Hope you're looking for good-faith discussion here. I'll assume that you're looking for a response where someone has taken the time to read through your previous messages and also the linked ChatGPT interaction logs.
What you've shown is actually a great example of the what folks mean that LLMs lack any sort of understanding. They're fundamentally predict-the-next-token machines; they regurgitate and mix parts of their training data in order to satisfy the token prediction loss function they were trained with.
In the linked example you provided, *you* are the one that needs to provide the understanding. It's a rather lengthly back-and-forth to get that code into a somewhat useable state. Importantly, if you didn't tell it to fix things (sqlite connections over threads, etc.), it would have failed.
And while it's concurrent, it's using threads, so it's not going to be doing any work in parallel. The example you have mixes some IO and compute-bound looking operations.
So, if your need was to refactor your original code to _actually be fast_, ChatGPT demonstrated it doesn't understand nearly enough to actually make this happen. This thread conversation got started around correcting the misnomer that an LLM would actually ever be able to possess enough knowledge to do actually valuable, complex refactoring and programming.
While I believe that LLMs can be good tools for a variety of usecases, they have to be used in short bursts. Since their output is fundamentally unreliable, someone always has to read -- then comprehend -- its output. Giving it too much context and then prompting it in such a way to align its next token prediction with a complex outcome is a highly variable and unstable process. If it outputs millions of tokens, how is someone going to actually review all of this?
In my experience using ChatGPT, GPT4, and a few other LLMs, I've found that it's pretty good at coming up with little bits to jog one's own thinking and problem solving. But doing an actual complex task with lots of nuance and semantics-to-be-understood outright? The technology is not quite there yet.
Did you learn anything from that exercise? Are you a better programmer now for having seen that solution? Because if not, this seems like a great way for getting the fabled "one year of experience, twenty times"
What you've shown is actually a great example of the what folks mean that LLMs lack any sort of understanding. They're fundamentally predict-the-next-token machines; they regurgitate and mix parts of their training data in order to satisfy the token prediction loss function they were trained with.
In the linked example you provided, *you* are the one that needs to provide the understanding. It's a rather lengthly back-and-forth to get that code into a somewhat useable state. Importantly, if you didn't tell it to fix things (sqlite connections over threads, etc.), it would have failed.
And while it's concurrent, it's using threads, so it's not going to be doing any work in parallel. The example you have mixes some IO and compute-bound looking operations.
So, if your need was to refactor your original code to _actually be fast_, ChatGPT demonstrated it doesn't understand nearly enough to actually make this happen. This thread conversation got started around correcting the misnomer that an LLM would actually ever be able to possess enough knowledge to do actually valuable, complex refactoring and programming.
While I believe that LLMs can be good tools for a variety of usecases, they have to be used in short bursts. Since their output is fundamentally unreliable, someone always has to read -- then comprehend -- its output. Giving it too much context and then prompting it in such a way to align its next token prediction with a complex outcome is a highly variable and unstable process. If it outputs millions of tokens, how is someone going to actually review all of this?
In my experience using ChatGPT, GPT4, and a few other LLMs, I've found that it's pretty good at coming up with little bits to jog one's own thinking and problem solving. But doing an actual complex task with lots of nuance and semantics-to-be-understood outright? The technology is not quite there yet.