jdw64

I feel the same way. Want to become a dumb and dumber duo? I believe you could be my friend.

Generally, merely possessing nuclear weapons incurs diplomatic disadvantages. Moreover, due to the sheer maintenance costs of the weapons themselves, they have effectively become symbols of state power for major powers rather than acting as a true 'deterrent,' and authoritarian regimes tend to fixate on such outward displays. In reality, unless a country is one of the existing major powers (US, Russia, UK, France, China), simply possessing nuclear weapons brings massive drawbacks. Looking at the current state of nuclear weapons, they practically serve no function other than as symbols for the survival and legitimacy of dictatorial regimes. Therefore, their practicality remains highly questionable.

AI, on the other hand, immediately drives people into unemployment. While AI will create jobs, I believe the jobs it creates will generally be of lower quality and lower pay than before—even if they are better than outright unemployment.

Looking at AI's current potential, it essentially forces a dependency on those who own massive data center infrastructures. Furthermore, while the principle of LLM chatbots is that output varies based on input, it is actually a dopamine-driven structure that yields instant gratification, much like cheap crack. Even though I constantly use LLM chatbots myself, they are addictive.

From this perspective, I agree that AI is more dangerous than nuclear weapons. Setting aside the actual capabilities of LLMs, I agree that the risks LLMs pose are far greater than those of nuclear weapons.

First, the greatest risk is that the knowledge industry itself is in danger of being subordinated to AI companies. Once this infrastructural dependency takes hold and 'friction' disappears, traditional knowledge providers who relied on that friction will go bankrupt, leading to a monopolistic convergence. This means that while workers at AI companies will earn astronomical sums, the knowledge workers on the periphery will become impoverished. We will see a severe wealth polarization where the 'average' wage might increase, but the overall population becomes poorer.

Second, as the areas where AI cannot perform continue to shrink, the cost of learning the skills required to handle those remaining untouched areas will increase significantly. In other words, the hurdle for knowledge labor will rise. This rising hurdle means that the educational and learning costs required to cross that barrier will become an overwhelming burden, making it even harder for the poor to succeed.

I believe that AI, in many ways, is more dangerous than nuclear weapons.

I think it depends on the workflow. I believe I extract a fairly high information density.

That said, this is ultimately a personalized experience, so I think there will be differences between people. Separately from that, I think it's also partly because I basically like writing densely myself. When I start a project, I always use error handling and templates, so I have a fixed form that comes to about 3,000 lines by default.

LLMs produce a lot of wrong answers, but in fact people make just as many errors. I dislike LLM's grammar, but LLM writing has a higher information density than short pieces, so when it's not narrative writing, I trust LLM information.

I dislike that an LLM can produce tens of thousands of lines in 30 minutes, but if I had done it, it would have taken a month.

I know that using LLMs degrades my skills and also degrades my ability to verify LLMs. But in the freelance market, these days contracts are made on the premise that you use LLMs.

I hate AI slop, but here I am actually trying to make a small indie game using AI.

I hate LLMs, and I also like them. I have complicated feelings about it.

Your figures show that Singapore has high output and a tight labour market. They don't by themselves tell us how the gains are distributed or whether median households face affordability pressure.

Here's my view. There are many points where the current economy doesn't feel all that good to me.

Because of you, I got curious and looked into it, and Singapore's income has risen, but consumer prices and especially housing asset prices have also risen sharply.

And this is only Singapore being strong in macroeconomics and export employment; I don't think this means the global economy is good.

Strictly speaking, I think this is because of our particular situation and the fact that we are knowledge workers. Of course, I'm not saying you're wrong. In your situation, you're right, but I think my view is more general. In other words, I don't think it's a case of the global economic situation being good, nor is it easy to say that China's economic situation is booming. I have Chinese friends, and while I don't know China as well as you do, since you're ethnically Chinese, I do know that the number of people who can leave China for abroad is very small in class terms, and I can also argue that China's statistics do not indicate such a good economic situation.

The AI debate is, in fact, somewhat ideological. The problem is that in the process, whether advocating for it or pushing for progress, there is a failure to face its limitations directly. In other words, it is emotional. Because AI's mere existence is a threat to knowledge workers, much like machines were a threat to blue-collar workers. That is, if the value of intellectual labor drops, it damages the value of the labor force through which workers earn capital in a capitalist system.

I have tried solving a few math problems with AI (they were Erdős problems), but because I know absolutely nothing about that math, I couldn't just take the AI's word for it, so I am actually a bit skeptical. A problem arises where you arrive at the answer without actually understanding it. It's not that AI is bad. The problem is that AI destroys the equilibrium between the knowledge I have and the knowledge I lack. That boundary collapses, making it feel as if I can know everything.

Actually, academia is fundamentally about mental models. It's a kind of internal worldview, and that worldview is shared. When you actually listen to the thoughts of scholars and professors, there are subtly different aspects. That forms the person's worldview... and I get the feeling that sharing it is what constitutes intellectual activity.

