LLM can translate in the style you want them to. You can make them translate more creatively or just translate word by word. I even think you can make them explain their choice of translation and help you proof-read the result.
There's no way to do it traditionally. Your request would need the algorithm to understand the content of the image. Deep learning based image vectorization probably has a similar objective.
First year in collage is probably a rare case. Everyone is in a new environment and everyone's social group is quite limited. They probably know that they're Anna's only social connnection at the campus so the effort can be worthwhile.
> Most of this was uncovered by simply asking ChatGPT directly.
Is the result reliable and not just hallucination? Why would ChatGPT know how itself works and why would it be fed with these kind of learning material?
Yeah, asking LLMs how they works is generally not useful, however asking them about the signatures of the functions available to them (the tools they can call) works pretty well b/c those tools are described in the system prompt in a really detailed way.
I checked has-ansi. What's the reason that this library would exist and be popular? Most of the work is done by the library it imports, ansi-regex and then it just return ansi-regex.test(string), yet it has 5% of the weekly downloads of ansi-regex. ansi-regex also has fewer than 10 lines of code.
I don't know anything about the npm ecosystem, what's the benefit of importing these libraries compared to including these code in the project?
I don't think "simple" here means lack of functions. It means more intuitive and simpler code, and easier curve of learning. And to me f-string is very simple.
