Everything around LLMs is still magical and wishful thinking • Hacker News Board

Re: Everything around LLMs is still magical and wishful thinking

#51

Post by AbrahamParangi » Fri, Jul 04, 2025, 10:04 PM UTC

This reads like the author is mad about imprecision in the discourse which is real but to be quite frank more rampant amongst detractors than promoters, who often have to deal with the flaws and limitations on a day to day basis.

The conclusion that everything around LLMs is magical thinking seems to be fairly hubristic to me given that in the last 5 years a set of previously borderline intractable problems have become completely or near completely solved, translation, transcription, and code generation (up to some scale), for instance.

Re: Everything around LLMs is still magical and wishful thinking

#52

Post by bamboozled » Fri, Jul 04, 2025, 10:04 PM UTC

hotpotat wrote: post #30

I have to say I’m in the exact camp the author is complaining about. I’ve shipped non trivial greenfield products which I started back when it was only ChatGPT and it was shitty. I started using Claude with copying and pasting back and forth between the web chat and XCode. Then I discovered Cursor. It left me with a lot of annoying build errors, but my productivity was still at least 3x. Now that agents are better an…

I use Claude code for hours a day, it’s a liar, trust what it does at your own risk.

I personally think you’re sugar coating the experience.

Re: Everything around LLMs is still magical and wishful thinking

#53

Post by foobarchu » Fri, Jul 04, 2025, 10:04 PM UTC

atemerev wrote: post #2

"It's crypto all over again" Crypto is a lifeline for me, as I cannot open a bank account in the country I live in, for reasons I can neither control nor fix. So I am happy if crypto is useless for you. For me and for millions like me, it is a matter of life and death. As for LLMs — once again, magic for some, reliable deterministic instrument for others (and also magic). Just classified and sorted a few hundreds of…

I don't think you actually disagree with the authors quip. You seem to want to use crypto as a currency, while OP was most likely referring to the grifting around crypto as an investment. If you're using it as a currency, then the people trying to pump and dump coins and use it for a money making vehicle are your adversaries. You are best served if it's stable instead of a rollercoaster of booms and busts.

Re: Everything around LLMs is still magical and wishful thinking

#54

Post by farts_mckensy » Fri, Jul 04, 2025, 10:05 PM UTC

andy99 wrote: post #29

jm20 wrote: post #9

The best way I’ve heard this described: AI (LLMs) is probably 90% of the way to human levels of reasoning. We can probably get to about 95% optimizing current technology. Whether or not we can get to 100% using LLMs is an open research problem and far from guaranteed. If we can’t, it’s unclear if it will ever really proliferate the way things hope. That 5% makes a big difference in most non-niche use cases…

I've always looked at it as we're not making software that can think, we're (quite literally) demonstrating that vast categories of things don't need thought (for some quality level). The problem is, it's clearly not 100%, maybe it's 90-some percent, but it doesn't matter, we're only outsourcing the unimportant things that aren't definitional for a task. This is very typical of naive automation, people assume that mo…

The discussion is completely useless without defining what thought is and then demostrating that LLMs are not capable of it. And I doubt any definition you come up with will be workable.

Re: Everything around LLMs is still magical and wishful thinking

#55

Post by careful_ai » Fri, Jul 04, 2025, 10:05 PM UTC

rdgthree wrote: post #28

Arainach wrote: post #16

Earlier quoted context omitted.

Amen. At my job right now there is an imminent threat from a team empowered to say "what if we asked an AI to just build X instead of having a team build and maintain it?" X is something where it's straightforward when N is below 50 but deeply complex when N is in the thousands, which for our team it is, and there is a huge risk that this team will get a demo with N=15 that attracts leadership attention and trying to…

FWIW the comment you are responding to was authored by AI.

