Comment by sevenzero

3 months ago

Also people tend to forget that LLMs still just work on compressed data... Where are the MAJOR breakthroughs? Where is all the "crazy" AI output going? Software seemed to degrade in quality a lot in the recent years. All "improvements" LLMs go through are simply improvements on how to burn more tokens out of my pockets given that Claude now want an actual browser extension to "visually" confirm small changes every time I use it for UI. They are still just data parrots.

10 comments

sevenzero

Reply
vladms  3 months ago

From what I see most benefits are for people that work with LLMs, but usually smaller percentages never 50% or more because of the LLMs (OK, unless you were doing basic, repetitive stuff, but then that's not to write about).

Which kind of answers the original question "why bother working?" with "because now, I can do a bit more than before".

I also see bad quality (in code, documents, presentations). It comes from people that had no clue how to do something before and now they imagine that just asking Claude is solving well the problem. And is annoying (and hard) to explain to it them, and then they get frustrated.

Yopolo  3 months ago

LLM don't work on 'compressed data'. LLM compress data into their latent space which allows them to become general.

They learn the concept of things and how to do them because this is better compression than learning concepts one by one.

Which means, if an LLM 'learns' the concept of a poem, it can put everything into the formad of a poem instead of learning a billion poems.

  • discreteevent  3 months ago

    > They learn the concept of things and how to do them because this is better compression than learning concepts one by one.

    When anthropic looked at how an LLM does addition it found it had some mental math heuristics that might or might not always work. The LLM hadn't learned the concept of addition. It had learned some heuristics that might work for some numbers. The result is that LLM's cannot add numbers reliably because they have not learned the concept of addition.

    • Yopolo  3 months ago

      It learned a concept of a heuristic which made it smart enough for the learning reward.

      Might be an architecture issue or a parameter size issue that it didn't learn to do math like a caculator.

      But look at your own math skills: How many numbers / how big of numbers can you keep in your head? How far is this heuristic away from how much a human learned until you start using pen and paper or a caculator?

ChicagoDave  3 months ago

There’s a small group of established architects talking about harness engineering, but I’m not sure anyone is actually listening to them.

And those same architects are quietly extracting real productivity from GenAI.

And even this write up skips that info by waving, “Some people…”

iammrpayments  3 months ago

The only thing I noticed in some of the software I use is more design changes, but they are often not better than before.