GPT4 can talk shop, and demonstrate useful code generation and refactoring, as well as understanding.
For instance, as an exercise, I had it write a maze-generating Lisp program that produces ASCII. I wanted it in a relatively little known Lisp dialect, so I described to it some of the features and how they differ from the code it generated.
For instance, GPT4 hypothesized that it has a let which can destructure multiple values: (let ((x1 y1) (function ..)) ((x2 y2) (function ...)) ...).
In plain English, I explained to GPT4 that the target Lisp dialect doesn't have multiple values, but that when we have a pair of values we can return a cons cell (cons a b) instead of (values a b). I also explained to it that we can destructure conses using tree-bind, e.g (tree-bind (a . b) (function ...) ...). And that this only takes one pattern, so it has to be used several times.
GPT4 correctly updated the function to return a cons cell and replaced the flat let with nested tree-bind.
At the end of the chat, when the code was almost working, I made a bugfix to the function which allocates the maze grid, to get it working.
I told GPT4: this version of the grid allocation function makes it work. Without it, the output is almost blank except for the vertical walls flanking the maze left and right. Can you explain why?
GPT4 correctly explained why my function works: that the function it wrote shared a single row vector across the grid rows, giving rise to sharing. It explained it like a computer scientist or seasoned developer would.
It's like a somewhat dim-witted, but otherwise capable coding clerk/technician, which talks smart.
With GPT4, you're Sherlock, and it is Watson so to speak. (Sorry, IBM Watson.) It can speak the language of crime investigation and make some clever inferences. In the end, you do all the real work of designing and debugging, and judging complex requirements against each other in the broader context. It saves you the keystrokes of doing the tedious coding you're used to doing yourself.
On the other hand, you expend some keystrokes explaining yourself. Some of the chat could be saved and used for documentation, because it captures requirements and why some things are done in a certain way and not otherwise (rejected decisions).
For instance, as an exercise, I had it write a maze-generating Lisp program that produces ASCII. I wanted it in a relatively little known Lisp dialect, so I described to it some of the features and how they differ from the code it generated.
For instance, GPT4 hypothesized that it has a let which can destructure multiple values: (let ((x1 y1) (function ..)) ((x2 y2) (function ...)) ...).
In plain English, I explained to GPT4 that the target Lisp dialect doesn't have multiple values, but that when we have a pair of values we can return a cons cell (cons a b) instead of (values a b). I also explained to it that we can destructure conses using tree-bind, e.g (tree-bind (a . b) (function ...) ...). And that this only takes one pattern, so it has to be used several times.
GPT4 correctly updated the function to return a cons cell and replaced the flat let with nested tree-bind.
At the end of the chat, when the code was almost working, I made a bugfix to the function which allocates the maze grid, to get it working.
I told GPT4: this version of the grid allocation function makes it work. Without it, the output is almost blank except for the vertical walls flanking the maze left and right. Can you explain why?
GPT4 correctly explained why my function works: that the function it wrote shared a single row vector across the grid rows, giving rise to sharing. It explained it like a computer scientist or seasoned developer would.
It's like a somewhat dim-witted, but otherwise capable coding clerk/technician, which talks smart.
With GPT4, you're Sherlock, and it is Watson so to speak. (Sorry, IBM Watson.) It can speak the language of crime investigation and make some clever inferences. In the end, you do all the real work of designing and debugging, and judging complex requirements against each other in the broader context. It saves you the keystrokes of doing the tedious coding you're used to doing yourself.
On the other hand, you expend some keystrokes explaining yourself. Some of the chat could be saved and used for documentation, because it captures requirements and why some things are done in a certain way and not otherwise (rejected decisions).