Is there a rational explanation on why there seems to be a HN article answering the weirdest questions i had in my mind just a few days ago ?
Only yesterday i wondered how did CPU performed division. I didn't ask or type anything about it. It was just in my mind. And now this.
Are we part of a collective mind ? Do social networks algorithms shape society that deeply that we all end up having about the same random thoughts ?
I definitely noticed this trend of article chaining, bu it must have been something else in this case, because i have absolutely 0 memory of seing that post yesterday. Actually, i think my thought came from an instagram video in my feed of a guy showing human division algorithm using sticks on a whiteboard.
This is probably just availability bias / frequency illusion at work. Thinking about something once makes the brain suddenly notice it everywhere. The coincidences stick, but all the misses don’t. Feels like magic, but it’s just how attention and memory play tricks.
I may have the answer. This website nand2mario and something like this article was here on HN a while ago. Maybe a month or two months ago. So maybe then you were busy and didn't read it fully but your mind caught it somehow from HN or coworkers/friends and now when you are relaxed you can register the stuff nicely. Typing 80386 in the search doesn't gets me that exact post.
how do apis typically manage to actually « use » the « bar » of your example, such as storing it somewhere, without enforcing some kind of constraints ?
Depending on exactly what you mean, this isn't correct. This syntax is the same as <T: BarTrait>, and you can store that T in any other generic struct that's parametrized by BarTrait, for example.
> you can store that T in any other generic struct that's parametrized by BarTrait, for example
Not really. You can store it on any struct that specializes to the same type of the value you received. If you get a pre-built struct from somewhere and try to store it there, your code won't compile.
I'm addressing the intent of the original question.
No one would ask this question in the case where the struct is generic over a type parameter bounded by the trait, since such a design can only store a homogeneous collection of values of a single concrete type implementing the trait; the question doesn't even make sense in that situation.
The question only arises for a struct that must store a heterogeneous collection of values with different concrete types implementing the trait, in which case a trait object (dyn Trait) is required.
An ecosystem is being built around AI : Best prompting practices, mcps, skills, IDE integration, how to build a feedback loop so that LLM can test its output alone, plug to the outside world with browser extensions, etc...
For now i think people can still catch up quickly, but at the end of 2026 it's probably going to be a different story.
> Best prompting practices, mcps, skills, IDE integration, how to build a feedback loop so that LLM can test its output alone, plug to the outside world with browser extensions, etc...
Ah yes, an ecosystem that is fundamentally inherently built on probabilisitic quick sand and even with the "best prompting practices", you still get agents violating the basics of security and committing API keys when they were told not to. [0]
For example, what if your code (that the LLM hasn't reviewed yet) has a dumb feature in where it dumps environment variables to log output, and the LLM runs "./server --log debug-issue-144.log" and commits that log file as part of a larger piece of work you ask it to perform.
If you don't want a bad thing to happen, adding a deterministic check that prevents the bad thing to happen is a better strategy than prompting models or hoping that they'll get "smarter" in the future.
Part of why these things feel "not fit for purpose" is that they don't include the things Simon has spent three years learning? (I know someone else who's doing multi-LLM development where he uses job-specialty descriptions for each "team member" that lets them spend context on different aspects of the problem; it's a fascinating exercise to watch, but it feels even more like "if this is how the tools should be used, why don't they just work that way"?)
I have tons of examples of AI not committing secrets. this is one screenshot from twitter? I don’t think it makes your point
CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
> I have tons of examples of AI not committing secrets.
"Trust only me bro".
It takes 10 seconds to see the many examples of API keys + prompts on GitHub to verify that tweet. The issue with AI isn't limited to that tweet which demonstrates its probabilistic nature; Otherwise why do need a sandbox to run the agent in the first place?
Nevermind, we know why: Many [0] such [1] cases [2]
> CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
Except you just made a false equivalence. CPUs can be tested / verified transparently and even if it does go wrong, we know exactly why. Where as you can't explain why the LLM hallucinated or decided to delete your home folder because the way it predicts what it outputs is fundamentally stochastic.
you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t think either proves the point, but posting a screenshot from twitter like it’s proof of some widespread problem is what I was responding to (N=2, 1 vs 1)
my point is more “skill issue” than “trust me this never happens”
my point on CPUs is people who don’t understand LLMs talk like “hallucinations” are a real thing — LLMs are “deciding” to make stuff up rather than just predicting the next token. yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are. can you really explain in detail how everything you use works? I’m guessing I can explain failure modes of agentic systems (and how to avoid them so you don’t look silly on twitter/github) and how neural networks work better than most people can explain the technology they use every day
> you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t think either proves the point, but posting a screenshot from twitter like it’s proof of some widespread problem is what I was responding to (N=2, 1 vs 1)
That doesn't refute the probabilistic nature of LLMs despite best prompting practices. In fact it emphasises it. More like your 1 anecdotal example vs my 20+ examples on GitHub.
