I am yet to see a vibe coded success that isn't a small program that already exists in multiple forms in the training data. Let's see something ground-breaking. If AI coding is so great and is going to take us to 10x or 100x productivity let's see it generate a new, highly efficient compression algorithm or a state-of-art travelling salesman solution.

> Let's see something ground-breaking

Why? People don't ask hammers to do much more than bash in nails into walls.

AI coding tools can be incredibly powerful -- but shouldn't that power be focused on what the tool is actually good at?

There are many, many times that AI coding tools can and should be used to create a "small program that already exists in multiple forms in the training data."

I do things like this very regularly for my small business. It's allowed me to do things that I simply would not have been able to do previously.

People keep asking AI coding tools to be something other than what they currently are. Sure, that would be cool. But they absolutely have increased my productivity 10x for exactly the type of work they're good at assisting with.

You’re right, but at the same time, 99% of software people need has already been done in some form. This gets back to the article on “perfect software” [1] posted last week. This bookshelf is perfect for the guy who wrote it and there isn’t anything exactly like it out there. The common tools on the App Store (goodreads) don’t fit his needs. But he was able to create a piece of “perfect software” that exactly meets his own goals and his own design preferences. And it was very easy to accomplish with LLMs, just by putting together pieces of things that have been done before.

This is still pretty great!

1: https://outofdesk.netlify.app/blog/perfect-software

I was able to lead these two competitions using LLM agents, with no prior rust or c++ knowledge. They both have real world applications.

- https://highload.fun/tasks/15/leaderboard

- https://highload.fun/tasks/24/leaderboard

In both cases my score showed other players that there were better solutions and pushed them to improve their scores as well.

Much of the coding we do is repetitive and exists in the training data, so I think its pretty great if AI can eliminate that toil and liberate the meat to focus on the creative work.

You're just asking for the opposite of what AI does.

90-99% of an engineer's work isn't entirely novel coding that has never existed before, so by succeeding at what "already exists", it can take us to 10x-100x productivity.

The automation of all that work is groundbreaking in and of itself.

I think that, for a while into the future at least, humans will be relegated to generating that groundbreaking work, and the AI will increasingly handle the rest.

I find this type of comment depressing. This is a time for exploration and learning new things. This is a prefect way to do so. It’s a small project that solves the problem. Better time spent vibe coding it then to evaluate existing alternatives.

I have worked on out of sample problems, and AI absolutely struggles, but it dramatically accelerates the research process. Testing ideas is cheap, support tools are quick to write, and the LLM itself is a tremendous research tool itself.

More generally, I do think LLMs grant 10x+ performance for most common work: most of what people do manually is in the training data (which is why there's so much of it in the first place.) 10x+ in those domains can in theory free up more brain space to solve the problems you're talking about.

My advice to you is to tone down the cynicism, and see how it could help you. I'll admit, AI makes me incredibly anxious about my future, but it's still fun to use.

I am yet to see an "AI doesn't impress me" comment that added anything to the discussion. Yes, there's always going to be a state of the art and things that are as of yet beyond the state of the art.

Forget utterly groundbreaking things, I want to hear maintainers of complex, actively developed, and widely used open-source projects (e.g. ffmpeg, curl, openssh, sqlite) start touting a massive uptick in positive contributions, pointing to a concrete influx of high-quality AI-assisted commits. If AI is indeed a 10x force multiplier, shouldn't these projects have seen 10 years' worth of development in the last year?

Don't get me wrong, AI is at least as game-changing for programming as StackOverflow and Google were back in the day. Being able to not only look up but automatically integrate things into your codebase that already exist in some form in the training data is incredibly useful. I use it every day, and it's saved me hours of work for certain specific tasks [0]. For tasks like that, it is indeed a 10x productivity multiplier. But since these tasks only comprise a small fraction of the full software development process, the rest of which cannot be so easily automated, AI is not the overall 10x force multiplier that some claim.

[0] https://news.ycombinator.com/item?id=45511128

Its good that way right? Let me as a human do the interesting thinking for which my brains are meant while you AI do what they chips were built for.

