> The rest of us just build tons of cool stuff for personal use or for $JOB. Releasing stuff to the public is, in general, a massive amount of extra work for very little benefit. There are loads of FOSS maintainers trapped spending as much time managing their communities as they do their actual projects and many of us just don't have time for that.

I wouldn't worry about this.

There are many examples of people sharing a project they've used LLMs to help write, and the result was not a huge amount of attention & expectation of burden.

Perhaps "I don't share it because I'm worried people will love it too much" even suggests the opposite: you can concretely demonstrate the kinds of things you've been able to build using LLMs.

> This is such a tired response at this point.

Lack of specificity & concrete examples frequently mean all that's left for discussion is emotion for hype and anti-hype, though.

In this thread, the discussion was:

  pro: use LLMs or get left behind

  conserve: okay, I'll start using LLMs when they're good

  pro: no no they won't be that good, it takes effort to get to use them

  conserve: do you have any examples?

  pro: why should we have to share examples?

The response may be tired when asked in this personal way, but in general, it's a fair question. Nobody is forced to share their work. But with all the high praises, we'd expect to see at least some uptick in the software world. But there is no surge in open source projects. No surge in app store entries. And for the bigger companies claiming high GenAI use, they're not iterating faster or building more. They are continually removing features and their software is getting worse, slower, less robust, and less secure.

Software quality has been on a step downwards curve as far as quality and capabilities are concerned, for years before LLM coding had its breakthrough. For all the promises I'd have expected to, three years later, at least notice the downward trajectory easing off. But it hasn't been happening.

All I took from your reply was

> I could if I wanted to, but I just don't feel like it.

What am I missing where I can understand that's not what you meant?

I am not at all an AI sceptic, but probably less impressed by what LLMs are capable of.

Looking at these projects, I have a few questions:

1. These seem to be fairly self-contained and well specified problems, which is the best case scenario for “vibe coding”. Do you have any examples of projects where the solution was somewhat vague and open-ended? If not, how do you think Claude Code or similar would perform?

2. Did you feel excited or energized by having an LLM implement these projects end-to-end? Personally, I find LLMs useful as a closely guided assistant, particularly to interactively explore the space of solutions. I also don’t feel energized at all by having it implement anything non-trivial end to end, outside of writing tests (and even then, not all types of tests!).

3. Do you think others would find these projects useful? In particular, if you vibe coded them, why couldn’t someone else do the same thing? And once these projects are picked up by future model training runs, they’ll probably be even easier to one shot, reducing the value even further.

Let me provide an example of what I mean by (2), at least in the context of hobbyist dev. I could have Claude Code vibe code a Gameboy emulator and it would probably do a fine job given that it’s a well specified problem that is likely well represented in its training data. But the process would neither be exciting nor energizing. I would rather spend hours gradually getting more and more working and experience the fruits of my labor (I did this already btw).

At $DAYJOB, I simply do not have confidence in an LLM doing anything non-trivial end to end. Besides, the complexity remains in defining the requirements and constraints, designing the solution, gaining consensus, and devising a plan for implementation. The goal would be for the LLM to pick up discrete, well defined chunks of work.

Based on those, it seems you are not actually using them to create big codebases from scratch, but rather for problems that would normally take quite a while, not because they are inherently difficult to implement, but because you would normally have to spend considerable time on the finicky implementation details.

I think that's the reason why LLMs work so well for some like you, and generate slop for others, because if you let them alone with projects that require opinionated code and actual decision making they most often don't grasp the users intention well or worse misinterpret it so confidently that you end up with something with all the wrong opinions and decisions compounding path-dependently into the strangest and most useless slop.

> What do I care if you are on board?

Without enough adoption expect some companies you are a client of to increase prices more, or close entirely down the road, due to insufficient cash inflow.

So, you would care, if you want to continue to use these tools and see them evolve, instead of seeing the bubble pop.

All of the linked apps look trivial to me. Also, the first one, the UI has no feedback once you click the answer (plus some questions don't really make sense as they have the answer in them). There is more on the website, so there could be something interesting, but I'm having trouble finding it among all the noise. Not saying simple apps have no value. Even simple throwaway UIs can have value, especially if you develop them quickly.

This is not really cool or impressive at all?

A page that outputs your user agent as an example of 'cool stuff built with AI'?

llmslave3 appears to have deliberately picked the least interesting from my HTML+JavaScript tools collection here. This post describes a bunch of much more interesting ones: https://simonwillison.net/2025/Dec/10/html-tools/