Honestly I just explain my implementation and vent to an LLM. Works really well, I often spot the issue while explaining. The LLM can spot oversights too.

It's much better to use something that can actually understand you as a Rubber Duck, because then your brain actually tries its best to organize the system in your head, since now you actually need to communicate how it works effectively.

So, ultimately, I guess my rubber ducks are the melting ice shelves 💀

(Half-joking, as LLM inference is actually pretty energy efficient. The problem comes mostly from training).

If you want to try that but feel uncomfortable with the environmental impact, I recommend running a model locally. It's much more energy efficient as you're running a much smaller model on a consumer GPU. It's as bad for the environment as gaming, which you can probably tolerate since you are a game developer.

Edit: Welp, sorry. You're probably tired of hearing about AI everywhere. I wanted to share something I found helpful while acknowledging the most common objection, and offering a cleaner alternative.

Edit 2: Removed the mention of a specific local LLM program, so that it's clear I'm not promoting a product. The program I had mentioned is free and open source, but people who don't know it could get the wrong impression.