YHK - yet another hacker news clone

    > Fact is, vibe-coded projects devolve over time into an unmaintainable mess. The reason is simple, yet hard to fix: code maintainability and good architecture don’t have good measurements that we can apply, because it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
    > 
    > For one, AI is not trained on what it means for code to be maintainable. For instance, any reinforcement learning done needs a reward signal that can be measured immediately, not in months or years.

I think at the end of the day, it is not impossible to have AI write "good" or "high quality" code. If anything, once the patterns are established, AI will be more likely to adhere to the patterns and rules than any human team. It requires the most experienced engineers on the team to split their time writing the core patterns and documenting them in references/skills.

But it takes a lot of "taste" and a willingness to slow down a bit with AI (to create necessary artifacts), something teams find hard to do when you can ship so fast now.

My experience has been that there is a camp of very senior engineers that are unwilling to adapt to reality and focus on documentation and writing (effectively producing skills and agent guidance which multiplies their effectiveness); they will cling to their knowledge thinking coding a sacred art.

We're a few years into a new technology that is still improving. This is a point-in-time critique.

We (collectively) were unprepared for a machine that presents itself in human forms. We were the frogs that boiled ourselves. We built a world of images and words on a screen. And then we built a machine that can (increasingly) mirror that world; it does so in a way which most of us are incapable of disambiguating.

It feels like there is indeed a ghost in the machine.

And there is, but that ghost is us. And that ghost is fading surprisingly quickly.

has the author not started to develop instincts with regard to ai usage and pitfalls?

At least I did not find a new thought in that (granted, relatable) rant.

"This is bad and you are bad" requires people to not defend their reality through rationalization, but the point we're at with AI right now is driven by exactly that. So this is at best highly ineffective at reaching the people it claims to want to reach.

That said, the underlying emotion of "you all suck and I hope you lose your jobs you frauds" is relatable and worth screaming from the rooftops of Linkedin dot com for the catharsis alone.

"Code maintainability and good architecture don’t have good measurements that we can apply"

Who has no wisdom? There are dozens of ways to measure code maintainability. Cyclomatic complexity is just one.

Nothing stops you from wiring up something like SonarQube metrics to your agentic coding workflow.

The author seems to be confusing "I didn't write any code" with "I don't care about software design and maintainability". There exist maintainable and thoughtfully designed software systems for which the designer did not write any code and didn't read most of it. It's not the median, but the median software project has always been unmaintainable before agents.

- Use the code that your agents write in anger.

There you go. Do I know when my agents fuck up? Yes, I absolutely do -- because I'm a user of the code I have my agents write, and I ask things like "why is it taking 50 ms to start this program ..." and then I go in and find stupidity, and excise it. I do this over and over again.

Is it faster than writing it out by hand? Maybe! It's definitely a different perspective.

Start behaving like a baby "why, why, why" and then do a bit of reading, and you'll be fine.

A lot of these blog posts seem like they're aimed at software written by B2B companies who don't even use their own software ...

We've had strong coding agents for less than a year. Anyone making such a definitive statement about how vibe-coded projects progress over time is basing it on guesswork, not evidence.

I absolutely agree with the author that humans need to be in the loop reviewing and understand the code they're merging, and generally take a "Hey, build X like Y utilizing Z" approach when using AI to build instead of the "Hey, solve this problem" approach. Our PE overlords actually mandate the latter, but I'm not doing it.

However, a point the author misses is that with AI, major refactors become relatively quick. Hours instead of months/years.

Yes, AI can and probably will land you with major foundational and architectural problems, but your architecture isn't set in stone anymore. Your entire codebase bends like a leaf in the wind.

Counter prediction: using AI attractive even for employees. Do you really think you’d wanna join such a company? No way.

This is the labor theory of value; consumers don't care if the code is hand-made, they want the cheap goods (software) that are the output.

Let's stop overvaluing human work. Sure, I don't want AI to write the entire codebase without I know what the fuck it did.

But AI is on an equal footing with an average dev.

> The proficient developers, the experts, rely on their intuition built with sweat and tears, working long hours trying to debug and fix production issues, swearing to never again be so foolish as to repeat past mistakes. It’s the kind of intuition that can’t really be made into a list of rigid rules, because everything is context-dependent. Experts are incompatible with the same rules and recipes that make beginners more productive. Experts don’t follow the rules, they make the rules.

.... because I have found the same thing - that there's nobody more zealous about some paradigm than those who are recently converted to it and who haven't come to find that everything has its trade-offs. Design is always about evaluating the trade-offs and seeing which ones most suit the given situation.

And, so what? Software as an engineering discipline has long lacked standardization and regulation to be on par with other engineering disciplines, and the fact that code and all its surrounding ecosystems are not "visibile" or "malleable" makes this extremely hard.

You can use terraform and yaml to define infrastructure that literally spins up machines _somewhere_ in the internet. With all its issues, bugs and associated consequences mostly being ignored.

I just don't understand the difficulty in KNOWING what needs to be done: NO LLM usage in university/grad/high schools, NO LLM usage in the first 3 years of your professional career.

Once the basics are solidly grasped, then they can use it at will.

The problem has never been about wisdom, knowledge or LLMs writing good, bad, maintainable or terrible code. It has always been about the skill level of people using it AND on the fact that people start off-loading basic things to these models that they wouldn't before.

If you have the knowledge and "suffered" through experience to learn the fundamentals, than not using LLMs becomes more deterimental than beneficial.

