i learned this industry has lower morals and standards for excellence than i ever previously expected
Things we learned about LLMs in 2024
281–290 of 615 posts
Re: Things we learned about LLMs in 2024
#282Earlier quoted context omitted.
Like all AI simps, your blanket response to pointing out flaws is to tell me to do more prompt engineering and then dismiss the issue entirely. In the time it takes me to coax the model to do the thing I was told it knows how to do, I could just do the task myself. Your examples of LLM code generation are simple, easy to specify, self-contained applications that are not representative of software you can actually bui…
Like all stubborn anti-AI know-it-alls, you sound like you’ve tried a couple of times to do something and have decided to label all LLMs with the same brush. What models have you tried, and what are you trying to do with them? Give us an example prompt too so we can see how you’re coaxing it so we can rule out skill issue. And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 1…
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Re: Things we learned about LLMs in 2024
#283Earlier quoted context omitted.
If effect, kind of. The money being invested does not go directly to investors. It goes to the cost of R&D, which in turn increases the value of openai shares, then the early investors can sell those shares to realize those gains. The difference between that and a ponzi is that the investment creates value which is reflected in the share price. No value is created in a Ponzi scheme. The actual dollar worth of the val…
Only a part of the value is created in OpenAI's stock valuation. Most of it is still a ponzi-like scheme.
I have no love for openai, but they did make the fastest growing product of all time. There’s value in being the ones to do that.
I do agree it’s a very very thin line.
Re: Things we learned about LLMs in 2024
#284About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…
Both new Sonnet and Haiku have a masking overhead.
Using a few messages to get them out of "I aim to be direct" AI assistant mode gets much better overall results for the rest of the chat.
Haiku is actually incredibly good at high level systems thinking. Somehow when they moved to a smaller model the "human-like" parts fell away but the logical parts remained at a similar level.
Like if you were taking meeting notes from a business strategy meeting and wanted insights, use Haiku over Sonnet, and thank me later.
Re: Things we learned about LLMs in 2024
#285Earlier quoted context omitted.
Like all AI simps, your blanket response to pointing out flaws is to tell me to do more prompt engineering and then dismiss the issue entirely. In the time it takes me to coax the model to do the thing I was told it knows how to do, I could just do the task myself. Your examples of LLM code generation are simple, easy to specify, self-contained applications that are not representative of software you can actually bui…
Like all stubborn anti-AI know-it-alls, you sound like you’ve tried a couple of times to do something and have decided to label all LLMs with the same brush. What models have you tried, and what are you trying to do with them? Give us an example prompt too so we can see how you’re coaxing it so we can rule out skill issue. And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 1…
> And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 10 arxiv papers relating to prompt engineering and produce a report geared towards non-techies. And do this every 30 mins please. Also produce social media threads with that info. Is this a task you could do yourself, better than LLMs?
Right, but this is the part that is silly and sort of disingenuous and I think built upon a weird understanding of value and productivity.
Doing more constantly isn't inherently valuable. If one human writes a magnificently crafted summary of those papers once and it is promulgated across channels effectively, this is both better and more economical than having an LLM compute one (slightly incorrect) summary for each individual on demand. In fact, all the LLM does in this case is increase the amount of possible lower quality noise in the space. The one edge an LLM might have at this stage is to generate a summary that accounts for more recent information, thereby getting around the inevitable gradual "out of dateness" of human authored summaries at time T, but even then, this is not great if the trade off is to pollute the space with a. bunch of ever so slightly different variants of the same text. It's such a weird, warped idea of what productivity is, it's basically the lazy middle-manager's idea of what it means to be productive. We need to remember that not all processes are reducible to their outputs—sometimes the process is the point, not the immediate output (e.g. education).
Re: Things we learned about LLMs in 2024
#286Earlier quoted context omitted.
Why do people have such narrow views on what makes LLMs useful? I use them for basically everything. My son throwing an irrational tantrum at the amusement park and I can't figure out why he's like that (he won't tell me or he doesn't know himself either) or what I should do? I feed Claude all the facts of what happened that day and ask for advice. Even if I don't agree with the advice, at the very least the analysis…
At the risk of sounding impolite or critical of your personal choices: this, right here, is the problem! You don’t understand how medicine works, at any level. Yet you turn to a machine for advice, and take it at face value . I say these things confidently, because I do understand medicine well enough to not to seek my own answers. Recently I went to a doctor for a serious condition and every notion I had was wrong.…
No. Where do you read that I take it at face value? I literally said that I expect Claude to give me "half-assed" medical guidance. I merely said that that is still better than having no clue for the next 2 hours while I wait on the phone with 35 people in front of me, which is completely different from "taking medicine advice at face value". It's not like I will let my wife drink bleach just because Claude told me to. But if it tells me that it's likely an ear infection then at least I can discuss the possibility with the doctor.
