jdw64

Then my brain must be full of scars.

Every workflow is highly personal, so there's no real reason to argue about it. Depending on the work, some tasks become patternized and others don't. Most people overestimate themselves as being near average, and some of them are actually unskilled. There's no point in fighting with them.

Even on HN, people don't comment purely out of 'rationality.'

I think AI generates well patterned code. Essentially, it handles a lot of situations with standardized code. But recognizing those patterns and knowing how to implement them are different things.

In programming, there are people who know how to implement something but can't explain the contract or the model behind it. For those people, AI might not be very useful, or they might be one of the rare few who write code far better than AI. Either way, the fact that an AI beat a top competitive programmer is enough to say it performs better than most people on HN. There's no point in arguing with them.

It's not that they're necessarily wrong. It's that they tend to generalize their own personal workflows.

AI can't do software engineering, but it can code well. Software engineering is the problem of transforming a complex open system into a closed one, taking a PM's requirements and building a single system out of them. AI can't do that. Why? Because AI can't adjust emphasis the way humans do. The design for frequently accessed parts and rarely accessed parts should be different, but AI treats everything uniformly, which makes it harder to modify later.

On the other hand, once a problem is closed and well defined, AI does much better than most people. It's logical and doesn't make the kind of leaps humans do, at least with frontier models. So there's no point in arguing with them at all.

Their experiences are based on their own workflows, so their perceptions naturally differ. And it's hard to tell whether they're saying it's bad because they truly understand it well, or because they don't understand it at all.

I'm not saying they should live in an echo chamber, but there's no reason to argue with them. It's better to trust statistical facts.

>If you can't, then you shouldn't send PRs around.

I think this is exactly why our differences emerge.

I rarely collaborate with colleagues. In contract delivery work, that is simply how things operate. Usually, after the architecture is divided into modules, I take on the role of implementing one entire area from start to finish. Because of this, I actually have almost no experience with direct code level collaboration.

While multiple developers typically share a single code base and constantly exchange PRs, I take full responsibility for the internal implementation within the designed I/O interfaces, which seems to be where our divergence stems from.

When the modules are finally integrated, it only becomes a matter of accountability. In that sense, aside from my own website, I might not actually be doing any sustainable development. To be honest, as you know if you try AI vibe coding, the AI's abstraction and my abstraction are different. Because I am not used to its structure, it is not easy for me to manually fix the code generated by AI. Even if I do fix it, I mostly just tweak the surface level. In that regard, I completely agree that there are valid concerns regarding long term maintenance. However, since meeting strict deadlines and ensuring the required behavior are more important to me than long term maintainability, I tend to be more lenient toward AI generation.

It seems we reached different conclusions because we operate in completely different domains. It is always fascinating to see how perspectives differ depending on the field when having these conversations. Have a nice day.

I feel similarly, but at the same time, I think I am the exact opposite. I actually find formulating hypotheses more fun.

For hobby projects or things I start casually, I usually do not think about errors and such at all. When it is a tool I want to build or need for myself, I really do not care about that part.

In my case, I do not contribute to open source at all. Mostly, I deliver code for factory systems or specific companies, and usually, there are strict enterprise requirements. (To be precise, there is always that mandatory code the lead developer on their end dictates, right?) That kind of code is mostly no fun, but it has to meet their requirements and often clashes with my own style. Having AI write that code for me is a huge relief.

In that sense, I think it is just a difference in personality and preferences. I originally became a programmer because I wanted to make games. I started programming because I found it fascinating to see things drawn and displayed on the screen. Becoming a programmer was all because making Flash games was so much fun... So in that regard, for me, writing code is just 'drawing what I want on the screen', which is why I guess I do not mind if the code is written by AI.

When I contribute to other people's projects, I do not use AI for anything other than English translation, but for my own projects, I have no hesitation.

Is this really just a difference in inclination? It is not that I did not enjoy writing code, but rather that seeing what I want rendered on the screen brings me more joy.