You just CAN NOT skip the trial by fire of learning and absorbing knowledge on your own. That's all.

I'm going to make my own prediction: this isn't going to happen

Just to give a hot take, it's funny to look at his builtwith.com. As a developer you have a static site that depends on Cloudflare, Mailchimp, Postmark, Isso ...

Twenty years ago any self-respecting dev would have run the equivalent of all that themselves on their own metal. In 2026 elite neckbeard practice is write the "never reach mastery, because they no longer make choices, they no longer take responsibility" post, hit "publish" and it's magically deployed around the global internet for you. Like a child.

In the future we will see more and more companies proudly boasting their “NO-AI” policy as a competitive advantage. And they will be right.

Yes, a "NO-CLOUD" policy was already so popular, surely this will happen too.

https://builtwith.com/alexn.org

Just because it's bad for a human doesn't necessarily mean everything will fall apart - unless a human has to maintain it unaided.

dumbest take ever. AI is here and not going away, any company that does so will not survive or will be a niche thing for hippies.

The truth is, "good code" is relative. It is determined by the specific domain and the composition of the team. Is incomprehensible FP (Functional Programming) code good? No, it isn't. A programmer must assess the team's capabilities and adapt accordingly. Good code is ultimately something that morphs based on the shape of the organization. Once defined this way, good code might share certain commonalities (like readability or a shared mental model), but its actual form varies wildly.

So, what is good code? That definition is missing. To be blunt, the Hacker News posts insisting that we must write "good code" are essentially a form of self-hypnosis.

Just look at paradigms. The mechanics of OOP have changed significantly, FP approaches have evolved, and DOD or DDD are fundamentally different from their early days. Whenever paradigms are discussed, someone claims, "That problem was solved in the past, and nowadays we do X," only for someone else to reply, "I don't think that's actually solved," leading to a fragmented breakdown in consensus. Ultimately, which knowledge remains as tacit knowledge is entirely dependent on the organization's capability.

You could argue that AI is terrible at simplifying code. However, I am skeptical that AI coding needs to be identical to human coding. When you actually code with AI, it tends to churn out "God Objects"—a classic anti-pattern—but there are times when that actually yields better performance and faster execution. It is only hard for humans to maintain.

Of course, I am not denying that the rewards of good architecture are delayed, or that there comes a point where maintenance becomes impossible. But as the AI era ushers in an age of overproduction, software could become disposable, strictly personal, highly tailored to small niches, or ultimately, heavily polarized.

Realistically, programming domains fall into two major categories: "ship it and forget it" (one-offs) and continuous services. I agree with the OP's point that AI struggles to understand boundary delineations. But honestly, you can enforce those boundaries by injecting them into the spec. How those boundaries are drawn in the first place, however, is purely a matter of personal experience.

Personally, I define "good code" as code that allows the entity responsible for the software to achieve its purpose with a sufficiently low cost and error rate, factoring in the software's expected lifespan and future changes.

If you ask an AI to generate work based on this standard of what level of code is "adequate," you might get entirely different results. The biggest problem with discussions around AI is not just that ideological identities prevent proper evaluation (as seen in that article), but that the AI itself scales proportionally to its input. It is an incredibly difficult issue to judge because you don't know an individual's workflow or exactly how they are utilizing the tool.

I do think the value of reading code is important. However, much of what we are discussing in the AI era is actually rooted in the path dependency of how to become a good human senior developer.

Instead, the core focus of AI-driven development might shift toward defining broader abstractions: data semantics, invariant external contracts, and migration strategies.

Ultimately, I believe the paradigm shift of our era should lead us to ask: "How do we write code most economically in a system where AI is the primary maintainer?" The OP might think differently, but at least, that is where I stand.

i wouldn't know a single engineer who'd want to work at a place like that

At this point AI is a better developer than most mid-level SMEs I've worked with. I hate this fact, but I don't have a shred of evidence to dispute it anymore. I work on a 20 year old codebase that thousands of developers use and it finds bugs in pre-AI code daily.

Unless you steer and understand what an LLM will produce, you will end up with something that possible ”works” that has no future plans baked in. Suno generated music has a very unpleasant feeling of sounding like competent music with nothing to say.

I’d say that vibecoded software is similar. My speculation is that current breed of LLMs do not have an I, and I really don’t exactly knows what goes on in those vast arrays of numbers. There’s something there perhaps, but no person.

Still even in the short term someone wants to run a company that expects responsibility of its organisation, how are you going to exact that responsibility if no one actually understands how the thing the organisation makes works.

Maybe a simple crud system can be made fast and loose. But a bank settlement? A pacemaker? Deletion of sensitive data?

I know some companies are betting on that the agent can fix what the agent breaks. It may be true, but up until now everytime I try to relax on strict steering of an agent it tends to go badly rather fast.

Again I don’t know, but I think as long as we don’t invent synthetic persons with their own ideas on what they want to do, which btw opens a massive can of worms, the current situation will persist. However clever the current breeds of systems are.

I do want to state that a find the current trajectory fascinating. I use LLMs daily, it expands the number solutions I can explore. But in order to make something I feel is mine. There’s a choice and the buck stops with me.

I think this is a bad take... if you are going that long without noticing, you are (hopefully) delivering end user value all that time. That is the main driver of code. You can normally dig/hack/rearchitect your way out of an ugly code situation. If you've been building value for the last year based on the hacky code, that's a win.