My point tells you that not only it indeed does happen, but a previous old issue is now made even worse and more widespread, since we now have vibe-coders without security best practices assuming the agent should know better (when it doesn't).
> my point is more “skill issue” than “trust me this never happens”
So those that have this "skill issue" are also those who are prompting the AI differently then? Either way, this just inadvertently proves my whole point.
> yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are.
The additional problem is can you explain why it went wrong as you scale the technology? CPUs circuit design go through formal verification and if a fault happens, we know exactly why; hence it is deterministic in design which makes them reliable.
LLMs are not and don't have this. Which is why OpenAI had to describe ChatGPT's misaligned behaviour as "sycophancy", but could not explain why it happened other than tweaking the hyper-parameters which got them that result.
So LLMs being fundamentally probabilistic and are hence, more unexplainable being the reason why you have the screenshot of vibe-coders who somehow prompted it wrong and the agent committed the keys.
Maybe that would never have happened to you, but it won't be the last time we see more of this happening on GitHub.
I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point.
yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. you can still practically use the tools to great effect, just like we use everything else that has underlying probabilities
OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
> I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point.
Verses your anecdote being a proof of what? Skill issue for vibe coders? Someone else prompting it wrong?
You do realize you are proving my entire point?
> yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
Again, it exacerbates my point such that it makes the existing issue even worse. Additionally, that wasn't even the only point I made on the subject.
> I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic.
When you scale neural networks to become say, production-grade LLMs, then it does matter. Just like it does matter for CPUs to be reliable when you scale them in production-grade data centers.
But your earlier (fallacious) comparison ignores the reliability differences between them (CPUs vs LLMs.) and determinism is a hard requirement for that; which the latter, LLMs are not.
> OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
For the press, they had to, but no-one knows the real reason, because it is unexplainable; going back to my other point on reliability.
> and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
It is indeed wrong for LLMs because not even the researchers can practically give an explanation why a single neuron (for every neuron in the network) gives different values on every fine-tune or training run. Even if it is "good enough", it can still go wrong at the inference-level for other unexplainable reasons other than it "overfitted".
CPUs on the other hand, have formal verification methods which verify that the CPU conforms to its specification and we can trust that it works as intended and can diagnose the problem accurately without going into atomic-level details.
No one is arguing that it isn't useful. The problem is this:
> I’m saying it doesn’t matter it’s probabilistic, everything is,
Maybe it doesn't matter for you, but it generally does matter.
The risk level of a technology failing is far higher if it is more random and unexplainable than if it is expected, verified and explainable. The former eliminates many serious use-cases.
This is why your CPU, or GPU works.
LLMs are neither deterministic, no formal verification exists and are fundamentally black-boxes.
That is why many vibe-coders reported many "AI deleted their entire home folder" issues even when they told it to move a file / folder to another location.
If it did not matter, why do you need sandboxes for the agents in the first place?
I think we agree then? the tech is useful; you need systems around them (like sandboxes and commit hooks that prevent leaking secrets) to use them effectively (along with learned skills)
very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)
> I think we agree then? the tech is useful; you need systems around them (like sandboxes and commit hooks that prevent leaking secrets) to use them effectively (along with learned skills)
No.
> very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)
It's what happens when it all goes wrong.
You have to explain exactlywhy, a system failed in heavily regulated sectors.
Saying 'everything is probabilistic' as the reason for the cause of an issue, is a non answer if you are a chip designer, air traffic controller, investment banker or medical doctor.
that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point (in the second part of your response; I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine. they can be explained despite your repeated assertions they cannot be. also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf
> that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point
I have repeated myself many times and you decide to continue to ignore the reliability points that inherently impede LLMs in many use-cases which exclude them in areas where predictability in critical systems is required in production.
Vibe coders can use them, but the gulf between useful for prototyping and useful for production is riddled with hard obstacles as such a software like LLMs are fundamentally unpredictable hence the risks are far greater.
> I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine.
So when a neural network scales beyond hundreds of layers and billions of parameters, equivalent to a production-grade LLM, explain exactly how is such a black-box on that scale explainable when it messes up and goes wrong?
> they can be explained despite your repeated assertions they cannot be.
With what methods exactly?
Early on, I said formal verification and testing on CPUs for explaining when they go wrong at scale. It is you that provided absolutely nothing of your own assertions with the equivalent for LLMs other than "they can be explained" without providing any evidence.
> also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf
You did not make any point with that as it was a false equivalence, and I explained why the reliability of a CPU isn't the same as the reliability of a LLM.
