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1479 points sandslash | 1 comments | | HN request time: 0.345s | source
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whilenot-dev ◴[] No.44320812[source]
I watched Karpathy's Intro to Large Language Models[0] not so long ago and must say that I'm a bit confused by this presentation, and it's a bit unclear to me what it adds.

1,5 years ago he saw all the tool uses in agent systems as the future of LLMs, which seemed reasonable to me. There was (and maybe still is) potential for a lot of business cases to be explored, but every system is defined by its boundaries nonetheless. We still don't know all the challenges we face at that boundaries, whether these could be modelled into a virtual space, handled by software, and therefor also potentially AI and businesses.

Now it all just seems to be analogies and what role LLMs could play in our modern landscape. We should treat LLMs as encapsulated systems of their own ...but sometimes an LLM becomes the operating system, sometimes it's the CPU, sometimes it's the mainframe from the 60s with time-sharing, a big fab complex, or even outright electricity itself?

He's showing an iOS app, which seems to be, sorry for the dismissive tone, an example for a better looking counter. This demo app was in a presentable state for a demo after a day, and it took him a week to implement Googles OAuth2 stuff. Is that somehow exciting? What was that?

The only way I could interpret this is that it just shows a big divide we're currently in. LLMs are a final API product for some, but an unoptimized generative software-model with sophisticated-but-opaque algorithms for others. Both are utterly in need for real world use cases - the product side for the fresh training data, and the business side for insights, integrations and shareholder value.

Am I all of a sudden the one lacking imagination? Is he just slurping the CEO cool aid and still has his investments in OpenAI? Can we at least agree that we're still dealing with software here?

[0]: https://www.youtube.com/watch?v=zjkBMFhNj_g

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Workaccount2 ◴[] No.44321098[source]
The fundamental mistake I see is people applying LLMs to the current paradigm of software; enormous hulking codebases made to have as many features as possible to appeal to as many users as possible.

LLMs are excellent at helping non-programmers write narrow use case, bespoke programs. LLMs don't need to be able to one-shot excel.exe or Plantio.apk so that Christine can easily track when she watered and fed her plants nutrients.

The change that LLMs will bring to computing is much deeper than Garden Software trying to slot in some LLM workers to work on their sprawling feature-pack Plantio SaaS.

I can tell you first hand I have already done this numerous times as a non-programmer working a non-tech job.

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1. skydhash ◴[] No.44322519[source]
The thing is that there’s a need to integrate all these little tools because the problems they solve is part of the same domain. And that’s where problems lie. Something like Excel have an advantage as being a common platform for both data and procedures. Unix adopted text and pipes for integration.