I am happy as is tbh, not even looking for AGI and all. Just that the LLM be close enough to my thinking scale so that it does not feel "why am I talking with this robot".

https://thenewstack.io/how-deepminds-alphatensor-ai-devised-...

Not either of the species of algorithms you've described, but still an advance.

10x productivity can also mean allowing a single staff member to do the boring work of 10 humans.

It will not generate anything novel because it can’t.

… or, let’s see humans who are now 10-100x more productive (due to automation of mundane tasks that are already part of the training data) do the things you’re asking for.

And to add to this, for some reason people really bristle if you say that many LLM’s are just search with extra steps. This feels like an extension of that. It’s just reinventing the wheel over and over again based on a third party’s (admittedly often a solid approximation but still not exact) educated guess of what a wheel may be. It all seems like a rather circuitous way to accomplish things unless your goal isn’t to build a wheel but rather tinker and experiment with the concept of a wheel and learn something in the process. Totally valid, but I’m pretty sure that’s not what open AI et al are pitching lol

trifling.org is an entire Python coding site, offline first (localstorage after first load), with docs, turtle graphics, canvas, and avatar editor, vibe coded from start to finish, with all conversations in the GitHub repo here: https://github.com/zellyn/trifling/tree/main/docs/sessions

This is going to destroy my home network, since I never moved it off the little Lenovo box sitting in my laundry room beside the Eero waypoint, but I’m out of town for three days, so

Granted, the seed of the idea was someone posting about how they wired pyiodide to Ace in 400 lines of JavaScript, so I can’t truly argue it’s non-trivial.

As a light troll to hackernews, only AI-written contributions are accepted

[Edit: the true inception of this project was my kid learning Python at school and trinket.io inexplicably putting Python 3 but not 2 behind the paywall. Alas, Securely will not let him and his classmates actually access it ]

Every two months, I run a very simple experiment to decide whether I should stop shorting NVDA....Think of it as my personal Pelican on a Bike test. :-)

Here is how it works: I take the latest state of the art model, usually one of the two or three currently being hyped....and ask it to create a short document that teaches Java, Python, or Rust, in 30 to 60 min, complete with code examples. Then I ask the same model to review its own produced artifact, for correctness and best practices.

What happens next is remarkably consistent. The model produces a glowing review, confidently declaring the document “production ready”… while the code either does not compile, contains obvious bugs, or relies on outright bad practices.

When I point this out, the model apologizes profusely and generates a “fixed” version which still contains errors. I rinse and repeat until I give up.

This is still true today, including with models like Opus 4.5 and ChatGPT 5.2. So whenever I read comments about these models being historic breakthroughs, I can’t help but imagine they are mostly coming from teams proudly generating technical debt at 100× the usual speed.

Things go even worst, when you ask the model to review a Cloud Architecture....

Microsoft is currently hiring engineers to rewrite their entire codebase in Rust via vibecoding. Something to the tune of a million lines of code per developer per month.

> If AI coding is so great and is going to take us to 10x or 100x productivity

That seems to be a strawman here, no? Sure, there exist people/companies claiming 10x-100x productivity improvements. I agree it's bullshit.

But the article doesn't seem to be claiming anything like this - it's showing the use of vibe-coding for a small personalized side-project, something that's completely valid, sensible, and a perfect use-case for vibe-coding.

This comment is wrong in two ways:

1. Current LLMs do much better than produce "small programs that already exist in multiple forms in the training data". Of course the knowledge they use does need to exist somewhere in training data, but they operate at a higher level of abstraction than simply spitting out programs they've already seen whole cloth. Way higher.

2. Inventing a new compression algorithm is beyond the expectations of all but the the most wild-eyed LLM proponents, today.

> let's see it generate a new, highly efficient compression algorithm or a state-of-art travelling salesman solution.

This is the "promise" that was being sold here and in reality, we yet haven't seen anything innovative or even a sophisticated original groundbreaking discovery from an LLM with most of the claims being faked or unverified.

Most of the 'vibe-coding' uses here are quite frankly performative or used for someone's blog for 'content'.

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