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Yeah it was interesting to me that I identified it immediately. Perhaps been spending too much time w/ Claude It's capable of many different designs but this si
by jbs789 24d ago
Yeah it was interesting to me that I identified it immediately.
Perhaps been spending too much time w/ Claude
It's capable of many different designs but this site tells me Rashid didn't even bother.
- walrus01 24d agoNew proposal for LLM-on-LLM wars: Have an automated system that retrieves every front page URL link on HN and feeds it into a "smart" LLM for analysis of whether or not the site html, css, JS and general template layout match that of a known LLM generation engine. Then give it a "probably vibe coded slop" rank number. If you can't beat 'em, let them fight...
- zrail 24d agoI haven't tried it personally but my understanding from friends is that Pangram is very accurate, at least for now. https://www.pangram.com/ https://www.pangram.com/
- walrus01 24d agoAs a paid service it probably works great. In the spirit of weaponizing vibecoded slop vs vibecoded slop, I'm fairly sure that I could 'teach' locally hosted Qwen 3.8-Flash-Next to identify vibe coded slop, with a thorough enough system prompt and access to like, playwright and headless chromium. Or since a lot of vibe coded static sites are just plain html, cs, js, a first try attempt via wget to mirror the first 500KB of the site and then analyze it.
- _joel 24d agoIronic that site looks like it's AI generated too
- koito17 24d agoIt looks close to the kind of documentation artifacts GPT 5.6 Sol would generate for me. That style is still rare enough that I prefer it over OP's "we installed shadcn + Tailwind and look like every seed-stage startup SaaS from 2024" style.
- runarberg 24d agoPlease, do not use LLMs to do actual statistics. They suck at doing actual statistics (like they suck at everything else). Use proven traditional statistical methods like supervised learning for this. You can use traditional (not large) language models to tokenize the content and then a supervised learning trained on the most popular models to detect if those models generated the content.
- Retr0id 24d agoI could tell from the HN submission title + domain name.