Github stars? That's 100% marketing. Shit that clears a low quality bar can rack up stars like crazy just by being well marketed.
Number of startups? That's 100% marketing. Investors put money into products that have traction, or founders that look impressive, and both of those are mostly marketing.
People actually are vibe coding stuff rather than using SaaS though, that one's for real. Your example is hyperbolic, but the Tailwind scenario is just one example of AI putting pressure on products.
If you don't have a halo already, you need to be blessed or you're just going to suffer. Getting a good mention by someone like Theo or SimonW >> 1000 well written articles.
Another one (I've open sourced, you can check it out here https://github.com/luvchurchill/mani-gpg) A site I use (manifold.markets) announced they are getting rid of DMs due to spam (they've since brought it back) so I made an extension which makes it easy to use pgp & age encryption on the site so we can do pseudo DMs. It injects "Decrypt" buttons next to exncrypted text etc etc. You can see screenshots at https://manifold.markets/post/an-extension-to-assist-with-so...
(Look at the comments for the latest look)
Besides for that, there are a few I'm sure can be scripts
There is not bad publicity. More you spam more you will be noticed. Human attention is limited. So grab as much as you can. And also this helps your product name to get into training data and thus later in LLM outputs.
Even more ideas. When you find an email address. Spam that too. Get your message out multiple times to each address.
It's hard to disambiguate this from people who have a "fanbase." People will upvote stuff from people like simonw sight unseen without reading. I'd like to do a study on HN where you hide the author, to see how upvote patterns change, in order to demonstrate the "halo" benefit.
Falling salaries?
Remember that an average software engineer only spends around 25% of their time coding.
Instead of asking "where are the AI-generated projects" we could ask about the easier problem of "where are the AI-generated ports". Why is it still hard to take an existing fully concrete specification, and an existing test suite, and dump out a working feature-complete port of huge, old, and popular projects? Lots of stuff like this will even be in the training set, so the fact that this isn't easy yet must mean something.
According to claude, wordpress is still 43% of all the websites on the internet and PHP has been despised by many people for many years and many reasons. Why no python or ruby portage? Harder but similar, throw in drupal, mediawiki, and wonder when can we automatically port the linux kernel to rust, etc.
We have a smaller version of that ability already:
- https://simonwillison.net/2025/Dec/15/porting-justhtml/
See also https://www.dbreunig.com/2026/01/08/a-software-library-with-...
I need to write these up properly, but I pulled a similar trick with an existing JavaScript test suite for https://github.com/simonw/micro-javascript and the official WebAssembly test suite for https://github.com/simonw/pwasm
And yet it doesn't feel true yet, otherwise we'd see it. Why do you think that is?
(This capability is also brand new: prior to Claude Opus 4.5 in November I wasn't getting results from coding agents that convinced me they could do this.)
It turns out there are some pretty big problems that works for, like HTML5 parsers and WebAssembly runtimes and reduced-scoped JavaScript language interpreters. You have to be selective though. This won't work for Linux.
I thought it wouldn't work for web browsers either - one of my 2026 predictions was "by 2029 someone will build a new web browser using mostly LLM-code"[1] - but then I saw this thread on Reddit https://www.reddit.com/r/Anthropic/comments/1q4xfm0/over_chr... "Over christmas break I wrote a fully functional browser with Claude Code in Rust" and took a look at the code and it's surprisingly deep: https://github.com/hiwavebrowser/hiwave
[1] https://simonwillison.net/2026/Jan/8/llm-predictions-for-202...
If that's what is shown then why doesn't it work on anything that has a sufficiently large test-suite, presumably scaling linearly in time with size? Why should we be selective, and based on what?