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  2. 2023 Reddit API controversy - Wikipedia

    en.wikipedia.org/wiki/2023_Reddit_API_controversy

    Steve Huffman, Reddit's CEO. On April 18, 2023, Reddit announced it would charge for its API service amid a potential initial public offering. [6] Speaking to The New York Times ' Mike Isaac, Reddit CEO Steve Huffman said, "The Reddit corpus of data is really valuable, but we don't need to give all of that value to some of the largest companies in the world for free".

  3. Sherri Papini kidnapping hoax - Wikipedia

    en.wikipedia.org/wiki/Sherri_Papini_kidnapping_hoax

    Sherri's husband, Keith Papini, first became concerned when he returned from his job at Best Buy on November 2, 2016, and could not find his wife at home. He eventually used the "Find My iPhone" application to locate her cell phone and earbuds at the intersection of Sunrise Drive and Old Oregon Trail, about a mile from their home.

  4. Vibrating-sample magnetometer - Wikipedia

    en.wikipedia.org/wiki/Vibrating-sample_magnetometer

    The current is proportional to the magnetization of the sample - the greater the induced current, the greater the magnetization. As a result, typically a hysteresis curve will be recorded [5] and from there the magnetic properties of the sample can be deduced. The idea of vibrating sample came from D. O. Smith's [6] vibrating-coil magnetometer.

  5. AOL Mail offers a free, secure and beautifully designed email experience with standard mailbox features.

  6. PayPal Honey - Wikipedia

    en.wikipedia.org/wiki/PayPal_Honey

    A bug tester leaked the tool to Reddit, where it went viral. [3] By March 2014, the company had 900,000 organic users. [4] Honey raised a $26 million Series C round, led by Anthos Capital in March 2017. [5] By January 2018, Honey raised a total of $40.8 million in venture backing. [6] On January 6, 2020, it was acquired by PayPal for about $4 ...

  7. Sample complexity - Wikipedia

    en.wikipedia.org/wiki/Sample_complexity

    It is equivalent to a model-free brute force search in the state space. In contrast, a high-efficiency algorithm has a low sample complexity. [11] Possible techniques for reducing the sample complexity are metric learning [12] and model-based reinforcement learning. [13]

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