alt Hacker NewsAuthor here: it's not even clear that agents can reliably permute their training data (I'm not saying that it's impossible or never happens but that it's not something we can take for granted as a reliable feature of agentic coding).
As I mentioned in one of the footnotes in the post:
> People often tell me "you would get better results if you generated code in a more mainstream language rather than Haskell" to which I reply: if the agent has difficulty generating Haskell code then that suggests agents aren't capable of reliably generalizing beyond their training data.
If an agent can't consistently apply concepts learned in one language to generate code in another language, then that calls into question how good they are at reliably permuting the training dataset in the way you just suggested.
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> if the agent has difficulty generating Haskell code then that suggests agents aren't capable of reliably generalizing beyond their training data.
doesn't that apply to flesh-and-bone developers? ask someone who's only working in python to implement their current project in haskell and I'm not so sure you'll get very satisfying results.
I am very sceptical mainstream languages will be better. I have seen plenty of bad Python from LLMs. Even with simple CRUD apps and when provided with detailed instructions.
"that suggests agents aren't capable of reliably generalizing beyond their training data."
Yes? If they could, we would have a strong general intelligence by now and only few people are claiming this.
It can also mean that the other programming language is above the cognitive abilities of the LLM
Your argument is far too dependent on observations made about the model's ability with Haskell, which is irrelevant. The concepts in Haskell are totally different to almost any other language - you can't easily "generalize" from an imperative strict language like basically everything people really use to a lazy pure FP language that uses monads for IO like Haskell. The underlying concepts themselves are different and Haskell has never been mainstream enough for models to get good at it.
Pick a good model, let it choose its own tools and then re-evaluate.