alt Hacker NewsThe second example has nothing to do with the first. I am optimistic that LLMs are great for translations with good testing frameworks.
“Optimize” in a vacuum is a tarpit for an LLM agent today, in my view. The Google case is interesting but 1% while significant at Google scale doesn’t move the needle much in terms of statistical significance. It would be more interesting to see the exact operation and the speed up achieved relative to the prior version. But it’s data contrary to my view for sure. The cynic also notes that Google is in the LLM hype game now, too.
Why do you think it's not relevant to the "optimise in a loop" thing? The way I think of it, it's using LLMs "in a loop" to move something from arch A (that costs x$) to arch B (that costs y$), where y is cheaper than x. It's still an autonomous optimisation done by LLMs, no?