58 Matching Annotations
  1. Last 7 days
  2. Feb 2021
    1. Netflix’s NDBench is another open source tool for load testing data stores

      Tools recommendation

    2. mzbench really is the best in class for generating load, and supports a wide variety of protocols

      Tools recommendation

    3. There’s no dearth of open source load testing tools and frameworks, the most popular ones being Apache Bench, Gatling, wrk2, Erlang based Tsung, Siege, Scala based Twitter’s Iago

      Tools recommendation

  3. Jan 2021
    1. Zappos created models to predict customer apparel sizes, which are cached and exposed at runtime via microservices for use in recommendations.

      There is another company named Virtusize who is doing the same thing like size predicting or recommendation

  4. Nov 2020
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  13. Aug 2019
  14. Mar 2017
  15. Aug 2016
    1. Optimizing the digital humanities requires a focus on the digital humanist as an individual; once the individual’s work is facilitated, the pathway is set for greater and broader contributions. For libraries and IT, this is an essential point of convergence: centering on the user’s workflow as a roadmap for developing services and technologies that facilitate all phases of digital humanities research.
    1. we need our DH courses to teach people more than they teach tools. We should structure our curricula not around vague gestures towards collaboration but meaningful practice of it. We should encourage library students to see their work as meaningful and integral (and we should demonstrate this to humanists as well). And we should teach humanists that there are faces behind the tools they learn in class. Not until we model effective relationships in our courses will we be able to produce digital humanities work that is just and equitable.

      Great conclusion.

    2. we should be teaching students resources for working better (both together and alone), rather than what the GUI on different mapping tools looks like.
  16. Jul 2015
    1. Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy.