As long as the fine is cheaper than the earnings, they'll keep on doing it. And fines are retroactive, at that point they've already broadcasted to and influenced a bunch of people.
The slower models seem fine for home lab usecases such as processing document transcriptions and tagging them, for example. I don’t need that to be live, it can just churn overnight.
I don't use any paid AI or any agents. I just use some of their free interactive question answering interfaces.
There have been some times where I've been doing a project in a language or environment that I do not use a lot, and maybe need to use language or environment features I've never had occasion to learn.
I don't ask AI to write it for me, or even to write any particular functions. I might ask it some syntax questions, or what data structure in the language's standard library is usually used for a particular task. Mostly I use it as an interactive manual that is really good at generating examples if I need clarification on how something works.
If you were working on a similar project and posted asking questions about it, there is a decent chance I could write a useful answer for you.
Suppose the questions would have fairly involved answers, and it might take me 30 minutes to write up something entirely in my own words. That is still going to include knowledge that I got from the AI when I questioned it earlier.
I could also cover the same points, except for parts of it where what I'd be saying is mostly just writing in my own words what the AI taught me (and which I verified is correct before using), so might have to only spend about 4 minutes writing original material, and another minute doing some selected copy/paste from the AI (maybe with some editing).
That 5 minute answer would be exactly as useful in answering your questions as the 30 minute answer.
I don't know you. I have no reason to try to help you other than a general "it is nice to help people" thing.
If you can't be bothered to read it because I didn't spend 500% more time than was necessary to fully answer your questions (time that benefits me in no way whatsoever), why should I bother answering at all?
I feel like "having enough useful context to be jumping in and answering the question in question" and "needing an LLM to shave off 25 minutes of writing on the subject" feel like they're incompatible premises.
I would prefer you give me whatever part of your answer takes you five minutes and then I can work from there on my end, in the method and with the tools I see fit. In other words - give me the prompt.
Totally possible I'm misunderstanding the situation you're presenting, though.
> If you were working on a similar project and posted asking questions about it, there is a decent chance I could write a useful answer for you.
But if you do not know the environment, and use AI to create the answers and phrasing. Then you don't really sound like the authority to help me on that particular subject. Why not refer to the authority on the subject instead, or just send the prompts instead so the person on the other end can do their own processing.
If you don't know the answer to something, you don't need to reply. I get the idea of wanting to help people, and it's a nice sentiment. But it's also good to know where your expertise ends.
And the issue with these large generated blog post type things is that I don't know how well the author actually understands the subject. The LLM might've just gotten the right info and and wrote some confident text surrounding it. But it's framed as if the person wrote it. When they might just passing it off as their own. But I don't read blog posts or seek out forums to converse with LLMs, if I wanted that I would've used the LLM myself. Because if it's not the person's own thoughts, what's even the point of communication.
This isn't really helping to be nice, though. It's outsourcing the hard part in a way that presents as "nice and helpful" and frames yourself as knowledgeable, skilled, experienced, or trustworthy in a way that doesn't match reality.
Suppose I'm trying to explain how a project works and as part of that I need to explain about an algorithm I found in a book, are people going to object if I say "Here's an explanation from <cite to book>" and quote a few paragraphs?
Suppose instead I found that algorithm by describing the task I needed an algorithm for and asking an AI for the most used algorithms for that task. I picked one of its suggestions, read up on it to make sure what the AI said about it is correct, and then I implemented it. If when explaining how the project works to others I say "Here is a good explanation from <cite to AI> on how this algorithm works. I have verified that this is accurate", and quote the AIs explanation how is that different from the book case?
something akin to degrees of separation from the source, considering people have been complaining about this when it was "here's an explanation from google/wikipedia/stackoverflow" long before llms
Yeah, essentially. Just the first google hit without checking if it actually fits the use case. Or just throwing you a insanely long article about a topic. Maybe tell me what section it's in, or briefly tell me why it's relevant.
> "Here is a good explanation from <cite to AI> on how this algorithm works. I have verified that this is accurate", and quote the AIs explanation how is that different from the book case?