When the concept of 'vibe coding' first came out, I really hated it (since my knowledge was earned over 4 to 5 years of getting scolded by lead developers as a subcontractor and factory software provider). But thinking about it, what I really wanted to do as a developer was just to build the worlds I envisioned, so I decided not to let it bother me too much.

We talk often here on HN, and I really enjoy debating with you. I learn a lot from you.Mr."skydhash", I actually remember you quite often, and I even steal a few keywords from your posts sometimes. Because we have different tendencies, we occasionally clash, but having these conversations is exactly what makes it enjoyable.

Thank you for always replying. Have a great day, and I hope this does not offend you in any way.

Writing code directly takes longer to warm up. Usually, I'd keep tens of thousands of lines in my head. In the past, I spent a lot of time designing error propagation and execution contexts. (Talented people might figure it out right away, but unfortunately I don't have that kind of talent.) So I'd have to think about things like Result<T> and how far to propagate errors—and worry about whether my approach would conflict with the existing codebase.

But these days, AI just generates code following the existing patterns of the codebase. In the past, staring at a blank screen meant going through a checklist of things to design—starting from policies and writing everything down step by step. Now, I just ask AI and it gives me a template—which is great. Then if the AI makes a mistake, I fix it manually.

Of course, I still hand-code sometimes—but only in the areas I enjoy. Most of the time, I use AI coding. Both are fun, and they complement each other in interesting ways. Doing both together is actually enjoyable.

These days, my work has become: generate code for 20 minutes, then spend an hour reading it.

I think it's time for people to have their own homepages. Building and customizing a homepage is more fun than you'd think. What's the main reason the internet feels less fun these days? I think it's because there are fewer people actually communicating. But wouldn't it be more fun if we communicated through homepages? Maybe someday there will be a community—a hub for people who have their own homepages—where only those with homepages can participate

My life has been more aligned with computers, and I prefer it that way. Computers don't lie, they don't pressure you. At least they respond honestly to my input, almost all the time. Everything outside of computers was awful. So I like everything that involves computers. The internet let me read and learn so much, which made computers good for me. There are downsides, like YouTube and some communities, but overall, I enjoy building things with computers and making them work.

On the other hand, the world outside computers was mostly bad—people who deceived me when I was just starting out, fraudulent contracts, and all the physical pain I went through in manual labor. Looking at it from the other side, people with real-world capital tend to have good relationships and good people around them. But for those without that capital, the world inside a computer can actually feel more real than the outside.

I've come to realize that trustworthy colleagues and respectful relationships largely depend on social status. And it feels like I don't really belong there.

Computers don't get angry when I can't do something. They don't ask me to meet some standard, and they don't get frustrated when I fail to meet it. I can learn at my own pace, doing what I want. In the past, I had to reach out to people online and exchange emails during that process, but now even that's been replaced by AI giving me answers.

For me, computers aren't a cheap substitute for human relationships. They're the infrastructure that gave me the autonomy, knowledge, and productive capability I never got from the real world.

A shelter and a prison can look the same from the outside. But I chose to be here, because this world inside the computer has been less painful than what I experienced outside.

When code fails, at least I can investigate why it failed. People, on the other hand, can twist the same words depending on relationships and power, and exclude you without ever explaining why.

That's why I like computers. At least when I turn on my computer, work on something, and see it run, I feel a sense of accomplishment, whether it's AI generated or hand written code.

Honestly, I'm not sure if having my own home or meeting someone compatible would change anything.

What is good data and what is bad data?

Typo3? Are you European? I recall it's a framework mainly used by people in Europe—a DACH-origin framework. I found it quite difficult to work with myself.

Ah, I see. These days, there's a trend of calling everything AI slop, so I agree with you. That's why I've been hesitant to use Bootstrap templates lately. even though they used to be considered pretty reliable

Does it look like Claude made it? It was actually made from a template I used 5 years ago. The world never stops surprising me.

As an indie software maker, I agree. I've been thinking about this a lot too. I'm going to start by polishing my indieweb site first. A site needs to look decent to be trustworthy.

I've exchanged a few messages with a mathematician before. We did a small project together, but then contact was lost.. probably due to circumstances on their side. I miss them.

