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Companies track us with cookies[1] [1]https://www.makonea.com/en-US/blog/how-cookies-became-the-we...

Thank you for the in-depth comment. We have some points where we disagree, but also points where we're similar, and I think this has been a genuinely enjoyable discussion. I largely agree with your current comment.

I also agree that specialized forums have well-managed aspects. But conversely, getting into a specialized forum usually required a ticket of admission—something like having to attend a certain conference socially.

So the problem is this point. Very high-level forums required an admission ticket, or basically a certain level of qualification, and were hard to get into; you had to get in through an existing member, and if you were physically far from that member, it was hard.

Usually, the forums someone like me could get into were often ones at a somewhat lower level. These days things move to Discord, but it's hard to go into Discord's closed ecosystem and study there.

Actually, as you said, forum activity and human interaction are important. Because in the end, humans have to learn about the world through humans.

The world isn't simply a place to obtain information—things like the patience to endure unpleasant situations and people, the understanding to listen to others' opinions without distorting them, and the courage to admit to others what you don't know are not things that can be cultivated through AI, I think.

The various values that have limits when learned through AI can be learned by mixing with people. In that sense, people have to learn about the world among people.

Because AI basically gives you wrong information to please you and reinforces information only within the scope of what you already know. I think friction with people is necessary to find keywords you genuinely don't know.

My words got sidetracked in the middle. I agree with your comment that AI isn't the only thing that changed the internet this way, and I also agree with your point that forum activity and the like are necessary. It's just that where we put the emphasis and weight is what makes our thinking differ. Thank you for the long comment, and have a good day

I think it varies from person to person. Since I'm kind of from the TikTok generation, I don't have much trouble reading this kind of animation, but some people prefer static sites. That's just how it is.

I also found scroll-triggered animations hard to read at first, but these days a lot of sites use them, so I've gotten used to them.

I liked that this site explained the vulnerability in an easy-to-understand way.

Oh, this game brings back memories

Haha, so how do I become a palace clown? Just kidding. I like and trust AI, but I don't think AI can solve the debt, because AI code creates a different type of debt than code written by humans.

I don't know whether it's nostalgia for the past or whether it really was easier to find things. If you say so, then it could be. It's true that in certain fields it really was easy to find things, and it also differed from country to country. Where I was, there was mainly gatekeeping—a kind of control. In that sense, was the past really better? I don't know. In the past, there were quite a few cases where learning a particular skill required an absurd amount of money (though this may be because I'm from an East Asian country). I think what made people enthusiastic about Udemy was probably that, in my memory, it offered those expensive courses cheaply or gathered the information together.

I don't think forums are bad, but I've questioned whether experiences in such forums were always pleasant. There was a lot of wrong information, and there were also many people who picked fights for no reason simply because they didn't like something. But AI really does just tell you knowledge easily.

Of course, forum activity gave me the opportunity to come across knowledge whose keywords I didn't even know, and that got me studying, but I question whether such forum activity was open to everyone.

I also somewhat agree that the problem of the knowledge base shrinking is real. But is that problem really unsolvable? As far as I know, the results on the degradation problem of synthetic data say that if there is synthetic data, there isn't that much degradation. People, too, tend to idealize the past as they get older. Like treating the inconveniences of that time as nostalgia.

I don't know. Rather, once the current SLOP era passes, certain knowledgeable people may become rare and be treated well then. The future can't be known.

Anyway, this is purely a matter of values "Some people may regret the destruction of knowledge communities, but I benefited from that destruction, so I can say it's good. People always judge by their own experience, don't they? I respect your past experience. In the cases you went through, I think AI produced more bad effects than good.

I've also seen many things disappear because of AI, but it let me try things I wanted to try, and I think that part is good. So, honestly, I don't know. I have both affection and hatred for AI. But the affection seems a little bigger.

I don't think you're wrong, but fundamentally we're in an age of information overload, and this may be the new normal. In other words, the environment where you had to know things deeply and become an expert, like in the past, may be different from an environment where you know many things broadly and combine them.

I basically don't think you're wrong. However, books and reading have their information fixed at the point of publication, whereas depending on where you live or your community, the information you get buried in differs. Because I was in an Asian country, it was actually very hard for me to synchronize my programming skills with the latest technology in the US. The frameworks commonly used in my country are stuck at roughly 15 years ago. The time lag between US technology and my mainstream technology is about 5 years. In my area, it's about 15 years.

Anyway, thinking about your opinion, I think there are both valid points and dark points. I think learning for its own sake is important. I agree that people should have an attitude of learning for its own sake, but I don't think it's mandatory. I think it should vary depending on the environment. Still... I do think having that attitude is a good thing. I like your idealism, senior.

However, that holds for people who are middle class or above and can survive. Because rich kids can picture the future and are given the right to love learning. But if you're poor, that's very hard. And when you grow up in such a poor family, there's an emphasis on it having to lead to practical skills.

And even if someone loves learning for its own sake, I think quite a lot of people love the honor that comes from learning. But that too can be called loving learning itself. It's purely a matter of individual values.

