[skinner_]

I work on very complex problems. Some of my solutions have small, standard substeps that now I can reliably outsource to ChatGPT. Here are a few just from last week:

- write cvxpy code to find the chromatic number of a graph, and an optimal coloring, given its adjecency matrix.

- given an adjecency matrix write numpy code that enumerates all triangle-free vertex subsets.

- please port this old code from tensorflow to pytorch: ...

- in pytorch, i'd like to code a tensor network defining a 3-tensor of shape (d, d, d). my tensor consists of first projecting all three of its d-dimensional inputs to a k-dimensional vector, typically k=d/10, and then applying a (k, k, k) 3-tensor to contract these to a single number.

All were solved by ChatGPT on the first try.

[lazypenguin]

To be honest, these don’t sound like hard problems. These sound like they have very specific answers that I might find in the more specialized stackoverflow sections. These are also the kind of questions (not in this domain) that I’ve found yield the best results from LLMs.

In comparison asking an LLM a more project specific question “this code has a race condition where is it” while including some code usually is a crapshoot and really depends if you were lucky enough to give it the right context anyway.

[skinner_]

Sure, these are standard problems, I’ve said so myself. My point is that my productivity is multiplied by ChatGPT, even if it can only solve standard problems. This is because, although I work on highly non-standard problems (see https://arxiv.org/abs/2311.10069 for an example), I can break them down into smaller, standard components, which ChatGPT can solve in seconds. I never ask ChatGPT "where's the race condition" kind of questions.

[aprilthird2021]

He doesn't mean complex/ research-level tasks.

He means complex in bridging together many non-public libraries, APIs and services which the LLM doesn't know well. That kind of complexity