The Python coding dojo is back and this time it allows AI assisted coding which means that some of the standard katas become trivial and instead the challenges have to be different to either require different problems to be combined in an interesting way or have a very hard problem that doesn’t have a standard solution.

The team I was in worked on converting image files to ASCII art (with a secondary goal of trying to create an image that would work with the character limit of old-school Twitter).

We used ChatGPT and ran the code in Jupyter notebooks. To be honest ChatGPT one-shotted the answer, clearly this is a thing that has many implementations. Much of the solution was as you would expect, reading the image and converting it to greyscale. The magic code is this line (this is regenerated from Mistral rather than the original version).

ascii_chars = "@%#*+=-:. "

This string is used to map the value of each pixel to a character. It is really the key to a good solution in terms of the representation of the image and also when we tried to refine the solution to add more characters this was the bit of the code that went wrong as the generated code tends not to understand that the pixel mapping depends on the length of this string. A couple of versions of the code had an indexing issue as it kept the original mapping calculation but changed the size of the string.

On the one hand the experience was massively deflating, we were probably done in 15 or 20 minutes. Some of the team hadn’t used code assistance this way before so they got something out of it. Overall though I’m not sure what kind of learning experience we were having and whether the dojo format really helps build learning if you allow AI-assistance.

If the problems become harder to allow for the fact that anything trivial is already in the AI databank then the step up into understanding the problem as well as the output is going to be difficult for beginners.

There’s lots to think about here and I’m not sure there are any easy answers.