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  2. Filler text - Wikipedia

    en.wikipedia.org/wiki/Filler_text

    Filler text (also placeholder text or dummy text) is text that shares some characteristics of a real written text, but is random or otherwise generated. It may be used to display a sample of fonts , generate text for testing, or to spoof an e-mail spam filter .

  3. Dissociated press - Wikipedia

    en.wikipedia.org/wiki/Dissociated_press

    Dissociated press is a parody generator (a computer program that generates nonsensical text). The generated text is based on another text using the Markov chain technique. The name is a play on "Associated Press" and the psychological term dissociation (although word salad is more typical of conditions like aphasia and schizophrenia – which is, however, frequently confused with dissociative ...

  4. Zalgo text - Wikipedia

    en.wikipedia.org/wiki/Zalgo_text

    The sentence "The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents", in Zalgo textZalgo text is generated by excessively adding various diacritical marks in the form of Unicode combining characters to the letters in a string of digital text. [4]

  5. Postmodernism Generator - Wikipedia

    en.wikipedia.org/wiki/Postmodernism_Generator

    The Postmodernism Generator is a computer program that automatically produces "close imitations" of postmodernist writing. It was written in 1996 by Andrew C. Bulhak of Monash University using the Dada Engine, a system for generating random text from recursive grammars. [1] A free version is also hosted online.

  6. Inside the shady world of celebrity meme coins - AOL

    www.aol.com/news/inside-shady-world-celebrity...

    Sahil Arora has been hustling since he dropped out of high school to launch his own taxi app, Cabby, to challenge Uber. "I'm offering a better business model," he boasted at the time.

  7. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [129] and reweighting training data. [130]

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