Other than that, I've contributed a few bug fixes to projects from people I met through various posts. The collaboration periods were about two weeks and a month, maybe? Most of them were good people, but we mostly only talked about technical stuff. Whether it was because of my programming skills or something else, the connections never really lasted long.

I agree with the OP's point. But if you've worked in a corporate environment, isn't that just how it is? Like how programming work and getting promoted to a manager are essentially different careers—they feel like fundamentally different things.

They're different kinds of work, and both are interesting in their own way. But in the freelance market, LLMs have already become the baseline, so I have to use them whether I like it or not. There are both pros and cons.

It's good to be able to read code and understand its structure, but writing code and reading it to transform it into a different structure are different skills. There's definitely some decay in raw coding ability, though. So I use LLMs for professional coding and for tasks that I couldn't do before, while I keep hand-coding smaller things that feel manageable.

Honestly, I think most people who hate LLM coding actually hate being forced to use it under workplace pressure. And when LLM output looks bad, it's often because managers tend to be strict about their subordinates' work but lenient about their own. Once an LLM generates something, people tend to get attached to it and become more forgiving—since it feels like they made it.

It's tough that LLMs have made deadlines tighter. But these days, compared to the old days when I had to go through interviews and conversations to build a proposal, I actually find it more convenient that clients send me proposals written by LLMs. There are pros and cons to everything.

The term 'overhauling' is typically used as a weapon in framing battles. It is usually employed by an emerging faction, or a group dissatisfied with the existing establishment, to frame their opponents as evil. The logic is truly absurd.

They argue that investments have surged while results have diminished. This is a given. Just like a gold mine, gold is easily extracted at first, but the deeper you dig, the more expensive extraction becomes.

The article dismisses things like "spending half their time on paperwork" as mere bureaucracy, but these are necessary procedures to ensure the transparent execution of public funds. Shifting power and capital from universities to private AI companies effectively dismantles this public oversight.

Transitioning to a "new initiative" is simply a move to siphon academic power and prestige toward their own agenda. In reality, the exodus of scientists to the private AI industry leaves immense room for scientific neutrality to be compromised. Of course, it is undeniable that inefficiencies and flawed practices do exist in certain areas of academia. Furthermore, once researchers enter the corporate world, they are bound by NDAs. The trajectory of science then becomes subordinated to the creation of short-term market value, rather than the pursuit of truth or long-term value generation.

History has clearly shown the consequences of eating your seed corn just because you are hungry. I do not understand why they are proceeding this way.

However, I think this is a classic example of the category error the OP is talking about. Making accurate predictions isn't necessarily the same thing as asset management. Once you reach that level of fame, people will 100% target and trade against you, and when someone is that well-known, their market positions inevitably get exposed—how could they possibly handle that?

Everyone has some sort of category error to an extent. But more than that, the part about safety guardrails really resonates with my experience. I inevitably have to go down to the low-level to inspect memory, but because of safety issues, I can barely use Codex as it keeps prompting me for 'trusted access.' AI is actually really good at catching bugs, so I'd love to use it, but it does this every single time...

I'm really curious about this: the foundational paper behind today's LLMs came from Google, and some of the world's best scientists were at Google. So why are they falling so far behind in the AI race?

I agree with your opinion. Even if corporate domiciles are moved to other states, the core R&D personnel and major infrastructure often remain in California. Therefore, the human resource network that Silicon Valley has built over decades cannot be relocated overnight simply by moving the headquarters on paper. The evidence that a wealth tax only causes an exodus on paper without actually destroying the industrial infrastructure has already been proven dozens of times.

Furthermore, even if paying a wealth tax forces stock liquidations that cause stock prices to plummet, unrealized gains do not flow into the real economy anyway. The key is the velocity of money, and from that perspective, unrealized gains are dead capital. Even at the cost of a stock market decline, if capital is forcibly circulated and pumped into wage increases or public infrastructure, the actual income rate and purchasing power of the general public with a high marginal propensity to consume will rise.

