zlacker

>>moored+(OP)
This stuff smells like maybe the bitter lesson isn't fully appreciated.

You might as well just write instructions in English in any old format, as long as it's comprehensible. Exactly as you'd do for human readers! Nothing has really changed about what constitutes good documentation. (Edit to add: my parochialism is showing there, it doesn't have to be English)

Is any of this standardization really needed? Who does it benefit, except the people who enjoy writing specs and establishing standards like this? If it really is a productivity win, it ought to be possible to run a comparison study and prove it. Even then, it might not be worthwhile in the longer run.

>>iainme+Qb
I share your skepticism and think it's the classic pattern playing out, where people map practices of the previous paradigm to the new one and expect it to work.

Aspects of it will be similar but it trends to disruption as it becomes clear the new paradigm just works differently (for both better and worse) and practices need to be rethought accordingly.

I actually suspect the same is true of the entire 'agent' concept, in truth. It seems like a regression in mental model about what is really going on.

We started out with what I think is a more correct one which is simply 'feed tasks to the singular amorphous engine'.

I believe the thrust of agents is anthropomorphism: trying to map the way we think about AI doing tasks to existing structures we comprehend like 'manager' and 'team' and 'specialisation' etc.

Not that it's not effective in cases, but just probably not the right way to think about what is going on, and probably overall counterproductive. Just a limiting abstraction.

When I see for example large consultancies talking about things they are doing in terms of X thousands of agents, I really question what meaning that has in reality and if it's rather just a mechanism to make the idea fundamentally digestable and attractive to consulting service buyers. Billable hours to concrete entities etc.

>>davnic+Ch2
I can see what you're getting at, but think about how humans are a general intelligence and we still ask them to perform specialized jobs. That said, they acquire knowledge in that position rather than being pre-loaded with everything they will ever know (outside of working memory).

>>tehjok+Pz4
yeah I think this is exactly how the analogy breaks down.

As humans we need to specialise. Even though we're generalists and have the a priori potential to learn and do all manner of things we have to pick just a few to focus on to be effective (the beautiful dilemma etc).

I think the basic reason being we're limited by learning time and, relatedly, execution bandwidth of how many things we can reasonably do in a given time period.

LLMs don't have these constraints in the same way. As you say they come preloaded with absolutely everything all at once. There's no or very little marginal time investment per se in learning anything. As for output bandwidth, it also scales horizontally with compute supplied.

So I just think the inherent limitations that make us organise human work around this individual unit working in teams and whatnot don't apply and are counterproductive to apply. There's a real cost to all that stuff that LLMs can just sidestep around, and that's part of the power of the new paradigm that shouldn't be left on the table.