He’s playing the game. You have to say AGI is your goal to get attention. It’s just like the YouTube thumbnail game. You can hate it, but you still have to play if you want people to pay attention.
Sometimes we get confused by the difference between technological and scientific progress. When science makes progress it unlocks new S-curves that progress at an incredible pace until you get into the diminishing returns region. People complain of slowing progress but it was always slow, you just didn’t notice that nothing new was happening during the exponential take off of the S-curve, just furious optimization.
And at the same time I have noticed that people don’t understand the difference between an S-curve and an exponential function. They can look almost identical at certain intervals.
As far back as 2017 I copped a lot of flak for suggesting that the coming automation revolution will be great at copying office workers and artists but wont be in order of replacing the whole human race. A lot of the time moores law got thrown back in my face. But thats how this works, we unlock something new, we exploit it as far as possible, the shine wears off and we deal with the aftermath.
That's putting the cart before the horse. Thermodynamics came after the steam engine was made practical. Flight came before aerodynamics. Metallurgy before materials science. Radio before electromagnetic theory took hold. Even LLMs are the result of a lot of tinkering rather than scientific insight. It’s the successful tinkering that creates the puzzle science later formalises.
The "traffic" did not incentivize "publishers" (as they call themselves) to produce any valuable content.. instead, it incentivized them to produce SEO garbage that's not helpful and often deceptive.
I think it's worth being thoughtful about the whole range of folks doing a whole range of stuff out on the Internet. I like this piece about the various incentives that exist:
https://vbuckenham.com/blog/how-to-find-things-online/
I don't know how your comment addresses my comment and the article you mentioned is long. But since you replied, you know what you meant, so could you share your particular argument?
I think connecting this to what V expressed is the value I meant to add here, I think they expressed what they meant with the detail required to express it, and I'm not interested in engaging with the topic on a level that works from summaries alone. Suffice to say that you responded to a comment speaking of how "people are properly incentivized to produce content" by saying '"traffic" did not incentivize "publishers" (as they call themselves) to produce any valuable content' as though traffic didn't have to do with the people out there publishing valuable stuff on the internet today, or as though such people don't exist. V's thing considers the interaction among these people, their incentives, and LLMs.
> as though traffic didn't have to do with the people out there publishing valuable stuff on the internet today, or as though such people don't exist
Even if there are people, today, that do publish valuable stuff on the internet only because (or thanks to) the expected traffic (either own ads or selling ad space), I claim that without that traffic, there will be others who will be publishing valuable stuff, and the traffic incentivized garbage more than the valuable stuff.
Finally, if that's not the case, then in order for the LLM to be useful, it needs valuable training data. So this problem solves itself - competitive LLM company will need to pay for the data directly, and for me, that model incentivizes valuable stuff more than garbage.
If it’s easy enough to produce creative “content” thanks to AI, and people have enough money and free time, they’ll create without being paid (for themselves, social status, social influence, scientific advancement, etc.)
In the meantime, I think people should focus on attribution, and algorithms to find related work (which may suitably substitute for the former). This will allow us to fund creators and publishers for AI output, maybe by forcing the AI companies, or naturally through patronage (see AI output you like, find who owns the training data that contributed to it, donate to them). Moreover, it will help people discover more interesting things and creators.
I hope they can figure out why these give some people headaches and eye strain (like myself) I really want to use this, but can't stand the pain for more than a few minutes.
For normal VR/AR, definitely, since you want to have objects moving in the Z direction. For this usecase it should be enough to show the "flat" virtual screen at the focal distance.
This article captures a lot of the problem. It’s often frustrating how it tries to work around really simple issues with complex workarounds that don’t work at all. I tell it the secret simple thing it’s missing and it gets it. It always makes me think, god help the vibe coders that can’t read code. I actually feel bad for them.
> I tell it the secret simple thing it’s missing and it gets it.
Anthropomorphizing LLMs is not helpful. It doesn't get anything, you just gave it new tokens, ones which are more closely correlated with the correct answer. It also generates responses similar to what a human would say in the same situation.
Note i first wrote "it also mimicks what a human would say", then I realized I am anthropomorphizing a statistical algorithm and had to correct myself. It's hard sometimes but language shapes how we think (which is ironically why LLMs are a thing at all) and using terms which better describe how it really works is important.
Given that LLMs are trained on humans, who don't respond well to being dehumanised, I expect anthropomorphising them to be better than the opposite of that.
Aside from just getting more useful responses back, I think it's just bad for your brain to treat something that acts like a person with disrespect. Becomes "it's just a chatbot", "It's just a dog", "It's just a low level customer support worker".
While I also agree with you on that, there are also prompts that make them not act like a person at all, and prompts can be write-once-use-many which lessens the impact of that.
This is why I tend to lead with the "quality of response" argument rather than the "user's own mind" argument.
I am not talking about getting it to generate useful output, treating it extra politely or threatening with fines seems to give better results sometimes so why not, I am talking about the phrase "gets it". It does not get anything.
