I think the point is that line endings are a really, really stupid hill for either Linux or Windows to die on. The day when any programs should have cared about line endings came and went decades ago.
Most of the economy is not journalists or people who sell "content" online. In most cases I can think of - retailer, restaurant, hotel, plumber, any local small business, they want their content ingested. That means the AI chatbot knows about them and they can be in answers potentially.
Neat. Not sure if your site is a gold mine inside a rabbit hole, or a rabbit hole inside a gold mine, but I really dig both the aesthetic and the content.
No, no, you don't get it. See, it's just like a text search, if a text search could return text that never existed before, and that solves original math problems, and answer your emails for you, ... and ...
But most of the stuff it returns existed before, and llm's parent company during their training part stole all that info, legal or not who cares right. The rest is combined in sort of least-resistance-path which can produce impressive results but its not what you wrote. Many people don't actually care much about morality in their lives only when its convenient for them, and this is a prime example of such tunnel vision.
Start with clean llm, no external previous ideas of humans inserted into it, and let it generate some wisdom on its own and then lets talk. (btw thats how I would expect we could get closer to AGI with these statistical models, but thats just my opinion)
Start with clean llm, no external previous ideas of humans inserted into it, and let it generate some wisdom on its own and then lets talk.
An LLM is about as likely to do that as you are. The ability to generate "wisdom" ab initio cannot possibly be a criterion for intelligent reasoning. The ability to arrive at novel mathematics proofs, on the other hand, is good enough for me.
Intelligence means making the most of the resources and information available, not the ability to speedrun the Big Bang. LLMs are certainly smarter than humans who dismiss them as "text searchers."
It did. In the form of a pattern that humans are too incapable of recongizing. LLMs identify and repeat the pattern. That is all.
For example, if A -> B and B -> C, logic dictates A -> C. But LLMs will be able to state that A -> C, without actually using logic, if there is sufficient statements in its training data that says A -> B and B -> C and A -> C. So now if you say P -> Q and Q -> R, it will say that P -> R, when there is no explict P -> R in the training data and NOT using logic. For you, it looks like a new discovery inferred using logic when it is not. But that is how that happens..
It is just pattern recognition masquarading as logic, x, y, or z.
This "super intelligent" and "capable" thing cannot even understand that your ssh keys are private and should not be sent to randos. It can solve complex math, but does not understand basic security/privacy.
Ever heard of social engineering?
Also, models nowadays are way sharper than they were even a year ago. They’re not going to make stupid mistakes like that unless you basically ask them to. GPT-5.x for example would bend over backwards to avoid even reading your passwords into context.
Oh wait, I thought these things were super smart. I didn't expect "social engineering" to work on them.
> models nowadays are way sharper than they were even a year ago.
You are missing the point. If the thing can solve complex math problems and at the same time be so dumb as to fall for "social engineering", then that means that it is not "smartness" or "reasoning" that is helping it to solve those problems. Just some form of advanced, but yet dumb, search algorithm.
By "heard of social engineering?" I meant that humans are vulnerable to malicious input too. Prompt injection is basically a simplified form of social engineering for language models. It looks different because models operate over much smaller and more explicit contexts than humans do and are explicitly trained to follow instructions, but the general idea is similar: malicious input tries to manipulate how the system interprets trust and instructions. This is why we need protocols, permissions, and opsec for both agents and humans. That said, I’m not criticizing how you choose to use, or not use, these models, though.
>I meant that humans are vulnerable to malicious input too.
No they are not. Social engineering won't work on a human security expert who knows and understands the implications of the information they are giving away. Your analogy is pointless.
> Social engineering won't work on a human security expert who knows and understands the implications of the information they are giving away
Social engineering, like prompt injection, is a context attack — easy to spot if you're ready for it, but harder in different circumstances (rushed, panicked, tired, having a bad day, etc.).
Troy Hunt (security consultant, creator of HaveIBeenPwned) and Cory Doctorow have both been successfully phished [0][1]. They're both tech- and security-savvy people who "should have known better" but it happened to them anyway. But maybe you're different... you'd never fall for an online scam, right? [2]
To be very clear — I was specifically responding to "social engineering won't work on a human security expert". It can, and has. People are not infallible, and a "that would never happen to me" mindset (1) gets people phished when they think they're too smart for it and (2) is a pet peeve of mine and so sometimes I can't resist pointing that out.
Largely agree with you otherwise, not sure why you read my comment as mental gymnastics to justify LLMs. I don't think that they have an internal emotional state that can feel rushed, panicked, so on. They do — superficially — "respond" to such cues in language, which is why they can be "threatened" [0] and "flattered" [1]. But without an internal theory of mind, LLMs do this sycophantically without any internal model of the world (hence the quotes above, to avoid fully anthropomorphizing their behavior).
The only parallel I'm drawing is that both humans and LLMs can be coerced into unintended behavior via language ("social engineering" and "prompt jailbreaking" respectively), and those attacks are more effective if an attacker is allowed to control more "context", even if the underlying mechanism of why those attacks work is completely different.