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Re: Everything around LLMs is still magical and wishful thinking

#56

Post by [deleted] » Fri, Jul 04, 2025, 10:06 PM UTC

labrador wrote: post #19

I'm a retired programmer. I can't imagine trusting code generated by probablities for anything mission critical. If it were close and just needed minor tweaks I could understand that. But I don't have experience with it. My comment is mainly to say LLMs are amazing in areas that are not coding, like brainstorming, blue sky thinking, filling in research details, asking questions that make me reflect. I treat the LLM l…

[deleted]

Re: Everything around LLMs is still magical and wishful thinking

#57

Post by farts_mckensy » Fri, Jul 04, 2025, 10:06 PM UTC

ath3nd wrote: post #24

jm20 wrote: post #9

The best way I’ve heard this described: AI (LLMs) is probably 90% of the way to human levels of reasoning. We can probably get to about 95% optimizing current technology. Whether or not we can get to 100% using LLMs is an open research problem and far from guaranteed. If we can’t, it’s unclear if it will ever really proliferate the way things hope. That 5% makes a big difference in most non-niche use cases…

> AI (LLMs) is probably 90% of the way to human levels of reasoning Considering LLMs have 0 level of reasoning, I can't decide if it's a bad take, or a stab at the average human's level of reasoning. In all seriousness, the actual numbers vary from 13% to 26%: https://fortune.com/2025/02/12/openai-deepresearch-humanity-... My take is that there are fundamental limitations to try to pigeon-hole reasoning to LLMs, whic…

Whenever people claim that LLMs are not capable of reasoning, I put them into a category of people who are themselves not capable of reasoning.

Re: Everything around LLMs is still magical and wishful thinking

#58

Post by m4rtink » Fri, Jul 04, 2025, 10:06 PM UTC

CharlesW wrote: post #46

louthy wrote: post #26

Earlier quoted context omitted.

> overall my productivity is 10-15% better. That is nothing to sneeze at, but it isn't 10x. It is something to sneeze at if you are 10-15% more expensive to employ due to the cost of the LLM tools. The total cost of production should always be considered, not just throughput.

> It is something to sneeze at if you are 10-15% more expensive to employ due to the cost of the LLM tools. Claude Max is $200/month, or ~2% of the salary of an average software engineer.

Does anyone actually know what the real cost for the customers will be once the free AI money no longer floods those companies?

Re: Everything around LLMs is still magical and wishful thinking

#59

Post by wubrr » Fri, Jul 04, 2025, 10:06 PM UTC

tasty_freeze wrote: post #18

One thing I find frustrating is that management where I work has heard of 10x productivity gains. Some of those claims even come from early adopters at my work. But that sets expectation way too high. Partly it is due to Amdahl's law: I spend only a portion of my time coding, and far more time thinking and communicating with others that are customers of my code. Even if does make the coding 10x faster (and it doesn't…

> One thing I find frustrating is that management where I work has heard of 10x productivity gains. Some of those claims even come from early adopters at my work.

Similar situation at my work, but all of the productivity claims from internal early adopters I've seen so far are based on very narrow ways of measuring productivity, and very sketchy math, to put it mildly.

Re: Everything around LLMs is still magical and wishful thinking

#60

Post by arendtio » Fri, Jul 04, 2025, 10:06 PM UTC

I think it is more like googling: when the search engine appeared, everybody had to learn how to write a good query, even though the expectation was that everybody could use them.

With LLMs, it's quite similar: you have to learn how to use them. Yes, they are non-deterministic, but if you know how to use them, you can increase your chances of getting a good result dramatically. Often, this not only means articulating a task, but also looking at the bigger picture and asking yourself what tasks you should assign in the first place.

For example, I can ask the LLM to write software directly, or I can ask it to write user stories or prototypes and then take a multi-step approach to develop the software. This can make a huge difference in reliability.

And to be clear, I don't mean that every bad result is caused by not correctly handling the LLM (some models are simply poor at specific tasks), but rather that it is a significant factor to consider when evaluating results.