zlacker

>>tablet+(OP)
I find the Konwinski Prize to be very interesting in this context. 1 million dollars to whoever's open source LLM solves >90% of a set of novel Github issues.

https://www.kaggle.com/competitions/konwinski-prize/

Currently, the #1 spot sits at a score of 0.09, not 0.9. A far cry from being useful. I know that open source models are not as good as closed source, but still, we're a long way from LLMs being good for code on their own.

And that supports OP's point - these tools aren't AGI, they produce trash that needs evaluation, but they're still useful.

>>a_bono+vq
They’re tab-completion with extra cognitive-load steps.

>>jachee+ft
I mean, if you can solve 9% of Github issues automatically that's a fairly huge load of work you can automate. Then again you'd have to manually identify which 9% of issues.

>>a_bono+zu
"update dependencies"

that would probably cover it, and you don't need "AI" to do that