I don't think you're wrong, senior, but I also wonder whether the old ways are right in today's era of excessive information. It's just that because our lives and the environments surrounding us are different, we came to different conclusions. I upvoted because I agree with your thoughts. But there are points where I differ. Have a good day.

In East Asian countries, certification systems are still maintained, but in reality they're practically meaningless. If a certification system exists, people will take an interest in CS and work harder at it, or so you'd think—but in reality, the exam questions end up stuck in the old style. In other words, there's a big gap between the exams and the actual field.

Because a certification has to be fixed at a particular point in time. Korea's certification exams start with the 23 GoF patterns and go on to rote memorization of countless patterns and layered terminology.

The problem is that the way you learn for this certification is the old way, so someone who gets the certification ends up writing legacy code.

But if you always update the questions to the latest, then nobody can pass the certification. So, having seen how the certification system works in East Asia, I don't think it's all that good

To be honest, I think it depends on the environment. In the past, if you didn't come out of a good university, your access to knowledge was limited. But these days, in terms of access to knowledge, I think things have clearly improved, whether or not AI is illegally sharing knowledge.

Looking back now, was it really happier before AI existed? I'm not sure. In the past, when I wanted to find out whether I was right or wrong about some piece of knowledge or information, I'd contact several people and, far from getting an answer, I'd often be told not to bother them. But AI, in that respect, is the best teacher I can think of.

I think the times are continuously getting better. It's just that there's a problem of power concentration among the people who handle it. Still, if AI is a tool of that nature, then I suppose that can happen.

But considering what access to knowledge was like before AI came along, I think having AI is better.

When I was a college student (15 years ago), I first encountered C through A Book on C, and C seems like a language that is simple but can really do a lot. Looking at how old this language is, and how many concepts already used in modern languages were tried during its development history, is surprising. It's surprising that the concepts I thought were modern techniques today were already being tried decades ago—that is, before I was even born.

It's amazing that a method like the Bump Allocator was already tried.

How were the senior programmers able to do all of this? I'm just amazed.

But I'm curious—why did DIS fail this time with C++26? Weren't feature additions in C++ usually plentiful?

But they say this is the first case where a DIS failed during the standardization consensus process. Why is that?

To start with, I don't really know about AI, but I have worked in robotics, so I'll speak from that. There are cases where it's ambiguous to call something an emerging business. In fact, this technology is more than 20 years old, and existing companies have already staked out the market. So, apart from space, the industries you mentioned actually have quite a long history.(To be precise, it wasn't the robotics research field—I built my career specializing in installing robots and getting them running in factories)

What I mean by an emerging industry is one that requires little capital and has few entrenched players—in the end, an industry where new entrants can compete with small initial capital. That's how I'd redefine it.

Because in the IT boom era, there were many technological opportunities. The boss who hired me was one example. But in the case of industrial robot manufacturing, the existing hardware supply chain is, by and large, the one investing.

In the end, what I want to say comes down to this: there are fewer industries open to new entrants.

Because in the IT boom and the construction boom, there were markets that needed less capital than you'd think in the early days. In subcontracting, for instance, you could enter with relatively little capital. But in today's technologically advanced industries, initial capital requirements are far higher. So in the past, even within the same category of industry, there clearly existed sub-markets you could participate in with comparatively little initial capital.

For example, in the old IT industry, my seniors 20 years ago said it took surprisingly little capital. But today's market isn't like that. I think this is a problem that arises as industries become more advanced.

There's a paper I read some time ago arguing that as an industry becomes more advanced, the knowledge and resources required to make a product increase.[1] [1]https://www.nber.org/papers/w27787

If I think about why these differences show up across generations, in the end what matters seems to be whether or not you can be at the forefront of a new field in a new era's industry. In the case of today's younger generation, there simply isn't such a new industry.

But this site looks like GPT made it, doesn't it? I can see GPT's characteristic green button UI and beige color scheme. GPT 6.1 tends to use a green-toned UI.

Wow, the demo is really great. It’s impressive that even these demos run fast enough on my phone.

The end syntax is definitely Ruby-style, but I guess I felt that way just from looking at syntax like var, func, and for x in. It does seem like a mix of the two, but maybe it’s because I’m so used to Python that I noticed the Python-like syntax first. Thanks for pointing that out.

It’s similar to Python syntax.

MSGA lol

In the end, the service was maintained because of subsidies, and the service quality seems to be getting worse. There’s nothing that can be done.

Thank you so much for the advice. I’m a beginner in this area, so your help has been a great help to me. When I did it myself, verification kept being satisfied, but it stayed in a state with no progress; but after working based on your comment, progress seems to have sped up.

Since I’m a freelance programmer, my workflow is far removed from other people’s (there’s no review, and I mostly deliver programs based on whether they run), and I had no place to hear advice from seniors. But rramadass always helps me. Thanks to the advice you gave me, I was able to study formal verification and also study ADTs, and explore a broader world.

Some local regressions still exist, but the big looping around has decreased compared to before. It still seems long-running agents can’t fully replace humans yet. I’m always grateful, and thank you for not getting annoyed and for giving me advice even though I’m always lacking. Have a good day. Thank you so much.