I mean this is fine really, as long it's clear to me that you've actually done something with the content. And it's okay when I already know you and know that you actually do the research on the topics the LLM retrieves for you. It's much different when you don't know each other and you just get some generated response. I have no clue if you've digested the information and verified it before giving it to me, or just tossed it over the fence.
The big issue I have with it in this thread is things like blog posts and comments that seem straight up copied from the LLM. At that point the blog seems meaningless to me. It's not the author's work, it's ChatGPT or Claude, so why would I bother reading their prompt's output. I go to a blog for their writing, not the LLM.
Similarly, I go to a concert to hear the artists. I would be very uninterested to a see an empty stage and hearing an AI generated "live version" blasting through the speakers.
> that’s like teachers saying “you won’t always have a calculator
They were right, though. For one, you still need to now what kind of equation to put into the calculator. If you don't know the concepts, what are you going to calculate. Real life situations don't actually spell out neat textbook questions.
And just take a look throughout the day how many quick maths you do subconsciously. Just things like cooking or grocery shopping have tons of moments where you just do the calculation in your head. And you can do that because you've learned how and when to do that.
It's not just a matter of "What if it's inaccessible", but also "Do you want to be dependent on your phone even more". Are just going to walk around with your phone on voice mode narrating your life into your LLM to let it tell you what to do?
They were not. I'm not dead yet so I suppose there's still a chance, but so far in decades of living life I've always had calculators when I've needed them.
> For one, you still need to now what kind of equation to put into the calculator.
That's a very different construct. Math concepts don't care how the arithmetic is performed.
> If you don't know the concepts, what are you going to calculate.
If you don't know the concepts, then what kind of arithmetic are you going to perform with anything -- whether the mind's eye, pencil and paper, a sliderule, or a pocket supercomputer?
I have a calculator (and many other tools, of course) in my pocket at all times, now... and yet I don't pull it out very often. Because I can add $1.99 + $3.49 faster in my head ($2 plus $3.50 minus two cents = $5.48) than I could get the result by pulling out the phone, swiping my access pattern, tapping the calculator app, and punching in the results. Heck, even if I had a dedicated calculator that only required pulling it out and pressing an On button, I'd still come up with the result faster than I could punch the buttons.
It's definitely worth it for kids to invest the time it takes to learn the little shortcuts involved in mental math: that effort gets repaid many times over during the course of a normal human lifetime.
> I reject all notions that these are mutually-exclusive concepts.
Agreed; just because I "rarely" pull out the calculator doesn't mean I never use it. Sometimes my wife asks me "If this cake recipe calls for filling a 9-inch pan with batter, and makes 3 layers, but I have an 8.5-inch pan and I'm making two layers, then how much should I reduce the recipe?" The π terms cancel each other out in that math, but I still can't do (4.25×4.25×2) / (4.5×4.5×3) in my head faster than I can punch it into the calculator. (It comes out to about 0.59, BTW, meaning she should use either half or two-thirds of the recipe depending on if she wants a slightly thinner or thicker cake).
Bringing this back to the broader LLM-related discussion, the lesson I took away from the Fable kerfluffle was "the model will usually, but not always, be available." You might have an Internet connection hiccup, you might have hit your subscription usage credits for the week and not be willing to pay API pricing, the model might even be taken away from you. (Most of which are problems you won't face if you run a local model, but that's a different discussion). So just as it's important to learn mental math even when you have a calculator as backup, it's important to retain the skill to solve many categories of problems without the LLM, even if you still reach for the LLM for the categories of problems that it can solve faster than you can.
I don't think I'd want to rely upon having a rental pocket calculator to use. It could become unavailable at any time, for any reason. For something as common, inexpensive, and easy to find as pocket calculators have been for decades, renting one sounds crazy.
But that's the way that clown-based LLMs are: They're rented. They could disappear at any time.
And sure, I do use the bot to get some stuff done, anyway. I think of it as somewhat akin of the time-sharing computer systems of yore, where people absolutely rented (often far-away) computing resources to accomplish their work.
Those time-sharing systems were only temporary. The giants like Control Data Corp fell out of favor as computers became smaller, faster, and cheaper. (And then they came back in the shape of things like AWS, but that has a lot of non-technical things driving it; AWS is, strictly-speaking, completely optional.)