Isn't "unambiguous representation" impossible in practice anyway ? Any representation is relative to a formal system.
I can define sqrt(5) in a hard-coded table on a maths program using a few bytes, as well as all the rules for manipulating it in order to end up with correct results.
Well yeah but if we’re being pedantic anyway then “render these bits in UTF-8 in a standard font and ask a human what number it makes them think of” is about as far from an unambiguous numerical representation as you could get.
Of course if you know that you want the square root of five a priori then you can store it in zero bits in the representation where everything represents the square root of five. Bits in memory always represent a choice from some fixed set of possibilities and are meaningless on their own. The only thing that’s unrepresentable is a choice from infinitely many possibilities, for obvious reasons, though of course the bounds of the physical universe will get you much sooner.
Only if you've added a signing certificate the VPN controls to your CA chain. But at that point they don't have to do anything as complicated as you described.
TLS means “there’s a certificate”. Yeah, if a VPN/proxy can forge a certificate that the user’s browser would trust, it’s an issue.
But considering those are browser extensions, I think they can just inspect any traffic they want on the client side (if they can get such broad permissions approved, which is probably not too hard).
as i know really nothing about the subject, could someone explain why parent was downvoted ? is it for the tone, or the content ? Because, i , having viewed the youtubers in question, had the same opinion about string theory.
The word 'falsifiable' comes from Popper's criterion, which is central to scientific methodology. What it means: if theory predicts something, and later observations show that prediction doesn't hold, then the theory is incorrect.
String theory doesn't work this way, whatever was measured will be explained as an afterthought by free parameter tuning.
Do you mean that have been falsified? Of course, no standing theory delivers falsified predictions, when that happens you throw the theory in the garbage.
Do you mean that can be falsified in principle? In that case String Theory has falsifiable predictions, I gave you one. In principle, we can make experiment that would falsify special relativity. In fact, we've made such experiments in the past and those experiments have never seen special relativity being violated. The test of special relativity are the most precise tests existing in science.
I suspect what they mean is that there is no outcome of an experiment such that, prior to the experiment, people computed that string theory says that the experiment should have such a result, but our other theories in best standing would say something else would happen, and then upon doing the experiment, it was found that things happened the way string theory said (as far as measurements can tell).
But there are such experiments. String theory says that the result of such experiment is: Lorentz invariance not violated.
> but our other theories
This is not how scientific research is done. The way you do it is you a theory, the theory makes predictions, you make experiments, and the predictions fail, you reject that theory. The fact that you might have other theories saying other things doens't matter for that theory.
So string theories said "Lorentz invariance not violated", we've made the experiments, and the prediction wasn't wrong, so you don't reject the theory. The logic is not unlike that of p-testing. You don't prove a theory correct is the experiments agree with it. Instead you prove it false if the experiments disagree with it.
There are no such experimental results satisfying the criteria I laid out. You may be right in objecting to the criteria I laid out, but, the fact remains that it does not satisfy these (perhaps misguided) criteria.
In particular, predicting something different from our best other theories in good standing, was one of the criteria I listed.
And, I think it’s pretty clear that the criteria I described, whether good or not, were basically what the other person meant, and should have been what you interpreted them as saying, not as them complaining that it hadn’t been falsified.
Now, when we gain more evidence that Lorentz invariance is not violated, should the probability we assign to string theory being correct, increase? Yes, somewhat. But, the ratio that is the probability it is correct divided by the probability of another theory we have which also predicts Lorentz invariance, does not increase. It does not gain relative favor.
Now, you’ve mentioned a few times, youtubers giving bad arguments against string theory, and people copying those arguments. If you’re talking about Sabine, then yeah, I don’t care for her either.
However, while the “a theory is tested on its own, not in comparison to other theories” approach may be principled, I’m not sure it is really a totally accurate description of how people have evaluated theories historically.
> But there are such experiments. String theory says that the result of such experiment is: Lorentz invariance not violated.
This is not a new prediction... String theory makes no new predictions, I hear. I don't understand why you need to be told this.
To your point, there exist various reformulations of physics theories, like Lagrangian mechanics and Hamiltonian mechanics, which are both reformulations of Newtonian mechanics. But these don't make new predictions. They're just better for calculating or understanding certain things. That's quite different from proposing special relativity for the first time, or thermodynamics for the first time, which do make novel predictions compared to Newton.
It has delivered falsifiable postdictions though. Like, there are some measurable quantities which string theory says must be in a particular (though rather wide) finite range, and indeed the measured value is in that range. The value was measured to much greater precision than that range before it was shown that string theory implies the value being in that range though.
Uh, iirc . I don’t remember what value specifically. Some ratio of masses or something? Idr. And I certainly don’t know the calculation.
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