It's a feature of language to describe things in those terms even if they aren't accurate.
>using terms which better describe how it really works is important
Sometimes, especially if you doing something where that matters, but abstracting those details away is also useful when trying to communicate clearly in other contexts.
Working as an instructor for a project course for first-year university students, I have run in to this a couple of times. The code required for the project is pretty simple, but there are a couple of subtle details that can go wrong. Had one group today with bit shifts and other "advanced" operators everywhere, but the code was not working as expected. I asked them to just `Serial.println()` so they could check what was going on, and they were stumped. LLMs are already great tools, but if you don't know basic troubleshooting/debugging you're in for a bad time when the brick wall arrives.
On the other hand, it shows how much coding is just repetition. You don't need to be a good coder to perform serviceable work, but you won't create anything new and amazing either, if you don't learn to think and reason - but that might for some purposes be fine. (Worrying for the ability of the general population however)
You could ask whether these students would have gotten anything done without generated code? Probably, it's just a momentarily easier alternative to actual understanding. They did however realise the problem and decided by themselves to write their own code in a simpler, more repetitive and "stupid" style, but one that they could reason about. So hopefully a good lesson and all well in the end!
Sounds like you found a good problem for the students. Having the experience of failing to get the right answer out of the tool and then succeeding on your whits creates an opportunity to learn these tools benefit from disciplined usage.
There's a pretty big gap between "make it work" and "make it good".
I've found with LLMs I can usually convince them to get me at least something that mostly works, but each step compounds with excessive amounts of extra code, extraneous comments ("This loop goes through each..."), and redundant functions.
In the short term it feels good to achieve something 'quickly', but there's a lot of debt associated with running a random number generator on your codebase.
In my opinion, the difference between good code and code that simply works (sometimes barely); is that good code will still work (or error out gracefully) when the state and the inputs are not as expected.
Good programs are written by people who anticipate what might go wrong. If the document says 'don't do X'; they know a tester is likely to try X because a user will eventually do it.
I prefer just paying for metered use on every request. I hope monthly fees don’t carry over from the last era of tech. It’s fine to charge consumers $10 per month. But once it’s over $50 let’s not pretend you are hoping I under utilize the service, and you want me to think I’m over utilizing it. These premium subscriptions are too much for me to pretend that math doesn’t exist.
Sort of, but in a good way, if I’ve spent $15 on a problem and it’s not solved, it reminds me to stop wasting tokens and think of a better strategy. On net it makes me use less tokens, but more for efficiency. I mostly love that I don’t need to periodically do math on a subscription to see if I’m getting a good deal this month.
Yes, and thats why phone contracts migrated from "$0.0X per minute" to "$X for up to 500 minutes", and finally "$X for unlimited calls".
When the service you provide has near zero marginal cost, you'd prefer the customer use it as much as possible, because then it'll provide more value to them and they'll be prepared to pay more.
Back when I used dial-up, I experienced a lot of stress when I was connected. I felt I had to be as effective as possible, because we had to pay for every minute spent.
When I switched to DSL the stress went away, and I found myself using internet in different ways than before, because I could explore freely without time pressure.
I think this applies to Claude as well. I will probably feel more free to experiment if I don't have to worry about costs. I might do things I would never think of if I'm only focused on using it as little as possible to save money.
My first use of the internet was dial-up e-mail only exchange via UUCP to a local BBS that exchanged mail every 6 hours (might have been 4), and so to be as effective as possible, I'd prepare all my e-mails including mails to the e-mail<->web gateway at CERN so I could exchange a big batch right before the time slot. Often their exchange took long enough that if I sent the messages to the CERN bot first, I'd get the response included when I downloaded the replies after they'd exchanged with their upstream. Then I had a 6 hour window to figure out what to include in the next batch...
100% with you that how you access something can add constraints and stress - in my case there while we paid per minute, the big factor was the time windows. To maximise utility you wanted to include something useful in as many of the exchanges as possible.
With Claude Code as it is now, I often clear context more often than ideal because it will drive up cost. I could probably add a lot more details to CLAUDE.md in my repos, but it'll drive up tokens as well.
Some of it I'll still do because it affects speed as well, but it'll be nice not to have to pay attention to it.
It's great that there's a choice, but for me the Max plan is likely to save me money already, and I suspect my usage would increase significantly if the top-up (I have intentionally not set it to auto-top-up) didn't regularly remind me not to go nuts.
I wouldn’t beat yourself up over it. Very few papers can be understood without reading a significant amount of the neighboring literature and the history of how that work came to be. There are norms and customs and a kind of academic language in every community that you won’t be able to see unless you’ve read a lot from that community. Even if you have the right math level it’s tricky.
A single paper is part of a conversation, not something that stands alone. Trying to read one random paper is like finding a 1000 page thread on an obscure topic that has been running for 10+ years and reading only the last page. It won’t make any sense without reading back a ways.
If you pretend/imagine it was intentional, and insightful, you've created a nerd trap for amateur ontologists. Some of which decide to become professional ontologists and sell books on objected oriented design.
reply