May I save your comment and write it on my homepage later? Your advice seems like it will be a big help. Since I’m mostly doing things by reading manuals and books and running into problems myself, certain problems get solved quickly, but there are sections where I get stuck like this, and I always get a lot of help from you. Thank you always.

Thanks for the advice. This is what I struggle with the most. I’ve learned formal verification other than Lean proofs and used it a few times. It holds up well, but as soon as it becomes a slightly longer-term task, it passes formal verification but turns into work that doesn’t reach the goal G I’m aiming for, and I don’t know how to solve that. I clearly need a separate review agent—have you built a separate harness for the review agent?

Other people say that when they do AI coding, they finish quickly, but for me it’s painful because I keep having to verify it myself.

It seems you've summarized my problem properly. Haha.

What I meant in my comment is that when you use formal verification to verify AI, it passes formal verification, but even if it passes that formal verification, the AI still can’t complete the task. The original article also talks about the importance of specifications in the AI era, but my point is that even if it passes formal verification, the AI still has problems. I’m not sure whether I misunderstood the original.

The problem I often run into is this. It’s not formal verification, but I create a UML diagram to lay out the whole skeleton first. There are usually open-source architecture books, so I take it from there.

Then it should implement the whole UML skeleton, right? But it goes like this: halfway through, at some point, it says, “This algorithm is too slow? Hmm... I need to make it faster,” and tries local optimization, so sometimes I don’t get the result I want—that is, an MVP that at least runs.

I know the overall goal clearly, but there’s a problem where it gets obsessed with a specific subgoal or keeps expanding that subgoal.

I think my English is a bit lacking, so I didn't explain my situation well enough.

The main point is not that everything is solved if you specify all the conjunction conditions and prove that those conjunction conditions are correct. Rather, the key point is that in that process, the agent keeps expanding subgoals.

For example, two proofs, P1 and P2, are carried out, but they proceed in a way that diverges from the actual progress of the work. That is, you can prove things like "P1: the chicken is safe" and "P2: the origin of this sauce is trustworthy," but combining these does not make a chicken dish.

In other words, the proof logic holds, but my actual task doesn't get solved. I'm questioning how to solve this problem. It's frustrating that I'm not a native English speaker at times like this. I'm sorry for causing a misunderstanding.

For example, it goes like this.

I proved P1 and P2. But then it creates P1.1. It goes and finds information from an agency certifying that this chicken is safe, and puts that in. Then it starts creating P2.1 as well. The origin of this sauce is trustworthy, but maybe there’s a better one somewhere?

It goes on like that.

While learning and applying formal verification in the context of using AI agents, what I feel is that sometimes it's hard to express my own thoughts as formal verification. But more than that, I doubt whether formal verification being done correctly actually solves the phenomenon of AI spinning in circles on a specific task.

For example, I want to eat chicken, so I try formally verifying "chicken dish," and every time GPT or Claude starts digging into the origin of the sauce in the chicken dish or the breed of the chicken. Then the chicken dish never gets finished and never makes it to dinner, and instead I gain deep knowledge about the origin of chickens.

Of course, I know that formal verification has liveness — meaning that there must be progress in the work is also part of formal verification — but the AI judges that learning the origin of the chicken counts as progress too. Yet it ends up doing work that it actually shouldn't be doing.

Personally, while organizing these AI anti-patterns, I feel that when you formally verify one condition, verification of another condition gets weakened, and within that phenomenon I sense the AI spinning in circles again. In the end, I end up going through the process of reading and merging everything one by one myself, and I want to know whether this is something only I experience.

I think it's because technologists are easier to manage.

Product engineers, on the other hand, are actually quite ambiguous to evaluate. Many methodologies have been introduced, starting from Barry Boehm, but over time they gradually stop fitting, and there are many points that are hard to assess. It's essentially a matter of organizational management theory. The ideal of organizational management theory is that diversity comes together to create synergy, but conversely, such diversity means that decision-making within the organization slows down.

Technologists, by contrast, are very easy to observe in terms of speed or code quality. In fact, most people choose a career of learning technology and making money rather than founding a startup, and from that perspective, since "career" tends to be emphasized as an employee's career rather than a founder's career, isn't that why technologists are emphasized?

What companies value as a capability is ultimately an evaluation that can be measured. And product engineering, when you think about it, presupposes discussions with decision makers.

'Is it necessary to implement this feature?'

'Do customers actually need this feature?'

'Isn't the business model wrong?'

Most decision makers are people high up in the hierarchy, and friction with them is unavoidable. In other words, it crosses existing role boundaries, which means more frequent conflicts in organizational dynamics.

Whether that is ultimately a gain or a loss for the organization is something nobody can konw

These days, scraping bots and referrer spam seem really bad. Recently, referrer spam disguised as aocr.org was scraping every corner of my homepage, and as a personal website, it’s a bit of a burden.

I’m not sure whether AI companies can keep going with these low costs. My workflow is already completely merged into AI. I don’t want to go back to digging through Stack Overflow and spending an hour writing 100 lines of code like before.

It’s a pity. If he hadn’t been born in North Korea like that, he would have become a truly great programmer. My life is really hard and painful, but sometimes I think I’m better off than they are.