Coordinating with people is hard and only gets harder as you live. And actually, finding someone that is earnestly receptive to hearing you pitch your half-baked startup ideas (just an example) and is in some capacity qualified to be at all helpful, is uhhh, not easy.

My understanding was that rubber ducking was using a different portion of your brain by speaking the words.

The same discovery often happens when you explain a problem to a coworker and midway through the explanation you say "nvm, I know what I did wrong"

Sometimes I don't want creativity though, I'm just not familiar enough with the solution space and I use the LLM as a sort of gradient descent simulator to the right solution to my problem (the LLM which itself used gradient descent when trained, meta, I know). I am not looking for wholly new solutions, just one that fits the problem the best, just as one could Google that information but LLMs save even that searching time.

Absolutely, the whole point of the rubber duck is that it's inanimate. The act of talking to the rubber duck makes you first of all describe your problem in words, and secondly hear (or read) it back and reprocess it in a slightly different way. It's a completely free way to use more parts of your brain when you need to.

LLMs are a non-free way for you to make use of less of your brain. It seems to me that these are not the same thing.

Maybe it’s just a semantic distinction, which, sure. I guess I’d just call it research? It’s basically the “I’m reading blogs, repos, issue trackers, api docs etc. to get a feel for the problem space” step of meaningful engineering.

But I definitely reach for a clear and concise way to describe that my brain and fingers are a firewall between the LLM and my code/workspace. I’m using it to help frame my thinking but I’m the one making the decisions. And I’m intentionally keeping context in my brain, not the LLM, by not exposing my workspace to it.

Sometimes people just need something else to tell them their ideas are valid. Validation is a core principle of therapeutic care. Procrastination is tightly linked to fear of a negative outcome. LLMs can help with both of these. They can validate ideas in the now which can help overcome some of that anxiety.

Unfortunately they can also validate some really bad ideas.

I feel I've had the most success with treating it like another developer. One that has specific strengths (reference/checklists/scanning) and weaknesses (big picture/creativity). But definitely bouncing actual questions that I would say to a person off it.

I think that’s an unfair comparison. It can’t even produce Mills and Boon trash.

But can they produce Tom Clancy or James Patterson?

Yes, its context based.

It's like asking a coworker. Providing too little information, or too much context can give different responses.

Try asking the model to not provide it's most common or average answer.

Been using it this way for 2, almost 3 years.

Because AI is not a search engine. It does not return the best search result every time.

What it considers best, is what occurs most often, which can be the most average answers. Unless the service is tuned for search (perplexity, or google itself for example), others will not provide as complete an answer.

How well we ask can make all the difference. It's like asking a coworker. Providing too little information, or too much context can give different responses.

Try asking the model to not provide it's most common or average answer.

Been using it this way for 2, almost 3 years.