However, as you mentioned, unlike academics like yourself, academia and knowledge communities seem disconnected to someone like me (meaning they lack accessibility). Even if I were to make a discovery, it would probably be hard for me to become recognized, and I do think AI could actually play a role in opening up those closed communities. But apart from that, I find it hard to say that this only has positive aspects.

I followed Karpathy's research from start to finish to build a small LLM like nanoGPT on my own, and people say that because of the positive transfer that comes from feeding diverse data through modern LLM multimodal encoders, there will be new discoveries. But in reality, the types of problems AI excels at are generally those that humans have found but overlooked. In my opinion, rather than being a knowledge machine, LLMs (or AI) make me feel that what we call "intellectual activity" is closer to a kind of serialization work. A method of stacking things up one by one in sequence, so to speak? After all, the actual operating principle of an LLM proceeds according to the probability of the token coming in the next sequence.

In other words, I think the serialization method of our knowledge activities is similar to how LLMs operate, but I also think a different kind of thinking might be necessary. I am not that smart, and I have never interacted with scholars... (As you know, I am a subcontract worker. Of course, I have been hired by startups run by professors in my country, but it's not like I modeled that intellectual design myself.)

On the contrary, I feel that the evolutionary approach will slow down after GPT 6 Astra. They can continue to increase the size, but the issue lies in the cost-effectiveness of token costs.

Anyway, I agree with most of what you said in your discussion, but rather than anti-intellectualism, I consider this a direct threat to survival.

Watching the discussion unfold, here is what I feel:

Before arguing whether mathematics must strictly be done by humans, there are different motivations at play. Some people love the sense of solidarity within the community that forms during the process. Those excluded from that community might resent it, while others just purely want to solve problems.

Many things are being discussed, but looking at the overarching narrative, it seems that AI's true function isn't necessarily opening new horizons of specific knowledge, but rather excelling at 'serializing' topics that have been heavily fragmented until now.

In that sense, the concern is that because AI is solving the very problems needed to cultivate mathematicians internally, the stepping stones required for human growth are disappearing.

However, on the other hand, as the world and industries become increasingly complex and hyper-specialized, you could also argue that AI is the exact tool needed to unify this fragmentation across academia and industry. It is a highly complex dilemma.

From the perspective of researchers and the mathematical community, those 'problems for growth' must remain. But conversely, AI has the distinct ability to serialize siloed disciplines. Usually, when you go to graduate school, you often hear professors say that even within the exact same major, they cannot understand each other if their sub-specialties differ.

thanks!

That's fascinating. Does it mean it verifies mathematical proofs directly within the Rust code? Does anyone know the underlying principles of how this is possible?

Programming education in the LLM era will be different from what it is now. Many of the learning methods emerging now are the practices of a "good senior programmer."

But realistically speaking, choosing LLM programming ultimately means pouring out an enormous amount of code, and it's difficult to verify all of it. Common sense says that if you produce 10,000 lines in an hour, you can't read all of it, and even if you do read it, you'd have to rewrite it. The problem is that LLM code differs from human abstraction. Or more precisely, it lacks a programmer's habits, so it's hard for me to maintain.

Clearly, programming in the LLM era will be different. The problem is that I can't get a sense of what that way of doing things actually is.

I think that low-priority frontend work will probably be handled by LLMs, while only complex animation work will be handled by humans, and humans will end up working only on things like payment modules, which are hard to fix if something actually goes wrong.

LLMs are now better at optimization than most people.

It's a K-shaped economy, after all. From the way you talk, you seem to be upper class. Usually Singapore is centered on elite schools and technology for certain families, and just from the fact that you can go into the English-speaking and IT industries, it seems to me—based on this conversation—that you're basically upper class. You're probably connected to Lee Kuan Yew's family. Singapore itself is a standard English-speaking country, and there are many Chinese Singaporeans, but even so, considering the basic amount of education needed to learn IT skills in Asia, you're probably upper class.

Then it makes sense that you wouldn't feel it. In fact, because it's a K-shaped economy, the bosses and businesspeople I meet think the business climate is good. But separately from that, for ordinary people who are the actual consumers, it's hard to make a living.

We're all navigating through hard times.

No. China is a colonial structure of extreme 1-tier cities, 2-tier cities, and the lower cities beneath them. It's called hukou... That's also why ordinary Chinese young people don't want to go down to the provinces.

People in China's first-tier cities live well and are more up to date than almost anywhere, but in the lower cities, they are not free. I usually think that popular literature reflects a country's social conditions, and even in China's xianxia, there are ranks, and people act according to those ranks. To say that all of China has become wealthy would be an overstatement, given that there are many problems starting with the migrant worker issue. Of course, China is a great power, and the wealthy programmers in first-tier cities are extraordinarily talented.

It would be nice if quality became a standard, but the problem is that the world has become too complex. On top of that, I think the disappearance of friction is a big part of it.

In the past, if a garment factory was located in a certain country and the quality was poor, it was often directly tied to that country's reputation. But as the world began to lose friction and entered the era of globalization, much of the work was passed down to subcontractors, and the big problem is that consumer complaints don't get passed down to the subcontractors either.

