilized by a reputation engine to compute a code reputation score for the code artifact. The reputation engine may further utilize the collected code quality metrics to compute personnel reputation scores for one or more development personnel related to the code changes, such as developers, reviewers, testers, and the like. In addition, ratings regarding the code artifact may be received from development personnel in the development environment, and these ratings may be further considered in the computation of the code reputation score for the code artifact and the personnel reputation scores of the related development personnel.

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Seattle, WA, United States of America

Michael V Zampani