So I am curious about how TCM works. So what if an LLM hallucinates there? I am not writing papers on TCM or advising governments on TCM policy. I still follow the doctor's instructions at the end of the day.
For anything really critical I already double check with professionals. As you said, human in the loop is important. But needing human in the loop does not make it useless.
You are letting perfect be the enemy of good. A half-assed tax advice with some hallucinations from an LLM is still useful, because it will prime me with a basic mental model. When I later double check the whole thing with a professional, I will already know what questions to ask and what direction I need to explore, which saves time and money compared to going in with a blank slate.
The other day I had Claude advice me on how to write a letter to a judge to fight a traffic fine. We discuss what arguments to make, from what perspective a judge will see things, and thus what I should plead for. The traffic fine is a few hundred euros: a significant amount, but barely an hour worth of a real lawyer's fee. It makes absolutely no sense to hire a real lawyer here. If this fails, the worst thing that can happen is that I won't get my traffic fine reimbursed.
There is absolutely nothing wrong with using LLMs when you know their limits and how to mitigate them.
So what if every notion you learned about medicine from LLMs is wrong? You learn why they're wrong, then next time you prompt/double check better, until you learn how to use it for that field in the least hallucinationatory way. Your experience also doesn't match mine: the advice I get usually contains useful elements that I then discuss with doctors. Plus, doctors can make mistakes too, and they can fail to consider some things. Twitter is full of stories about doctors who failed to diagnose something but ChatGPT got it right.
Stop letting perfect be the enemy of good. Occasionally needing human in the loop is completely fine.
Re: Things we learned about LLMs in 2024
#287Earlier quoted context omitted.
Right, in simpler terms: The measure of LLMs success is how effectively they help you achieve your goal faster.
Exactly, and right now the LLMs acceleration effect is a tool , not "give me the final solution". Even people that can't code, using LLMs to build applications from scratch, still have this tool mindset. This is why they can use them effectively: they don't stop at the first failed solution; they provide hints to the LLM, test the code, try to figure what's the problem (also with the LLM help), and so forth. It's a m…
btw, fusion has arrived by that definition: No reactors that would produce more energy than they consume, But net positive reactions have been achieved. Tasks where LLMs output is more than 1x are few and far between.
Re: Things we learned about LLMs in 2024
#288Earlier quoted context omitted.
I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…
Did you need a Mac for that, or is it possible to use Linux to develop a Swift app targeting iOS? Would you mind sharing which app you released?
You need macOS, which you can run in a VM (e.g. https://github.com/kholia/OSX-KVM ) or by setting up a hackintosh.
Re: Things we learned about LLMs in 2024
#289Look, when are these models going to not just talk to me, but do stuff for me? If they're so clever, why can't I tell one to buy chocolates and send them to my wife? Meanwhile, they can allegedly solve frontier maths problems. What's the holdup to models that go online and perform simple tasks?
LLM's are inherently untrustworthy. They're very good at some tasks, but they still need to be checked and/or constrained carefully, which is probably not the best technology on which to base real-world autonomous agents.
Re: Things we learned about LLMs in 2024
#290Earlier quoted context omitted.
Like all AI simps, your blanket response to pointing out flaws is to tell me to do more prompt engineering and then dismiss the issue entirely. In the time it takes me to coax the model to do the thing I was told it knows how to do, I could just do the task myself. Your examples of LLM code generation are simple, easy to specify, self-contained applications that are not representative of software you can actually bui…
Like all stubborn anti-AI know-it-alls, you sound like you’ve tried a couple of times to do something and have decided to label all LLMs with the same brush. What models have you tried, and what are you trying to do with them? Give us an example prompt too so we can see how you’re coaxing it so we can rule out skill issue. And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 1…
> And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 10 arxiv papers relating to prompt engineering and produce a report geared towards non-techies. And do this every 30 mins please. Also produce social media threads with that info. Is this a task you could do yourself, better than LLMs?
I don't mean to nitpick, but how good do you really think the output of this would be? Papers are short and usually have many references, I would expect the LLM to basically miss the important subtleties on every paper it's given, and misunderstand and misattribute any terms of art it encounters.
I mean, of course LLMs are good at summarizing: the summaries are probably mostly sort of good, and anything I'm summarizing I won't read myself. But for technical and specific texts, what's the point when you're getting a "maybe correct" retelling? Best case scenario you get a pretty paragraph that's maybe good for an introduction, and worst case you get incorrect information that misinforms you.