jdw64

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

To be honest, I have no idea what kind of user the Gemini app was built for. It's just too frustrating to use

Wow, it's amazing that an individual built an IDE

I see things differently. This is a change unlike anything in the past. Back then, it didn't replace human intellectual activity. However, problems of increasing scale will continue to arise, and inevitably, issues will emerge that are too difficult to solve through the efforts of human communities alone.

The performance gap between free and paid AI models is massive. The resulting divide between children from low-income and high-income families is quite evident based on my observations in my country. It will only get harder to keep up.

I don't support UBI either. The fact that UBI is even being discussed means the market is broken. The problem is that in the current market, prices for freelance work tied to intellectual labor have been absolutely crushed by AI. Looking at indicators from Booth or the Korean freelance market, "middle-tier" jobs are disappearing. While currently established professionals remain unaffected for now, the volume of work has drastically decreased for developers or writers who are still building their reputations. There are statistics from Japan showing this as well. My workload has also decreased to a ridiculous extent. The so-called barbell structure is becoming entrenched.

The core issue is this: if that happens, how will society handle the dissatisfaction of such a massive number of people? If more than half the population are workers, there is no problem, but if more than half are unemployed, that is a serious issue. Income polarization is continuing to accelerate.

Because there are no aristocrats without slaves, capitalists need workers to elevate themselves. Wealth and power are strictly relative, and they need miserable ornaments to make themselves feel like "awe-inspiring beings." To confirm their own sense of superiority, capitalists require human spectators who look up to them while suffering in misery. Why else did ancient Rome unleash lions and gladiators in the Colosseum?

Later on, things made by humans will become expensive luxuries, while commoners like me will be fed UBI—the bare minimum bread for survival—along with cheap, AIslop.

If making money by learning a skill increasingly requires a longer time investment, you have to endure the period it takes to learn that skill and build a reputation, which is incredibly difficult for the average working class. It requires the financial backing of a wealthy family to afford a college degree, travel to academic conferences, and survive on low wages. And if someone can endure all that to build a reputation, it will naturally lead to a massive concentration of wealth at the very top.

Because, think about it. Let's assume techno-feudalism is complete. Every piece of infrastructure is going to have usage fees attached anyway. Everyone is using AI, so are you going to be the only one not using it? Of course you'll use it. It's a structure where everyone has to rely on a specific corporation's products to get anything done, meaning the wealth will automatically be absorbed back to them anyway. So why would they go out of their way to commit a massacre?

From a cost-benefit perspective, I think it's a terrible idea. In the case of a massacre, long-term instability makes it hard to build a supportive faction. But if you provide just the bare minimum to survive through UBI, the capitalists will gain a loyal faction that will act as their Red Guards to attack dissidents, which I think is much cheaper in the long run. There are already plenty of people who blindly defend corporations even when they do terrible things, right? In my opinion, UBI is simply the cheaper option. Historically speaking, post-massacre recovery has always ended up costing much more.

Is it because UBI is cheaper than a massacre? Since it will all end up being absorbed by the capitalists anyway.

The tenants might change in the future. Right now they are individuals, but perhaps properties will be leased to build power grids or autonomous logistics warehouses. Also, if techno-feudalism accelerates and public purchasing power collapses, leading to the introduction of UBI, I think most of that money will just end up going toward essential housing costs anyway. In fact, if you look at recent trends in real estate, commercial properties are where the big money is being made, aren't they?

>Automation can, therefore, reduce the value of work before it eliminates jobs.

As automation takes over, the value of labor collapses, and workers' market bargaining power under capitalism is the first to crumble. Ultimately, this drives up the value of real assets. I agree that tech companies can profit and gain the upper hand in negotiations simply by demonstrating a machine before licensing it, as this alone diminishes the value of workers. I am seriously worried these days.

It seems you and I now have something in common inside our bodies: PFOS. I've finally found a point of connection between us.

Out of curiosity as to why 3M would insert this commonality into me to help us share a sense of solidarity, I looked up some safety-related research papers. I came across a 3M technical paper by Seacat et al. (2002)[1], which claims that under their experimental conditions, no harmful effects were observed up to a certain dose.

Seeing this perfectly illustrates just how much science and technology are dictated by the institutions they are affiliated with.

[1]https://pubmed.ncbi.nlm.nih.gov/12075127/

Older developers in the Node ecosystem still sometimes write callback hells, but it's true that it's less prevalent than in the .NET world.

The problem is that I am the one who has to maintain that code. The reality is that code ultimately follows the prevailing trends and paradigms of its era. Most developers tend to stop growing around their 5th to 7th year (this is purely my observation). They settle into the complacency of having shipped a single complete program, an experience they usually hit around their 4th year. As a result, when you do actual maintenance work, you end up facing things like C++98 code or .NET codebases where the dependency graph is a complete disaster.

Compounding the issue is that these types of companies have very little technical exchange with the outside world. And that specific way of writing code ends up acting as a sort of gatekeeping mechanism.

Sometimes, I feel like programming ultimately boils down to how well you can read and adapt to these shifts in paradigms. The coding trends of today will likely become the outdated legacy of tomorrow, and someone might eventually tell me, "You write old code." It is always a difficult problem.