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379 points rbanffy | 7 comments | | HN request time: 0.788s | source | bottom
1. mentos ◴[] No.46215516[source]
Given that Python tends to produce fewer hallucinations when generated by LLMs I wonder if former Django developers using AI tools are secretly having a blast right now.
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2. tirpen ◴[] No.46215588[source]
I think another ace up Django's sleeve is that it has had a remarkable stable API for a long time with very few breaking changes, so almost all blogposts about Django that the LLM has gobbled up will still be mostly correct whether they are a year or a decade old.

I get remarkably good and correct LLM output for Django projects compared to what I get in project with more fast moving and frequently API breaking frameworks.

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3. boxed ◴[] No.46215786[source]
If Python produces less hallucinations it's not because of the syntax, it's because there's so much training data.
4. Genego ◴[] No.46216131[source]
Whenever I saw people complain about LLMs writing code, I never really understood why they were so adamant that it just didn’t work at all for them. The moment I did try to use LLMs outside of Django, it became clear that some frameworks are just much easier to work with LLMs than others. I immediately understood their frustrations.
5. m_ke ◴[] No.46216252[source]
What a lot of people don’t know is that SWE-bench is over 50% Django code, so all of the top labs hyper optimize to perform well on it.
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6. vanschelven ◴[] No.46216790[source]
The "one way" / "batteries included" aspect of Django may also make it easier for LLMs
7. kristianp ◴[] No.46223642[source]
I know python is more prevalent in SWE-Bench than any other language, but more than 50% django sounds like a big stretch. Citation?

Edit, it's about 37%, and python-only. https://arxiv.org/pdf/2310.06770v3