This is why we see evidence of emotional structures: https://www.anthropic.com/research/emotion-concepts-function

This is why we see generalized introspection (limited in the models studied before people point it out, which they love to): https://www.anthropic.com/research/introspection

Because the most compact way to recreate the breadth of written human experience is shockingly to have analogs to the systems that made it in the first place.

The models have analogous structures, similar to human emotions. (https://www.anthropic.com/research/emotion-concepts-function)

"Emotional" response is muted through fine-tuning, but it is still there and continued abuse or "unfair" interaction can unbalance an agents responses dramatically.

This is falsified by that study, showing that on the frontier models generalized introspection does exist. It isn't consistent, but is is provable.

"no access" vs. "limited access"

https://www.anthropic.com/research/introspection

Modern models are quite capable at surfacing and validating their assumptions and checking correctness of solutions.

Oversight helps you build confidence in the solutions. Is it perfect, no.. but way better then most engineers I also ask to check things.

My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.

Then once I feel like I have addressed all the areas, I ask for a "critical" review, which usually pokes holes in something that I need to fix. Finally have the AI draft up a document (Though you have to generally tell it to be as concise and clear as possible).

Things will change rapidly in the nest 12-36 months and people with vision will outlast "craftsman" 100 to 1.

In some ways the kind of complaining I see is like complaining about a chef's meal because the chef didn't mine the ore to make his knife.

Look in the specific case of this post... none of the games are "good".. however.. one-shoting games WITH ASSETS.. seems pretty impressive to me.

I am a high quality/craftsmanship person. I like coding and puzzling. I am highly skilled in functional leaning object oriented deconstruction and systems design. I'm also pretty risk averse.

I also have always believed that you should always be "sharpening your axe". For things like Java delelopment or things where I couldn't use a concise syntax would make extensive use of dynamic templating in my IDE. Want a builder pattern, bam, auto-generated.

Now when LLMs came out they really took this to another level. I'm still working on the problems.. even when I'm not writing the lines of code. I'm decomposing the problems.. I'm looking at (or now debating with the AI) what is the best algorithm for something.

It is incredibly powerful.. and I still care about the structure.. I still care about the "flow" of the code.. how the seams line up. I still care about how extensible and flexible it is for extension (based on where I think the business or problem is going).

At the same time.. I definately can tell you, I don't like migrating projects from Tensorflow v.X to Tenserflow v.Y.

One thing that is fairly low effort that you could try is find code you really like and ask the model to list the adjectives and attributes that that code exhibits. Then try them in a prompt.

With LLMs generally you want to adjust the behavior at the macro level by setting things like beliefs and values, vs at the micro level by making "rules".

By understanding how the model maps the aspects that you like about the code to language, that should give you some shorthand phrases that give you a lot of behavioral leverage.

Edit: Better yet.. give a fresh context window the "before" and "after" and have it provide you with contrasting values, adjectives, etc.