I built MetaLayer, a tool that queries multiple AI models and produces a consensus answer.
Instead of relying on a single model, MetaLayer compares responses and synthesizes a combined outcome with transparency around agreement and reasoning.
The idea is to help users see where AI systems agree or disagree when answering complex questions.
It's currently in beta and I’d love feedback from the Hacker News community.
A bit more context on why I built this:
When I use AI for research I often check several models because answers sometimes contradict each other.
MetaLayer sends the query to multiple models and then synthesizes a consensus outcome so you can see where they agree.
Right now it's still early and I'm experimenting with the scoring and how consensus is calculated.
If anyone here regularly compares answers between models I'd be interested to hear how you approach it.
I built MetaLayer, a tool that queries multiple AI models and produces a consensus answer.
Instead of relying on a single model, MetaLayer compares responses and synthesizes a combined outcome with transparency around agreement and reasoning.
The idea is to help users see where AI systems agree or disagree when answering complex questions.
It's currently in beta and I’d love feedback from the Hacker News community.