Comment by proc0 | Hacker News Reader

Comment by proc0 | Hacker News Reader

My experience is the same. In short, agents cannot plan ahead, or plan at a high level. This means they have a blindspot for design. Since they cannot design properly, it limits the kind of projects that are viable to smaller scopes (not sure exactly how small but in my experience, extremely small and simple). Anything that exceeds this abstract threshold has a good chance of being a net negative, with most of the code being unmantainable, unextensible, and unreliable.

Anyone who claims AI is great is not building a large or complex enough app, and when it works for their small project, they extrapolate to all possibilities. So because their example was generated from a prompt, it's incorrectly assumed that any prompt will also work. That doesn't necessarily follow.

The reality is that programming is widely underestimated. The perception is that it's just syntax on a text file, but it's really more like a giant abstract machine with moving parts. If you don't see the giant machine with moving parts, chances are you are not going to build good software. For AI to do this, it would require strong reasoning capabilities, that lets it derive logical structures, along with long term planning and simulation of this abstract machine. I predict that if AI can do this then it will be able to do every single other job, including physical jobs as it would be able to reason within a robotic body in the physical world.

To summarize, people are underestimating programming, using their simple projects to incorrectly extrapolate to any possible prompt, and missing the hard part of programming which involves building abstract machines that work on first principles and mathematical logic.

>Anyone who claims AI is great is not building a large or complex enough app

I can't speak for everyone, but lots of us fully understand that the AI tooling has limitations and realize there's a LOT of work that can be done within those limitations. Also, those limitations are expanding, so it's good to experiment to find out where they are.

Conversely, it seems like a lot of people are saying that AI is worthless because it can't build arbitrarily large apps.

I've recently used the AI tooling to make a docusign-like service and it did a fairly good job of it, requiring about a days worth of my attention. That's not an amazingly complex app, but it's not nothing either. Ditto for a calorie tracking web app. Not the most complex app, but companies are making legit money off them, if you want a tangible measure of "worth".

Right, it has a lot of uses. As a tool it has been transformative on many levels. The question is whether it can actually multiply productivity across the board for any domain and at production level quality. I think that's what people are betting on, and it's not clear to me yet that it can. So far that level looks more like a tradeoff. You can spend time orchestrating agents, gaining some speedup at the cost of quality, or you can use it more like a tool and write things "manually" which is a lot higher quality.

> Anyone who claims AI is great is not building a large or complex enough app

That might be true for agentic coding (caveat below), but AI in the hands of expert users can be very useful - "great" - in building large and complex apps. It's just that it has to be guided and reviewed by the human expert.

As for agentic coding, it may depend on the app. For example, Steve Yegge's "beads" system is over a quarter million lines of allegedly vibe-coded Go code. But developing a CLI like that may be a sweet spot for LLMs, it doesn't have all the messiness of typical business system requirements.

> For example, Steve Yegge's "beads" system is over a quarter million lines of allegedly vibe-coded Go code. But developing a CLI like that may be a sweet spot

Is that really a success? I was just reading an article talking about how sloppy and poorly implemented it is: https://lucumr.pocoo.org/2026/1/18/agent-psychosis/

I guess it depends on what you’re looking to get out of it.

I haven't looked into it deeply, but I've seen people claiming to find it useful, which is one metric of success.

Agentic vibe coding maximalists essentially claim that code quality doesn't matter if you get the desired functionality out of it. Which is not that different from what a lot of "move fast and break things" startups also claim, about code that's written by humans under time, cost, and demand pressure. [Edit: and I've seen some very "sloppy and poorly implemented" code in those contexts, as well as outside software companies, in companies of all sizes. Not all code is artisanally handcrafted by connoisseurs such as us :]

I'm not planning to explore the bleeding edge of this at the moment, but I don't think it can be discounted entirely, and of course it's constantly improving.

I'd say it is a success at being useful, but yeah it does seem like the code itself has been a bit of a mess.

I've used a version that had a bd stats and a bd status that both had almost the same content in slightly different formats. Later versions appear to have made them an alias for the same thing. I've also had a version where the daemon consistently failed to start and there were no symptoms other than every command taking 5 seconds. In general, the optimization with the daemon is a questionable choice. It doesn't really need to be _that_ fast.

And yet, even after all of that it still has managed to be useful and generally fairly reliable.

Anything above a simple app and it becomes a tradeoff that needs to be carefully tuned so that you get the most out of it and it doesn't end up being a waste of time. For many use cases and domain combinations this is a net positive, but it's not yet consistent across everything.

From my experience it's better at some domains than others, and also better at certain kinds of app types. It's not nearly as universal as it's being made out to be.

Reply on news.ycombinator.com