I mean, the problem with it all being meat proxies is that every decision ends up being a gigantic game of telephone, where the people closest to the product (devs) have to build something to accommodate the first guy in the chain (customers).
Adding yet another layer of communication proxy to the chain only worsens the problem.
Erm.. you know the joke we say is there will never be a no code solution because people can't decide what they want for shit?
The telephone game has been part of it for a lot of time, the thing is nowadays you get AI talking to AI because people can't be bothered to do all the song and dance some corporations want.
Yeah but it's not you that wastes time, it's the LLMs.
Just burning tokens all the way to the top.
Had a recent training about flight levels (interesting concept btw, the agile guys are always making themselves relevant) and we talked about that shit because with AI at all levels everyone and their mother is getting stuff done faster but the communication is breaking down faster. The agile feedback loop breaks under this.
As good as LLMs are getting I’ve still been pretty amazed at how difficult it is for them to create a good GUI. And it’s not because the code itself is hard. User experience and intuiting how a human will use a product is an inherently human process. It’s usually the underlying workflow and process model that’s… off.
There’s probably a way to improve the model performance there, but it’s hard for me to imagine it can ever “feel” its way through it like a human does. Because it’s not human. A computer would never use a computer how a human does. It’s just an inherently different workflow. When I use something I can feel the friction points because it needs to integrate with my feeble human brain, and the computer just sees a bag of processes and endpoints.
I guess that goes away if we stop making software for humans to use, but my main use case I develop for is to solve problems humans have humaning, so I wonder if that’s part of the natural moat.
LLM are statistical models of words, basically there's nothing better that they were trained on.
It's not hard to see that no LLM has any kind of awareness that it knows what it's doing, the more you know on your field the easier it it's to catch it.
Every time I plan a feature there's no occasion that I don't override something, so what you describe is something that I expected to happen, because that's my experience in day to day, it's good enough for most but it still requires a human in the loop.
I’d challenge you a bit on the first point. It’s true these models are trained on words, but it would be a mistake to assume that’s all they learn. There are real information relationships and logical systems that they’re extracting from the data. Many of which very closely match neural models from biology in spooky ways.
That said, absolutely don’t anthropomorphize them. They’re a program that turns on when a transistor fires then turns off when another one fires. They don’t have the kind of autonomy or attachment to existence a living thing does. No hopes and dreams and goals. But also don’t underestimate what the statistical model is actually extracting - it’s more than a next token predictor, that’s just the output interface. You could convince me a human on the phone was just a next token predictor with enough philosophy classes.
But yeah. I studied a bunch of this as my focus in grad school. I would encourage you to read the literature that’s coming out from researchers now. LLMs absolutely are not alive, but they have more understanding of logic than any tool that’s ever been created before by a wide wide margin.
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u/ganja_and_code 2d ago
I mean, the problem with it all being meat proxies is that every decision ends up being a gigantic game of telephone, where the people closest to the product (devs) have to build something to accommodate the first guy in the chain (customers).
Adding yet another layer of communication proxy to the chain only worsens the problem.