Hi HN! AI Product Rank lets you to search for topics and products, and see how OpenAI, Anthropic, and Perplexity rank them. You can also see the citations for each ranking.
We’re interested in seeing how AI decides to recommend products, especially now that they are actively searching the web. Now that we can retrieve citations by API, we can learn a bit more about what sources the various models use.
This is increasingly becoming important - Guillermo Rauch said that ChatGPT now refers ~5% of Vercel signups, which is up 5x over the last six months. [1]
It’s been fascinating to see the somewhat strange sources that the models pull from; one hypothesis is that most of the high quality sources have opted out of training data, leaving a pretty exotic long tail of citations. For example, a search for car brands yielded citations including Lux Mag and a class action filing against Chevy for batteries. [2]
We'd love for you to give it a try and let me know what you think! What other data would you want to see?
[1] https://x.com/rauchg/status/1898122330653835656
[2] https://productrank.ai/topic/car-brands
Comments URL: https://news.ycombinator.com/item?id=43632831
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I'm a software engineer with a solid full-stack background and web development. With all the noise around LLMs and AI, I’m undecided between two paths:
1. Invest time in learning the internals o
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Comments URL: https://news.ycombinator.com/item?id=4407831
Hi HN,
We’re Afnan, Theo and Ruben. We’re all ML engineers or data scientists, and we kept running into the same thing: we’d write useful Python functions, either for ourselves or internal tools
