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I only use LLMs for coding (and only Phind, since it doesn't require me to jump through any hoops to use it and cites its sources) and I'm completely surprised by both how good they are and how bad they are at the same time.
"Can you do this and that using Spark?" - generates code that does this and that in PySpark cleverly avoiding making an extra dataframe
"Can you rewrite this in Scala Spark?" - generates code that does only that and tells me I have to paste my own code that does this, even though it's the same Spark call
"Can I use A to implement B in C?" - "yes, you can do this, here's how you configure A to do B, here's how you configure C to do B"
"But how exactly do I use A from C?" - "oh, sorry, I meant you can't do this"
Makes me wonder how soon we'll get an LLM that doesn't code like an Accenture presales engineer.
I expect Bing with GPT-4 is better than Phind. It's also free.
When I was learning Python, it was a godsend, not that I can comment on how useful it can be for more complicated projects.
BTW, AlphaCode 2 just launched alongside Gemini, and it represents a massive leap in capabilities, far more impressive in that particular domain.
"Sorry, this service is not available in your region"
And it doesn't like my VPN either.
Well, I guess being in India is good for something.
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Once somebody can figure out a rigid procedure that, when followed, causes Accenture presales engineers to write robust working code that actually meets the criteria, that procedure can be ported to work with LLMs. The procedure in question can be quite expensive with real people, because LLMs are cheap.
I suspect there does exist some approximate solution for the above, but also I expect it'll end up looking like some unholy combination of test-driven development, acceptance testing, mutation testing, and checking that the tests actually test for meeting the business logic requirements (and that last one is probably harder than all the other ones combined). And it will take trying and iterating on thousands of different approaches to find one that works, and the working approach will likely not work in all contexts.
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