It’s hard to put your finger on exactly how it’s different until you learn how it works. Most algorithmic text generation you see on social media is automated. Twitter bots, for example, use Markov chains, algorithmic tools that analyze what words are most likely to follow others in the source material. The tool then automatically generates new text where words are sorted based on those linguistic probabilities. The user doesn’t have much say in the matter. Brew prefers a more hands-on approach.
“With my program, instead of making a random choice that you don’t get to see, it gives you the top 10 options [for the next word] and lets you choose at each step, in the same way that a predictive-text phone feature does,” Brew said. “It’s not entirely algorithmic.”
In practice, this means that while you have no control over the words that spring forth, your choices can shape how the garden path unfolds. Rather than fully automating the process of assembly of the text by turning it over to a program or bot, Brew’s work is more of a collaboration between a human being and an algorithm.
“It’s not letting the algorithm do all of the work,” said Brew. “It makes it more fun for me to use, I don’t just feel like a programmer making a proof of concept. I still feel like a writer.” (Source)