Sure they are, if the human expert follows instructions from a manager or a client, if they are of utility to anybody, then they are vulnerable to social engineering and malicious input. An attack may be easy or hard depending on the expert's training, but nobody is flawless.
> If the thing can solve complex math problems and at the same time be so dumb as to fall for "social engineering", then that means that it is not "smartness" or "reasoning" that is helping it to solve those problems. Just some form of advanced, but yet dumb, search algorithm.
I'm not just trying to be snarky, but I have no idea how to read this without taking the implication that humans are advanced, yet dumb, search algorithms.
A human being who states X (implying they know it to be true) will behave in a way that is consistent with X being true.
An LLM will happily say X and behaves in contradiction to X. Because it does not reason. Its behavior is not derived from things that it claim (or appears) to know.
Reading comments like this is like watching an impaired pedestrian about to be run over by an approaching bus. You yell, you wave your arms, but they aren't paying attention. There's no way to warn them, so all you can do is... watch.
> There's no way to warn them, so all you can do is... watch
…and then wake up from the nightmare wishing the stress from the job is lower.
I can only laugh that some people truly believe that developers, one of the most ardent group at automating the tedious part of their job, would refuse to use an effective tool. You only need to look at the open source world to see people litterally scratching their own hitch everywhere.
What if I don't want to automate away the part of my job that I actually like doing? What if, in my job as a programmer, I actually want to do programming?
That’s fair. I do think, however, that the software industry may become a bit like the clothing industry: there will still be an artisanal market for people who want human-made software, but to be honest I wouldn’t expect it to remain the mainstream option.
Sure, and I don’t disagree, but goods and services still need to scale to billions of people. Most people aren’t going to start knitting their own clothes, or have the time to, just like most companies probably won’t rely on fully hand-written software if cheaper automated alternatives are good enough. What you want or enjoy is one thing; the reality of society is another.
People who demand programmers start using LLMs in their work don't understand that it is essentially like asking programmers to start doing accounting or HR. Something fundamentally different from what they love to do..
Actually they are asking programmers to become managers in addition to programmers. Because when the LLMs stops working, they are expected to take over.
So I think programmers who are asked to use LLMs should demand their job descriptions to be changed to that of managers, and should ever deny responsibility if the LLM stops to make progress. Thus let the organization be responsible to actually find a version of AI that works, just like how it was responsible to find competent programmers to work under the managers before.
Reading comments like this is like watching someone who is absolutely convinced that they have a crystal ball in their lap when at best they have a foggy piece of plastic. You could be right you could be wrong, but don’t act like you have such certainty.
The foggy piece of plastic writes better code and better text than I do. I don't know about you, but that makes me sit up and stop waving my hands dismissively.
I really don't want to sound like an asshole, but I refuse the notion that an agent writes better code / prose than I do and I am concerned for anyone who does think that.
Is it _faster_? sure! That is NOT better.
Before you ask, I write code w/ agents daily, I find it useful, but it's not better than I am purely on quality.
What I've been seeing lately with Opus 4.7 under Claude Code is that it finds more bugs in my code than I find in its code. That, to me, makes it hard to argue that I am "better" than it is.
Certainly Claude/Codex's knowledge of algorithms and data structures is leagues ahead of any human programmer alive. Only its capacity for creating new ones on demand is weaker. Recent results from the mathematics field suggest that's a temporary state of affairs.
As far as prose goes, my best writing is indeed better than the best I've seen from LLMs. But that's a matter of opinion (mine.) On average the clanker wins, especially if conditioned to avoid LLM-isms.
The truth is that the models are getting better in both areas, while I'm not. Which IMHO is freaking awesome, not a reason to burn it all down.
I think it's pretty good at review and finding bugs, but IME it's really not great at designing solutions to non-trivial problems, which IME is the part of the job that makes me "good" at it. YMMV
Well in an interview I guess something like "Of course we shouldn't allow C-strings in general outside of syscalls and argv, but for the purpose of the exercise...." And now you've shown that you know what you're talking about and that you won't be difficult to work with.
Oh 100%, I was responding to the parent who's response was 'No'. Even if you have 100% ban on strcpy and z-strings you're forced to use them in certain cases (like argv), and I was pointing out that sometimes we engage with certain conceits in a job interview, and by refusing to engage with it you're giving out a signal that you'll be difficult to work with
You don't spend two years doing a Game only to realise later that is not fun
That does happen on occasion, the commonly-cited example being Half-Life. How awesome would it have been if the Valve team hadn't had to waste so much time, money, and personal energy on their initial failed prototype?
Unfortunately most studios ship their failures, either because they don't realize they built something crappy or because the alternative is bankruptcy. A cynic would say that if AI can reduce the cost of experimentation, it will only result in more bad games, while an optimist would argue that it will result in more good games. I think we'll find that they're both right.
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