It's been ten months since good models started landing and threatening our current job descriptions.

Do you think this is where it stops? This is where it begins.

Machines will be good at managing complexity too. You can't draw a line and say improvement stops here, because everything we've seen so far flies in the face of that.

I shudder to think what these models will be capable of in 24 months.

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preommr 21 hours ago | parent | next [-]

> Do you think this is where it stops? This is where it begins.

No, this is pretty much where it stops.

The models are good enough for the average coding task, and the slop they produce often is in the category of what a bad or careless dev that's being contracted out might produce.

Yea, they'll get better, but not in next-level sort of way.

The limitation is not the models or intelligence, it's the human in the loop. We're still stuck on stupid human issues ranging from usability bugs, to figuring out what the product should be, to how we should program in the first place.

I know the models are capable of sorting out issues it gets stuck on because it's writing error handling in the wrong way, or just it doesn't have the right abstractions, because we can't settle on the right way to program. I still see people arguing about dyanmic vs. static typing.

And obviously, there is a next level, but that's real singularity, and we're all out of jobs.

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therealdrag0 21 hours ago | parent [-]

I think the limitation is still context. Humans are better at judgment than AI because humans have more context to put together better decisions. when AI has all of the context, It beats the socks off me.. for better or worse sharing and organizing context is a notoriously difficult problem that won’t be solved overnight. This could easily be a 10 or 20 year problem because it depends on human organizational structures and investing in free flow of information and information storage tools. Unifying search tools like Glean help a lot here but they’re also expensive.

	▲	dwaltrip 13 hours ago \| parent [-]
		100% agreed. I consider Fable to be a super genius that was born yesterday and has memorized the internet, yet barely understands humans on a deep behavioral level. By the end of a session, sometimes Fable and I are cooking incredibly, but then alas we have to start a fresh session. A lot of the intangibles about what are going well at that point are extremely difficult, if not impossible, to capture compactly in a “handoff” or “guidelines”. The lived experience of working with me through the session, as represented in the current context, is what produces the higher quality results. And likewise, the naivety it has as a newborn at the start of every session, along with the lack of deep human behavioral understanding, explains why these cutting edge models like Fable can still be so dumb in some ways while being mind-blowing in other ways.

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HarHarVeryFunny 19 hours ago | parent | prev [-]

> Machines will be good at managing complexity too.

Sure - eventually.

The question is where is the training data going to come from.

Without "training data" (the non-existent diaries of people designing complex systems and recording their thought processes) you're stuck where we are today, where the LLM is basically doing cargo-cult design and decision making - copying the outcome of decision making (problem looks like X, so I'll use design pattern Y, same as most people do), without understanding why those decisions were made.

How much of the time this matters remains to be seen, as people try to use LLMs to help design more complex bespoke software, rather than just yet another CRUD app or three.js game.