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Vibe coding as a coding veteran: from 8-bit assembly to English-as-code

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181 points thunderbong | 1 comments | 28 Aug 25 15:55 UTC | HN request time: 0.2s | source
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mycentstoo ◴[31 Aug 25 13:57 UTC] No.45083181[source]▶
I believe choosing a well known problem space in a well known language certainly influenced a lot of the behavior. AIs usefulness is correlated strongly with its training data and there’s no doubt been a significant amount of data about both the problem space and Python.

I’d love to see how this compares when either the problem space is different or the language/ecosystem is different.

It was a great read regardless!

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Insanity ◴[31 Aug 25 14:17 UTC] No.45083320[source]▶
100% this. I tried haskelling with LLMs and it’s performance is worse compared to Go.

Although in fairness this was a year ago on GPT 3.5 IIRC

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1. danielbln ◴[31 Aug 25 14:30 UTC] No.45083408[source]▶
Post-training in all frontier models has improved significantly wrt to programming language support. Take Elexir, which LLMs could barely handle a test ago, but now support has gotten really good