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Add demo trio personas and polish empty-state panel UX.
Jordan Miller +1: CursorIntroduce Elena, Marcus, and Dr. Amira as default catalog participants with a build-vs-buy sample preview, dynamic empty-state guidance, readable bubble text, and sidebar prompt display fixes; reassign Marcus to Mistral after Fireworks DeepSeek v3.2 404s locally. Co-authored-by: Cursor <cursoragent@cursor.com>4944128
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Build credentials during Phase 1 and streamline human participant setup.
NeonClary +1: CursorGenerate per-participant credentials concurrently with initial opinions, rebuild only on model changes, and let humans join from profile text with background credential generation—no question required upfront. Also refresh chat-start controls, rate-limit notices, and header layout. Co-authored-by: Cursor <cursoragent@cursor.com>d4017c8
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Tighten CCAI conversation flow and visual threading
NeonClary +1: CursorThe CCAI demo's first end-to-end runs surfaced several UX and prompt issues. This commit addresses them across the orchestrator, prompt templates, transport, and chat UI. Conversation flow: - Each per-speaker prompt (CRITIQUE, FINALIZATION, CONSENSUS_ALLIED, CONSENSUS_SOLO) now opens with an "Open threads directed at you since your last turn" block populated by `_pending_addressed_for`. The templates instruct participants to address those threads FIRST, then their phase-specific contribution. Dropped CONSENSUS_TARGETED_RESPONSE in favor of this unified path. - Status-assessment follow-ups use a hybrid prompt: orchestrator prefers surfacing unanswered participant-to-participant questions verbatim (attributed to the asker) and only synthesizes its own when none exist. New `asker_participant_id` field in the JSON output. - Consensus phase caps consecutive addressed-to (dyadic) routings at 2 before forcing a round-robin pick, so two participants can't monopolize the fl…a763505 -
Build CCAI Vibe Demo on top of LLMChats3 baseline
NeonClary +1: CursorMulti-participant Collaborative Conversational AI demo: the orchestrator runs a six-phase state machine (Initial Opinions, Critique x2, Status Assessment, Finalization, Consensus, Closure), produces a JSON Credential Summary keyed on credibility-for-question, and routes addressed-to messages through alliance detection. Two failsafes - 60+20 participant messages and 100+50 orchestrator calls - pause the loop until the user clicks Continue. Every LLM response is funneled through a centralized strip_thinking sanitizer before being stored, displayed, or fed back to the summarizer. Backend - New: services/{models,prompts/*,credential,consensus,context_budget, json_calls,extra_personas,orchestrator}.py, utils/sanitize.py, api/personas.py, data/demo_questions.json (10 long-context prompts). - Rewrote api/chat.py with /chat/start (N participants, summarizer + max-participants overrides), /chat/{id}/continue, table-view + csv-table exports. - Rate limit bumped to 30/day per IP; HF org …0bb4dfa -
Import LLMChats3 master as CCAI baseline
NeonClary +1: CursorCo-authored-by: Cursor <cursoragent@cursor.com>c44fab6