Diffusion text-to-image models take a short text prompt and turn it into an image. Here are some prompts I've written that worked well:

{"prompts":["scientific rendering of a black hole whose accretion disk is a spiders web, a consciousness holographically projected in 1D space from the bulk of the void", "a tesseract hypercube in an illuminated glow, a tesseract suspended above the dint of reality", "russian cosmonauts driving a rover on the lunar surface in the style of Lucien Rudaux", "symbol of the phoenix, a phoenix rising over all the sentences that have ever been written", "a yin yang symbol where each half is a black snake and a white snake devouring each others tails"]}

Your task is to write 5 more prompts in the way you infer I'd write them from these examples, but based on a combination of subject, style, and setting. For example:

CertSimple just wrote a blog post arguing ES2017's async/await was the best thing to happen with JavaScript. I wholeheartedly agree.

In short, one of the (few?) good things about JavaScript used to be how well it handled asynchronous requests. This was mostly thanks to its Scheme-inherited implementation of functions and closures. That, though, was also one of its worst faults, because it led to the "callback hell", an seemingly unavoidable pattern that made highly asynchronous JS code almost unreadable. Many solutions attempted to solve that, but most failed. Promises almost did it, but failed too. Finally, async/await is here and, combined with Promises, it solves the problem for good. On this post, I'll explain why that is the case and trace a link between promises, async/await, the do-notation and monads.

First, let's illustrate the 3 styles by implementing

日時

2023-12-03

作

voluntas

バージョン

2023.1

url

https://voluntas.github.io

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