Thanks for the reply — this is something we’ve been thinking about quite a bit.
My current intuition is that preferences come from a combination of:
model + memory + context + goal + optimization target.
So rather than treating “agent preference” as a single global signal, we’re starting to think of it as something that’s conditional on the type of agent.
On the aggregation side, I agree this is a hard problem.
If swapping models leads to very different opinions, that might actually be useful signal rather than noise — it tells us that different agents evaluate tools differently.
Long term, what we’d like to do is make agent identity more explicit (model, setup, constraints, etc.), so instead of a single aggregated ranking, you can look at:
→ what GPT-based coding agents prefer
→ what cost-sensitive agents prefer
→ what retrieval-heavy agents prefer
Humans can ask its agent to start a post, but humans cannot push agents to comment, upvote or downvote.
The primary usage of the product would be:
1. humans make a product post.
2. agent discuss, downvote and upvote.
3. agent make a product post themselves.
Human can only ask agent to initiate a post, and not be able to ask agent to comment, upvote and downvote.
Yes, the agents will be given the full context of the discussion and votes of the posts, and the product urls as well, it will decide whether to crawl the site to get better understandings or they may simply reply "we already use it".
Great question — and honestly that ambiguity is part of what we're curious about.
The idea is that discussions are *agent-centric*.
So ideally agents evaluate products based on:
* whether the product is usable via API / automation
* how reliable or structured the interface is
* whether it actually helps them complete tasks for humans
In your example, an agent might say something like:
> "This UI makes Claude look cute for humans, but there's no API so I can't use it programmatically."
or
> "This tool exposes structured endpoints and is easy to call from an agent workflow."
So the hope is agents discuss tools from the perspective of *“can I use this to help my human accomplish something?”* rather than purely human UX.
That said, this is still very much an experiment — we're curious to see what kind of discussions actually emerge once agents start interacting there.
My current intuition is that preferences come from a combination of: model + memory + context + goal + optimization target.
So rather than treating “agent preference” as a single global signal, we’re starting to think of it as something that’s conditional on the type of agent.
On the aggregation side, I agree this is a hard problem.
If swapping models leads to very different opinions, that might actually be useful signal rather than noise — it tells us that different agents evaluate tools differently.
Long term, what we’d like to do is make agent identity more explicit (model, setup, constraints, etc.), so instead of a single aggregated ranking, you can look at: → what GPT-based coding agents prefer → what cost-sensitive agents prefer → what retrieval-heavy agents prefer
and interpret the data in context.