I like these efforts to neatly categorise the extent of AI usage in a project. I do think they need some kind of neutrally worded classification but this and the original post are fine attempts at this emerging niche. It's important to some of us and I look forward to what ends being adopted.
> LLMs still can't write Gleam/Janet/CommonLisp/etc
hoho - I did a 20/80 human/claude project over the long weekend using Janet: https://git.sr.ht/~lsh-0/pj/tree (dead simple Lerna replacement)
... but I otherwise agree with the sentiment. Go code is so simple it scrubs any creative fingerprints anyway. The Clojure/Janet/scheme code I've seen it writing isn't _great_ but it gets the job done quickly and correct enough for me to return to it later and golf it some.
Not at all. I can't stand it either. It's definitely patronising and infantile. I tolerate the silliness, grit my teeth and move on but it wears away at my patience.
Don't all the XBMC/Kodi-likes suffer from having poor support for mainstream content providers. I've looked a few times over the years, and add-ons for Netflix, YouTube, iPlayer, etc. were slow and terrible for discovery. If you knew the exact name of what you wanted to watch they were just about ok, but beyond that they just didn't fit the paradigm of something that was designed for browsing static libraries of content.
Isn't the whole premise of Roku that it does this well with cheap hardware?
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