Unless we stop making computers smaller/faster/cheaper (which we haven't, despite the present-day supply/demand market blip) I think it is certain that we will get there again with LLMs, where the giants fall. Not this year, or next, but small systems will catch up.
And these little machines don't even have to win the race. They just have to be Good Enough.
So it seems inevitable that people like you and I will live to see fantastic local LLMs happening with affordable hardware that fits on a desk.
And then, eventually, into our pockets -- like a calculator.
Given the power draw of the GPUs and RAM needed to run those local LLMs, I don't see them being comfortable to hold in one's pocket anytime soon. :-) Not until some not-yet-imagined breakthrough in heat dissipation is made. But a thin client that talks to the local LLM on your private network, that's already possible today. So yes, within the next 5-10 years I fully expect I'll actually want to use an AI assistant on my phone. Currently I go through the settings and turn everything related to Google Assistant off, and do that again after every Android update. But once it's talking to a model that I control, instead of a model controlled by the world's largest advertising company, I'll feel the opposite way about it.
P.S. If "clown-based LLMs" was an autocorrect-assisted typo for cloud-based, it was inspired. :-) If you typed that on purpose, it was also inspired. Have an upvote.
Power consumption per unit of work keeps going down, too. Our pocket supercomputers are astoundingly efficient compared to what it used to take to get the same work done at relatable points in the past. We'll get there.
Before we get there, we'll have network connectivity to our desktop AI boxes at home. That's pretty good, too.
And if this clown-bot[1] boom is a bubble (as I believe it is), then it's just a matter of time before it pops. The blast radius is unknown, but at one end it seems likely to result in something between potentially-idle fabs that have already been mostly paid for. Idle factories are bad and it makes sense to avoid that even if it is expensive, so this means cheap hardware.
At the other end, it means failed companies with fabs that are sold for pennies on the dollar alongside a resolute unwillingness amongst the investors (who just had their asses handed to them) to start on Boom 2.0. This latter scenario would mean golden age of cheap hardware.
I think we'll be fine.
[1]: Yeah, clown is on purpose. It fits as a replacement in any technical parlance where "cloud" would be used. :)
I'm of the same opinion re: the bubble. The dotcom bubble held on longer than I thought it would (I had been predicting that it would burst in 1999), so I'm not sure I should be making predictions as to when the rental-LLM bubble will burst. But I'm sticking with the hardware I have until memory prices finally fall again. Whether that's only because of spare fab capacity as you mentioned, or because one of the major companies has gone bankrupt and their already-purchased hardware is being sold off at fire-sale prices (perhaps to other companies, who then reduce their new-hardware orders accordingly, which again results in spare fab capacity), I'm going to buy more RAM when it's cheap.
... And I've just been agreeing with you on all points with this comment, haven't I? Time to wrap up the discussion, then: once total agreement is reached then there's not much point in continuing the discussion unless someone has something new to say, and I don't have anything new to say on this topic. See you around.
I feel like you missed the point I was trying to make.
Sure, a teacher framing the necessity of learning math as "you don't always have calculator" is pretty lazy answer that doesn't really get to the point. In my experience, teachers rarely even made this point, students did that tried to argue that Mathematics was pointless when you have calculators.
But my point was that actually knowing these concepts is good for complex reasoning you can do in all aspects of life. Whether you have the calculator with you or not.
The thing with calculator argument that always gets me. I do math unconsciously all the time. Even something as simple as adjusting a recipe when cooking. I don't need to grab a device to do that, I just do the quick math in my head. And the only way I can do that is because I learned how to do math. And even with a calculator, I still needed to know what to calculate. The argument "we don't need math, we have a calculator" assumes you always get a textbook question that lays it out for you.
Same goes for LLMs. I can use them for programming, and they're very convenient. But I still need to know what to ask it and make sure it stays within the confines of what I want. And without my knowledge I would have no clue if what it's trying do is correct, or safe.
Naturally, this assumes a workflow where you do actually look and modify the output yourself. But I'd argue that any non tech person is inevitably going to hit a wall where they can't debug themselves out of without getting a human involved.
> I just do the quick math in my head. And the only way I can do that is because I learned how to do math.
