The major design decision I'm a little skeptical about is removing variable names; it would be interesting to see empirical data on that as it seems a bit unintuitive. I would expect almost the opposite, that variable names give LLMs some useful local semantics.

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So both things can be true: the more important bottlenecks remain, but progress on discovery work has been very exciting.

I enjoy writing so a system like this would never replace that for me. But for someone who doesn't enjoy writing (or maybe can't generate work that meets their bar in the Ira Glass sense of taste) I think this kind of setup works okay for generating flash even with today's models.

From there you have a second prompt to generate a story that follows those details. You can also generate many candidates and have another model instance rate the stories based on both general literary criteria and how well the fit the prompt, then you only read the best.

This has produced some work I've been reasonably impressed by, though it's not at the level of the best human flash writers.

Also, one easy way to get stuff that completely avoids the "smell" you're talking about by giving specific guidance on style and perspective (e.g., GPT-5 Thinking can do "literary stream-of-consciousness 1st person teenage perspective" reasonably well and will not sound at all like typical model writing).

1.) probably human, low on style but a solid twist (CORRECT) 2.) interesting imagery but some continuity issues, maybe AI (INCORRECT) 3.) more a scene than a story, highly confident is AI given style (CORRECT) 4.) style could go either way, maybe human given some successful characterization (INCORRECT) 5.) I like the style but it's probably AI, the metaphors are too dense and very minor continuity errors (CORRECT) 6.) some genuinely funny stuff and good world building, almost certainly human (CORRECT) 7.) probably AI prompted to go for humor, some minor continuity issues (CORRECT) 8.) nicely subverted expectations, probably human (CORRECT)

My personal ranking for scores (again blind to author) was:

6 (human); 8 (human); 4 (AI); 1 (human) and 5 (AI) -- tied; 2 (human); 3 and 7 (AI) -- tied

So for me the two best stories were human and the two worst were AI. That said, I read a lot of flash fiction, and none of these stories really approached good flash imo. I've also done some of my own experiments, and AI can do much better than what is posted above for flash if given more sophisticated prompting.

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https://en.wikipedia.org/wiki/Publishers_Weekly_list_of_best...

https://en.wikipedia.org/wiki/Publishers_Weekly_list_of_best...

It is true that there isn't that much literary stuff that breaks through, and the stuff that does is usually somewhat crossover (e.g., All the Light We Cannot See in 2015 or Song of Achilles in 2021) but it exists. These two books are shelved under literary codes (though also historical). Song of Achilles in particular is beautifully written and a personal favorite of mine, at least among books published in recent years.

Then there are other works like Little Fires Everywhere and The Midnight Library that I might not consider super literary but nonetheless are also often considered so by book shops or libraries (e.g., https://lightsailed.com/catalog/book/the-midnight-library-a-...; the lit fic code is FIC019000).

I was really surprised that Ferrante's Neapolitan series, the best example (I would have thought) of recent work with both high literary acclaim and popular appeal, did not actually make the top 10 list for any year.

maybe this intuition is wrong but would be great for the work to address it explicitly if so!

I don't have the time or desire to switch all my python/ML work to more conventional Nix, and haven't really had any issues so far.

As far as I can read, the weights of the LLM are not modified. They do some kind of candidate selection via evolutionary algorithms for the LLM prompt, which the LLM then remixes. This process then iterates like a typical evolutionary algorithm.

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I've found creative writing in a target language is great for learning.

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