the1024
Joined 9 years ago
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254
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50
Member since
Oct 9
Recent submissions
Comment · 1 month ago
How do you think about enabling fully agentic onboarding without proper human identification? It feels like any rate limiting would be pretty easy to get around.
Comment · 1 year ago
> How does it work?
We don't know! We built this to learn a little bit more. We've seen that LLMs tend to prefer user-generated content (sites such as wikipedia, reddit, etc.) and strangely even youtube.
> How do marketers rank higher? Will LLMs prioritize other LLM content? At least so far, LLMs and search engines tend to downrank LLM created content. I could see this becoming indistinguishable in the future and/or LLMs surpassing humans in terms of effectively generating what reads as "original content"
> Who will pull the strings? At this point, it seems like whoever owns the models. Maybe we'll see ads in AI search soon.
https://www.tryprofound.com/_next/static/media/honeymoon-des...
Comment · 1 year ago
We absolutely do lose information here; that's a great point. The goal for us wasn't necessarily to surface the best ranking; it was to learn how LLMs produce a given ranking and what sources it pulls in.
The nugget of real interest here (personally speaking) is in those citations: what is the new meta for products getting ranked/referred by LLMs?
Comment · 1 year ago
Interesting call out - we're working on adding Gemini soon!
Comment · 1 year ago
That nuance is really important/hard to piece apart. Have you found any good techniques to solve for it?
Comment · 1 year ago
OP here - looking at what the models pick up as sources for "Trustworthy News Sources" is especially interesting. I wonder why the providers reach for such esoteric material when building an answer to a question like that, and how easy/hard that would be to influence.
Comment · 1 year ago
That's a great point - we built this moreso to learn a bit about how the AI models interpret ranking products, and less so to actually be a trusted source of recommendations. Seeing the citations come through has been really fascinating.
The use case for that is to better understand where the gaps are when looking to capture this new source of inbound, given people are using AI to replace search.
There's definitely a whole bunch of features missing that we'd need to make this a genuinely useful product recommendation engine! Price constraints, better de-duping, linking out to sources to show availability, etc.
Comment · 1 year ago
"kay-len" - confusing I know
Comment · 1 year ago
Thanks! You can think of Grimp as a lower-level tool for interacting with the import graph in Python, while Tach is a high-level tool responsible for 'modularity' as a whole (e.g. modules, interfaces, layers, deprecations etc.)
Tach is also more opinionated - so it doesn't require you to write any custom code, and uses declarative config to enforce your desired architecture.
Comment · 1 year ago
Thanks for this feedback! Here's a more structured walkthrough: https://www.loom.com/share/7c03d72ea2b54212a3509d4333f61b99?...
We'll add this to the README.