A lot of Hacker news commenters tend to overestimate human ability without education. That is, the tend to believe that people are able to do a lot more without regular training than they actually are. They believe that "math" is some naturally ordained eventuality that humans just do, or likewise that "reasoning" is some immutable natural behavior. In truth these things are "unnatural" human reinforced structures that we have to learn and adapt our brains into.
I'm abhorrent at arithmetic and I think it affects me greatly. I know quite a few people who are barely literate and manage to get by in life, but it obviously makes things hard for them and strips them of opportunities (and they forego holding any identity that seems to "smart" for themselves). I think numeracy is similar.
I wonder how much better I could become as an adult at mental math. I did a math course a few years ago (Math Academy) and put some effort into improving my addition, subtraction, division, and multiplication, but I wish I'd done more up front. Even one-figure arithmetic is so fuzzy to me, like it's a hashtable where the keys don't line up to the right values. Can I unlearn that now? O(1) doesn't matter if the result is wrong.
Underrated and paradoxical element of this, to which, as far as I can tell, LLM boosters offer no solution:
> And without my knowledge I would have no clue if what it's trying do is correct, or safe.
I would contend you got the knowledge by typing the code yourself, that there's no other way to get it, and that if you stop typing the code yourself--and the slogging that entails--you'll lose the ability to prompt LLMs effectively.
It's not that I think the physiokinetic aspects of typing as an input mechanism hold some metaphysical distinction, but rather the level of engagement it forces with the code, and the units in which it does so. I'm not aware as yet of any viable replacement for that.
It's easy to trade on decades of software engineering experience with LLMs: with sufficient experience, everything goes around and comes around, almost any pattern is recognisable, the gratification is immediate, the benefits are now, while the costs and disasters are down the road.
However, the technology world is not static, and if you don't keep up with new frameworks, libraries, languages and other tech in that physical-mechanical "mind-body-keyboard" way that typing--or something substantially close to it--accomplishes, you will lose the ability to navigate that world fluently. To say it's just another abstraction layer and that the world didn't crumble after compilers is to miss something quite essential about how LLMs differ from compilers or high-level languages. The disengagement with the process of physically programming something quite specific will take down with it the ability, over time, to formulate useful prompts and competently review the output.
> I would contend you got the knowledge by typing the code yourself, that there's no other way to get it, and that if you stop typing the code yourself--and the slogging that entails--you'll lose the ability to prompt LLMs effectively.
For sure, and I've tried picking up a new language with the use of LLMs, but the concepts just don't stick because I don't actually do the work. That's why I do try to limit my use of LLMs to fields I'm already closely familiar with, and also keep its output contained to actually reviewable chunks. Or things that are just tedious, like OCR and large text transformations.
> the concepts just don't stick because I don't actually do the work
Yes! And this is the part of the de-skilling puzzle that is completely unaddressed by AI boosters.
Maybe LLMs are a force multiplier, but there still has to be some force to multiply, and I don't think a lot of folks ask the question of how that force is actually cultivated. This nebulous, airy-fairy notion that humans add "architecture" or "taste" doesn't tell the story of how, concretely, they came to have it. It seems to me there is no escaping that it came from typing the code.
Like you, I have a much better grasp of code and API surfaces I've physically typed than things LLMs have emitted and I have reviewed, in a conceptual sense, but which I could not have typed myself then, nor can type now.
I think you are underestimating how fast this stuff is moving. Calling AI "just autocomplete" for system architecture is outdated.
If you were to ask it (especially with Claude's newer model, Fable 5) to draft an architecture design diagram, it would do a better job than any junior dev ever could. Sure, giving context is still necessary, but you can just speak in plain, normal English and it will understand. AI is getting scary good at system design.
> Calling AI "just autocomplete" for system architecture is outdated.
I didn't say that, though.
> it would do a better job than any junior dev ever could. Sure, giving context is still necessary, but you can just speak in plain, normal English and it will understand. AI is getting scary good at system design.
Sure. But that's not my point. Don't you still want those juniors to be capable of creating those diagrams? So they can understand them? If the juniors don't get what the AI is outputting, why are you even creating the diagrams in the first place, right?
That's what this topic is about. Just like how calculators helped us do complex tasks, we still needed to understand math. Same with AI, you still need to be understand the topics you ask it to do.
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