Single-model inference is a single point of failure. I got sick of Chatgpt hallucinating fake citations and having to manually check them in a different tab with Claude.
So I built Council
The Difference: Shared Context Most "multi-bot" UIs are just parallel silos. Council uses a sequential backend stream where every response is injected into the context of the next model.
If GPT cites a fake study, Claude sees it and calls it out.
If Gemini misses a logic gap, Grok roasts it.
Adversarial Logic Instead of "consensus" (which leads to boring, average answers), I'm using model-on-model friction to surface the truth. By forcing GPT-4o, Claude 3.5, Gemini 1.5, and Grok into one adversarial window, you get a "red-teamed" output that’s harder to fake.
What I need: It's an MVP. I'm trying to figure out if "Inter-model Cross-Examination" actually kills hallucinations or just creates more expensive ones.
Give it a spin and try to break the logic. No fluff, just testing the architecture.
Comments URL: https://news.ycombinator.com/item?id=46395140
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