Then, thinking about why ultra high net worth individuals refuse to pay taxes, I believe they are idealizing themselves as the heroes of humanity. This means they think it is more ideal to strive for human immortality than to invest in 'inefficient' things like infrastructure through taxes. In other words, they believe they are investing more 'efficiently'. However, such thinking is neither rational nor logical at all. The reason is simple.

The idea that their class can be maintained when the base system collapses is dangerously complacent. From a programming perspective, if the OS crashes, how could the application layer possibly survive?

That is an incredibly short sighted view. By acting this way, the industry is essentially selling its future for short term gains. A systemic reduction in the number of people capable of maintaining systems directly correlates with an inevitable collapse in overall software quality.

The perspectives of market advocates often stop at this short sighted level. In reality, their modeling is incredibly simplistic. I will not elaborate at length on their poor modeling here. The short sighted local optimization that only chases the margin of the next quarter is the exact enemy we must guard against the most. (Of course, given the nature of HN, this specific part of the comment will likely be attacked.)

It is not a matter of companies not owing anything; rather, companies are actively committing self sabotage.

Is it truly right to eat tomorrow's seed corn today?

The essence of the software industry is not the 'generation' of code, but the 'maintenance' of it throughout its life cycle. Why is Linux continually maintained, and why is Windows continually maintained? The core of this industry is fundamentally tied to the entire life cycle of a product.

You might be able to fix code with AI. However, under the current LLM structure, can it actually modify projects spanning hundreds of thousands or millions of lines? It cannot. The skill required to partition those architectures is still entirely left to humans. And the ability to define those boundaries is born strictly from experience.

Labeling certain individuals as useless or inadequate inherently destroys the resilience and buffer of the collective as a whole.

Why do humans embrace and care for the vulnerable when society has the capacity to do so? Why do we strive to preserve diversity? It is because, depending on the context, those very traits might prove to be more advantageous. Every human possesses distinct skills, and their unique temperament and proficiency simply manifest depending on the situation they face.

.com — in RFC 920, it originally meant 'commercial.' But come to think of it, a lot of recent startups seem to be selling things that aren't really commercial at all. So maybe they're actually heading in the right direction.

I'm not sure what skill really means, or what's considered bad anymore. AI coding ability and human coding ability are different, and different organizations need different skills. No skill is inherently bad—it just depends on what the organization needs.

I understand that AI writes better code than people like me in many cases. But an industry should still provide opportunities for those people. And sometimes those people create things that are better than AI in certain areas. The problem is when even those opportunities are taken away.

Wow, the methodology in the paper is so detailed—I'll try following it!

UB stands for undefined behavior—I've written about it in detail on my homepage[1] [1]https://www.makonea.com/en-US/wiki/undefined-behavior

If you don't want to destroy a precious friendship, don't use the 'concrete donkey.'

Ah, I see. I misunderstood.Is the report from an internal source, so it can't be shared? If not, I'd appreciate it if you could send me a link so I can look into it too.

I have a question: why do you think Go is better compared to other languages?

After all, learning a new language takes a lot of time. While basic syntax is common and quick to pick up, mastering a language's specific mental model requires a significant time investment, which is why I've used Go before but never seriously.

My interest was piqued recently when I heard about TypeScript tooling being ported to Go, and I know it is incredibly fast. However, where do the results claiming that AI agents generate superior Go code actually come from? Is it a fair, apples-to-apples comparison?

Since Go is a very small language with only 25 keywords, the way you write code is extremely standardized. Because of this, I would assume it naturally produces a lot of excellent best practices and conventions, but I'm not sure if there are actual, direct code examples proving this

Go doesn't have memory safety issues because of its GC, while Zig has UB problems. Zig might have slightly better performance, but I don't think choosing a language without memory safety is a good idea

Sometimes I wonder what 'product-level' code really means. What actually is a product?

The code that goes into PLC equipmentss and hardware is incredibly simple—but it's still a product. If you look under the hood of a lot of software, the code is a mess. Still, it's a product. And when Claude's code leaked, everyone laughed at the code hygiene—but a lot of people still used it.

What is a product, really?