[gjadi]

It's ego won't get in the way but it's lack of intelligence will.

Whereas a junior might be reluctant at first, but if they are smart they will learn and get better.

So maybe LLM are better than not-so-smart people, but you usually try to avoid hiring those people in the first place.

[falcor84]

That's exactly the thing. Claude Code with Opus 4.5 is already significantly better at essentially everything than a large percentage of devs I had the displeasure of working with, including learning when asked to retain a memory. It's still very far from the best devs, but this is the worse it'll ever be, and it already significantly raised the bar for hiring.

[vidarh]

> but this is the worse it'll ever be

And even if the models themselves for some reason were to never get better than what we have now, we've only scratched the surface of harnesses to make them better.

We know a lot about how to make groups of people achieve things individual members never could, and most of the same techiques work for LLMs, but it takes extra work to figure out how to most efficiently work around limitations such as lack of integrated long-term memory.

A lot of that work is in its infancy. E.g. I have a project I'm working on now where I'm up to a couple of dozens of agents, and ever day I'm learning more about how to structure them to squeeze the most out of the models.

One learning that feels relevant to the linked article: Instead of giving an agent the whole task across a large dataset that'd overwhelm context, it often helps to have an agent - that can use Haiku, because it's fine if its dumb - comb the data for <information relevant to the specific task>, and generate a list of information, and have the bigger model use that as a guide.

So the progress we're seeing is not just raw model improvements, but work like the one in this article: Figuring out how to squeeze the best results out of any given model, and that work would continue to yield improvements for years even if models somehow stopped improving.