You think that AI understands your code deeply, and the purpose and context that exists outside your code?

For all intents and purposes, yes.

Never worked with junior engineers before?

Yes, it does, much more than the person sitting next to you or yourself. I wrote probably 1-2 MLoC in C and C++, and I literally see no reason why would I not be using the AI to produce code when it's already much better than I am, and much better than pretty much anyone I know or have worked with.

An LLM can't have intuition itself.

It can only simulate the outcomes of intuition in its training set.

The likely uncloseable gap is that some intution is unspoken, literally felt by the brain's default mode network.

LLMs basically never shower, do the washing up, kick their foot in the dirt while sitting on a park bench with a coffee: the domain of the default mode network.

I employ a qualia harness that provides these cases to the LLMs I use.

Is that harness connected to a human?

> LLMs basically never shower, do the washing up, kick their foot in the dirt while sitting on a park bench with a coffee: the domain of the default mode network. And they never sit frustrated looking at things, guided by their long experience, with the itchy brain feeling that there is a solution before they can even say the words.

I'm sure the training data of LLMs contains a lot of those things, though.

It contains the averaged written ghosts of the outcomes of those things.

But they are not the things themselves — they are incomplete representations; just words that approximately describe the application of that intution to the problem at hand.

Think about how you visualise complex code projects in your mind — how it's not quite diagrams or buildings or words but multiple things in a sort of multi-dimensional, itchy space that is satisfied when a really good implementation appears. Think about how often the code you have built retains that wordless shape long after you wrote it down. Think about how impossible it is to adequately describe it.

Coming soon: language models trained on 2000 years of tactile, proprioceptive, spatio-temporal, calorific, olfactory, gustational, humidity, temperature and barometric data.

The downside: the model doesn't always have words to describe what it's feeling.

But it knows to blame it on the dog :-)

It may contain writings about these things, not the actual things. That's more like saving a jpg at high compression over and over if you want catchy metaphors.

FWIW, I guess it probably doesn't always matter too much in a tool where you want a logical, correct, standard, robust outcome like agentic coding.

Those outcomes are more easily averaged and we are after all talking about artificial intelligence.

I just think it's important to note, every now and then, that words aren't feelings or thoughts, and that an author's next book cannot be calculated from only the words of their previous books, etc.