And within this structure, multiple subcontractors can't survive; as subcontractors grow, they begin to centralize into one. In such a structure, it's not that the original contractor controls the brand quality of the subcontractor. Rather, the subcontractor that has grown huge begins to control the quality ceiling of the entire market.

On top of that, for a company to sustain continuous growth, a given product must not last too long, right? Because a product that can be used for 20 or 30 years means people won't have to buy that product line again.

wow!

Looking at the recent discussions on Hacker News about AI solving difficult problems, it seems there are specific types of mathematical challenges where AI truly excels.

It appears to be relatively good at problems where finding the initial answer is difficult, but verifying whether a candidate answer is correct is easy. In particular, AI feels very strong in matching-type problems, almost like fuzz testing. As seen in Terence Tao's conversations, it has a massive advantage in rapidly substituting and testing various models.

Given these strengths, I feel it would be highly effective for problems like the Hadamard matrix of order 668, the Lonely Runner conjecture, and the Graceful Tree conjecture.

Perhaps the unsolved problems I mentioned will be cracked in the near future? It is fascinating.

Wow, so AI can actually help with difficult problems like this. If that's really true, I mean. Lately I've been feeling that the ability to choose the right problem matters a lot. It's a game where the people who use AI to stake out these problems first have the advantage—so of course the people who were sustained by scientific discussion and community knowledge transfer would feel sad about it, right?

But it's really fascinating.

Thank you. Since most of my work involves small to medium-scale projects, I really appreciate the advice. I will definitely look into it.

Sounds good. I've lost count of how many mazes I've made. Just call me the Architect of the Labyrinth.

I want to work on coding for a service large enough to involve distributed systems someday. I keep studying the theory, but still...

But when I think about it, communities like HN don't exactly have only the taste I want either, do they?

That's always the case, really. There are people whose daily lives fall apart because they read too many books, but that usually isn't categorized as a form of addiction. So sometimes I wonder how addiction is actually defined.

I think I'm similar. But why didn't people talk about newspaper addiction back then? Sometimes I wonder—even before smartphones, people spent that time reading books, looking up information, and reading newspapers. That kind of addiction was seen as "positive." But in reality, it was probably disrupting daily life too, in its own way.

When I see things like this, it sometimes occurs to me that the people who create "addiction" are the ones in power. Come to think of it, it makes me wonder why there was never any such thing as newspaper addiction.

I feel like AI development is hitting a wall now. Evaluations are driven by benchmarks, but I have no idea who is even evaluating the validity of these benchmarks. The idea that continuously training and scaling models will automatically yield broad capabilities across multiple domains hardly seems true anymore.

Benchmarks like Senior SWE-Bench apply discontinuous and arbitrary thresholds to evaluate results. If the generated code is semantically identical to the reference solution but even slightly exceeds a length cutoff, it fails? That seems like a genuinely flawed criterion, and the LLM-based graders themselves feel highly unstable.

Honestly, my impression is that LLM advancement is now completely dictated by benchmarks. But looking at recent trends, token prices are skyrocketing while coding capabilities haven't shown any massive improvements beyond a certain model generation. In fact, comparing GPT-6 Astra to 5.6 SOL, SOL often writes better code.

Considering all this, while training across diverse domains can pack a model with various pieces of knowledge, ultimately, it feels impossible for this approach to do something like derive the theory of relativity from medieval knowledge.

I'm making a language. Its name is Pergyra Lang, and there's nothing particularly special about it.

I think one of the traits of people who feel a sense of loss about AI is that many of them value the bond that comes from discussion within their field. You could call it a sense of comradeship, or a shared sense of purpose.

Using Astra, I get the feeling that LLMs have hit a ceiling. To be exact, it's not necessarily a hard limit, but a point of diminishing returns where squeezing out tiny performance gains yields barely noticeable improvements in practice.

I switched from GPT-5.6 SOL to Astra, and while it is genuinely impressive at generating 3D meshes and similar assets, its coding capabilities actually seem worse than SOL in many cases. Granted, this is just my subjective impression.

There is a massive gap in token pricing between GPT-5.2 and GPT-6 Astra, yet the actual leap in quality feels negligible.

Will we really see a massive performance spike from where we are now? I'm somewhat skeptical, and I wonder what actual AI researchers think about this.

I also code as a hobby. It seems like a pretty good thing to have as a hobby.

>You must be a C#, Swift, Asyncio, or Tokio developer — hard to narrow down, you all agree on this one.

I think that's definitely right. Knowing the semantics of the language you mainly use is important.

The Spark feature's UI isn't as good as the others.

For example, I'm not sure if it should prominently show recent workload, and unless I use MCP to manipulate files on my computer, local file manipulation isn't possible. Personally, I don't think it's going to be that useful.

I asked Astra for fully working code, and it gave me bad code.

But when I broke it down into function units, some parts were bad and some parts were good.

So I can't tell the difference