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perf(orchestrator): parallel phases, streaming, early table summaries
NeonClary +1: Cursor- Parallel participant turns in Phases 1/2/4 via orchestrator_speed.run_roster_ai_turns_parallel - SSE token streaming on consensus + targeted follow-up turns (message_stream_start / message_delta) - Parallel ballot collection in majority / ranked-choice / Robert's Rules votes - Heuristic-first addressed_to classifier skips most LLM calls - Compact rolling transcript for orchestrator judge prompts (cached per session) - Fast orchestrator model (gemini-2.0-flash) for lightweight classifier calls - Lazy contribution summaries kicked off after Phase 4 so Table View loads instantly - Conditional Phase-3 credential refresh only when follow-ups are needed - max_tokens 4x multiplier limited to thinking models Co-authored-by: Cursor <cursoragent@cursor.com>83add3c
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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