A chat-first “vibe” recommender for movies that uses two agents to converge on intent: BEST (Llama) argues for the most on-vibe pick; WORST (Claude) argues for the intentionally off-vibe counterexample. Users can fork either agent into threads—BEST accumulates include-tags, WORST accumulates exclude-tags—so intent sharpens fast. Under the hood, we assemble a candidate set from MongoDB (sample_mflix), pass the same candidates to both models via ch.at (OpenAI-compatible), and return strict-JSON “receipts” (strong/weak details + tags_hit/tags_miss). It’s a general pattern for ambiguous preference search and showcases how evidence selection differs across models on the same corpus.
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